Mathematical Modeling Seminar
March 28 - Dr. Rustum Choksi, McGill University
Time: 2:00PM - 2:50PM Eastern
Location: 2300 Gosnell Hall / Zoom
Dr. Rustum Choksi
Professor, Department of Mathematics and Statistics
Title: Voronoi Tessellations: From Optimal Quantization to Modelling Collective Behaviour
Abstract: Given a set of N distinct points (generators) in a domain (a bounded subset of Euclidean space or a compact Riemannian manifold), a Voronoi tessellation is a partition of the domain into N regions (Voronoi cells) with the following property: all points in the interior of the i-th Voronoi cell are closer to the i-th generating point than to any other generator. Voronoi tessellations give rise to a wealth of analytic, geometric, and computational questions. They are also very useful in mathematical and computational modelling.
This talk will consist of three parts. We begin by introducing the basic definitions and geometry of Voronoi tessellations, centroidal Voronoi tessellations (CVTs), and the notion of optimal quantization. We will then address simple, yet rich, questions on optimal quantization on the 2D and 3D torus, and on the 2-sphere. We will address the geometric nature of the global minimizer (the optimal CVT), presenting a few conjectures and a short discussion on rigorous asymptotic results and their proofs.
We will then shift gears to address the use Voronoi tessellations in modelling collective behaviours. Collective behaviour in biological systems, in particular the contrast and connection between individual and collective behaviour, has fascinated researchers for decades. A well-studied paradigm entails the tendency of groups of individual agents to form flocks, swarms, herds, schools, etc. We will first review some well-known and widely used models for collective behaviour. We will then present a new dynamical model for generic crowds in which individual agents are aware of their local Voronoi environment -- i.e., neighbouring agents and domain boundary features --and may seek static target locations. Our model incorporates features common to many other ``active matter'' models like collision avoidance, alignment among agents, and homing toward targets. However, it is novel in key respects: the model combines topological and metrical features in a natural manner based upon the local environment of the agent's Voronoi diagram. With only two parameters, it captures a wide range of collective behaviours. The results of many simulations will be shown.
This talk will be aimed at an undergraduate level. All definitions and concepts will be introduced with prerequisites kept to a minimum (basically multivariable calculus).
Bio: Rustum Choksi received the PhD degree in mathematics from Brown University, in 1994. He held post-doctoral positions with the Center for Nonlinear Analysis, Carnegie Mellon University and the Courant Institute, New York University. From 1997 to 2010, he was a faculty member with the Department of Mathematics, Simon Fraser University. In 2010, he joined McGill University where he is currently a full professor with the Department of Mathematics and Statistics. His main research interests include the interaction of the calculus of variations and partial differential equations with pattern formation. His undergraduate book -- Partial Differential Equations: A First Course -- was recently published by the American Mathematical Society.
Future Seminars - Spring 2023
Dr. Nicholas Battista
Department of Mathematics and Statistics
The College of New Jersey
Time: 2:00PM - 2:50PM Eastern
Location: 2300 Gosnell Hall / Zoom
Time: 2:00PM - 2:50PM Eastern
Location: 2300 Gosnell Hall / Zoom
Dr. Daniel Anderson
Professor, Department of Mathematical Sciences
George Mason University
*Dr. Anderson will be delivering this talk in person!
Time: 2:00PM - 2:50PM Eastern
Location: 2300 Gosnell Hall / Zoom
Past Seminars - Spring 2023
Dr. Emmanuel Asante-Asamani
Assistant Professor of Mathematics
Title: Mathematical modeling of myosin dynamics in the cell cortex during confined bleb-based migration
Abstract: The actin cortex is very dynamic during cell migration, permitting the rapid formation of leading-edge protrusions that facilitate the forward motion of cells. When cells move in confined environments, they can use excess intracellular pressure to detach and expand small regions of their membrane, completely disassemble the cortex beneath the protruded membrane (actin scar) and form a new cortex at the expanded membrane. This mode of migration, known as blebbing, has been observed in metastatic cancer cells moving in highly confined tumor environments, and may be used to evade cancer drugs that target non-blebbing modes of motility. The mechanism by which cells completely disassemble their cortex during blebbing is not fully understood and could shed light on the dynamic regulation of the actin cortex during cell migration. Recent experimental data in Dictyostelium discoideum cells reveals a local accumulation of myosin II, an essential motor protein capable on inducing contractile stress, in the actin scar prior to any significant disassembly of the cortex. Thus, our data suggests a role for the motor protein in cortex disassembly. In this talk, I will discuss our efforts to use mathematical models to explain the accumulation of myosin in the cortex during blebbing. In particular, I will present a partial differential equation coupled with a system of ODEs that model the intracellular signaling pathway regulating the binding of myosin to the cortex. Numerical simulations of the model suggest that the increase in myosin’s binding rate can be induced by the separation of the cell membrane from the cortex which occurs at the onset of blebbing. Together, our experiments and theory elucidate the regulation of cortex disassembly and more broadly shed light on how cells can use mechanical events, such as membrane separation, to regulate internal cell process during confined migration.
Bio: Dr. Asante-Asamani is a Ghanaian by birth and an Assistant Professor of Mathematics at Clarkson University in New York. He received his PhD in Mathematics at the University of Wisconsin-Milwaukee in 2016 under the supervision of Bruce Wade. His Doctoral work was focused on the development of an exponential time differencing scheme for solving advection-diffusion-reaction equations. Before joining Clarkson in 2020, Dr. Asante-Asamani was a postdoctoral researcher at the Hunter College campus of the City University of New York where he developed mathematical models to understand how eukaryotic cells migrate under confinement using pressure-driven membrane protrusions. Since joining Clarkson, his research has been focused on the development, analysis, and numerical simulation of deterministic models for cell migration and cell signaling as well as the application of machine learning to develop predictive models for cancer. His research has been published in notable journals such as Journal of Computational Physics, PLOS One, Biophysical Journal and Frontiers in Oncology. In 2019 he was one of 70 mathematicians in the nation to receive the AMS Simons travel grant. He is married with 2 adorable kids and loves to write and direct plays.
Dr. Zois Boukouvalas
Department of Mathematics and Statistics
Title: Efficient and Explainable Data Fusion for Misinformation Detection During High Impact Events
Abstract: With the evolution of social media, cyberspace has become the de-facto medium for users to communicate during high-impact events such as natural disasters, terrorist attacks, and periods of political unrest. However, during such high-impact events, misinformation on social media can rapidly spread, affecting decision-making and creating social unrest. Identifying the spread of misinformation during high-impact events is a significant data challenge, given the variety of data associated with social media posts. Recent machine learning advances have shown promise for detecting misinformation, however, there are still key limitations that makes this a significant challenge. These limitations include the effective and efficient modeling of the underlying non-linear associations of multi-modal data as well as the explainability of a system geared at the detection of misinformation. In this talk we present a novel multivariate data fusion framework based on pre-trained deep learning features and a well-structured and parameter-free joint blind source separation method named independent vector analysis, that can reliably respond to this set of limitations. We present the mathematical formulation of the new data fusion algorithm, demonstrate its effectiveness, and present multiple explainability case studies using a popular multi-modal dataset that consists of tweets during several high-impact events.
Bio: Zois Boukouvalas' research focuses on the development of interpretable machine learning models and algorithms for the analysis of multi-modal data, by combining aspects from information geometry, mathematical statistics, and numerical optimization. His work involves different types of data, including biomedical images for studying psychiatric illnesses, social and linguistics data for understanding political and social trends, and chemical data for drug discovery and materials design. He is communicating his research findings through peer-review publications, invited talks, and podcast interviews, and he has been the lead principal investigator of several research grants. Through his research and teaching activities, his goal is to create effective environments that encourage and support the success of underrepresented students for entry into machine learning careers.
Dr. Karamatou Yacoubou Djima
Assistant Professor of Mathematics
Title: Extracting Autism's Biomarkers in Placenta Using Multiscale Methods
Abstract: The placenta is the essential organ of maternal-fetal interactions, where nutrient, oxygen, and waste exchange occur. In recent studies, differences in the morphology of the placental chorionic surface vascular network (PCSVN) have been associated with developmental disorders such as autism. This suggests that the PCSVN could potentially serve as a biomarker for the early diagnosis and treatment of autism. Studying PCSVN features in large cohorts requires a reliable and automated mechanism to extract vascular networks. In this talk, we present a method for PCSVN extraction. Our algorithm builds upon a directional multiscale mathematical framework based on a combination of shearlets and Laplacian eigenmaps and can isolate vessels with high success in high-contrast images such as those produced in CT scans.
Bio: Dr. Karamatou Yacoubou Djima is an applied mathematician and an Assistant Professor of Mathematics at Wellesley College. Before moving to Wellesley, she spent a year at Swarthmore College as a visiting postdoctoral fellow and was an Assistant Professor at Amherst College. She received her Ph.D. and MSc in Applied Mathematics & Statistics and Scientific Computing from the University of Maryland in College Park. Dr. Yacoubou Djima's current research interests lie at the intersection of applied harmonic analysis and machine learning. Her past and ongoing projects include novel spectral graph methods and early diagnosis of autism spectrum disorder using features present in placenta images.
Dr. Ami Radunskaya
Lingurn H. Burkhead Professor of Mathematics
Title: Between Yes and No: Making Decisions Under Uncertainty
Abstract: Often we attempt to answer a question with a “yes” or a “no” by developing predictive models (“Will the small remaining population of axolotls survive outside of their native wetlands?”) or by implementing binary classifiers (“Is this a cat or a dog?”). However, the answers that are provided by our models are often given in terms of probabilities. Even more confusing, different models - equally good according to accuracy metrics - can produce conflicting answers.
In this talk I will explore these issues and discuss their implications. How do we interpret an answer that is neither “yes” nor “no”? For example, a PCR test for COVID yields a probability. How does the choice of threshold affect the individual? How does it affect policy decisions, or the course of the disease? How can we disentangle the predictions given by competing models; i.e., how can we deal with predictive multiplicity? For example, if two models disagree on whether or not someone is a loan risk, which one should be trusted? Which groups are most affected? What new metrics can be used to compare models?
Bio: A California native, Professor Ami Radunskaya received her Ph.D. in Mathematics from Stanford University. She has been a faculty member in the Math Department at Pomona College since 1994. In her research, she specializes in ergodic theory, dynamical systems, and applications to various "real-world" problems. Some current research projects involve mathematical models of cancer immunotherapy, developing strategies for targeted drug delivery to the brain, and studying stochastic perturbations of dynamical systems. Professor Radunskaya believes strongly in the power of collaboration and that everyone can learn to enjoy mathematics; as President of the Association of Women in Mathematics, she encouraged collaborative research, international outreach and cooperation between all the mathematical societies. She is the President of the EDGE (Enhancing Diversity in Graduate Education) Foundation, whose summer program won a "Mathematics Program that Makes a Difference" award from the American Mathematics Society in 2007, and a Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM) in 2017.
Professor Radunskaya is a 2021 Fellow of the Association for Women in Mathematics, a Fellow of the American Math Society, and she is the recipient of several awards, including a WIG teaching award in 2012, the 2017 AAAS Mentor award, and the 2020 Intercollegiate Biomathematics Alliance Distinguished Senior Fellowship. She was featured in the documentary, “The Empowerment Project: ordinary women doing extraordinary things”, as well as in the 2020 book: “Power in Numbers: the Rebel Women of Mathematics”.
Past Seminars - Fall 2022
Dr. Olalekan Babaniyi
Assistant Professor of Mathematics
Rochester Institute of Technology
Title: Variational Formulations for Direct Inversion from Full-Field Wave Data
Abstract: Dynamic elastography is a technique used to noninvasively estimate the mechanical properties of tissue from propagating mechanical waves. These mechanical properties can be used to noninvasively diagnose and help with the treatment of various diseases. To compute the mechanical properties, one needs to solve an inverse problem governed by differential equations. I discuss some of the mathematical properties of this inverse problem, and present several variational formulations that can be used to efficiently solve the inverse problem. I discuss some of the mathematical properties of these variational formulations, and compare their performance on simulated data.
Bio: Dr. Olalekan Babaniyi is currently an assistant professor in the School of Mathematical Sciences at Rochester Institute of Technology (RIT). Prior to joining RIT, he was a postdoctoral scholar at the University of California, Merced, working on developing computational tools to solve large scale inverse problems. Prior to that, he was a postdoctoral associate at Duke university working on inverse problems in the medical imaging field. He earned his Bachelors, Masters, and PhD degrees at Boston University where he began working on inverse problems as an undergraduate student.
Professor, Department of Mathematics
University of California, Los Angeles
Title: Bounded-Confidence Models of Opinion Dynamics on Networks
Abstract: I will discuss the modeling of opinion dynamics on various types of networks. After introducing some general questions and ideas in the field, I will focus on bounded-confidence models (BCMs), in which nodes have continuous-valued opinions and update those opinions when they interact with nodes with sufficiently similar opinions. I will discuss various generalizations of BCMs and examine how they affect consensus, polarization, and fragmentation of opinions on BCMs.
Bio: Mason A. Porter is a professor in the Department of Mathematics at UCLA. He earned his B.S. in Applied Mathematics at Caltech in 1998 and his Ph.D. from the Center for Applied Mathematics at Cornell University in 2002. After postdoctoral positions at Georgia Tech, Mathematical Sciences Research Institute, and Caltech, Mason joined the faculty in the Mathematical Institute at University of Oxford in 2007. He moved to UCLA in 2016. Mason studies many topics in complex systems, networks, and nonlinear systems. Twenty-five PhD students have completed their doctorates under Mason's mentorship, and he has also mentored many postdoctoral scholars and undergraduate students. In 2017, Mason received the Council on Undergraduate Research (CUR) Faculty Mentoring Award (Advanced Career Category) in the Mathematics and Computer Science Division. Mason is a Fellow of the American Mathematical Society, American Physical Society, and Society for Industrial and Applied Mathematics.
Dr. Michael Richards
Department of Biomedical Engineering
Rochester Institute of Technology
*Dr. Richards will be delivering this talk in person!
Title: Towards a Non-Invasive, Functional Metric of Flexor Tendon Healing Using Ultrasound Elasticity Imaging
Abstract: Traumatic injuries to the flexor tendons (FTs) of the hand are very common and successful functional restoration after surgical repair represents a challenge. Currently, there are no longitudinal non-invasive metrics of tendon healing. B-mode ultrasound (US) images have been proposed as a structural metric to quantify scar-tissue volume (STV) during tendon healing, however STV is not a functional metric and does not correlate well with the bio-mechanical properties or states of the tissue. Broadly, US elasticity imaging (US-EI) is a technique that measures displacement fields from US image sequences and uses this information to quantify the mechanical properties or states of the underlying tissue. Thus it is an ideal candidate to develop a functional metric of healing in tissues whose function is mechanical in nature. In this talk, I will discuss research that has been done by our group to develop a US-EI metric of tendon healing in a mouse model and, similarly, for clinical assessment FT healing in humans. Results of the animal study will be presented and discussed. While the human clinical work is in the preliminary phase, preliminary results will be presented and future directions will also be discussed.
Bio: Dr. Richards received his Bachelor of Science with in Biomedical Engineering from the University of Rochester followed by a PhD in Biomedical Engineering from Boston University. His postdoctoral training was completed in the Department of Radiology, Basic Radiological Sciences Division at the University of Michigan and at the University of Rochester, Department of Electrical and Computer Engineering. Currently, he is an Assistant Professor of Biomedical Engineering at Rochester Institute of Technology and an Adjunct Assistant Professor in the Department of Surgery the University of Rochester Medical Center. His research interests focus on the biomechanics of soft tissues and measuring the changes in mechanical properties of diseased tissues using clinical imaging modalities. His research is focused on the development, validation and implementation of elasticity imaging, or elastography, for applications to a wide variety of pathologies, including the diagnosis and monitoring of vascular diseases, assessment of musculoskeletal disorder severity and guiding strategies for physical therapy and breast cancer diagnosis. Elasticity imaging is a technique that offers the ability to provide physicians with entirely new, noninvasive diagnostic information (e.g. tissue mechanical properties and states) using established medical imaging techniques such as clinical ultrasound.
Dr. Nourridine Siewe
School of Mathematical Sciences
Rochester Institute of Technology
Title: Discrete-Time Models and a SARS CoV-2 Mystery: Sub-Saharan Africa’s Low SARS CoV-2 Disease Burden
Abstract: Worldwide, the ongoing SARS-CoV-2 virus has infected more than 440.8 million people and killed nearly 6.0 million. In Africa, the number of confirmed COVID-19 cases was approximately 11.5 million as of March 2, 2022, that is about 2.62 percent of the infections around the world. Many theories and modeling techniques have been used to explain this lower-than-expected number of reported COVID-19 cases in Africa relative to the high disease burden in most developed countries. We noted that most epidemiological mathematical models are formulated in continuous-time interval, and taking Cameroon in Sub-Saharan Africa, and New York State in the USA as case studies, in this paper we developed parameterized discrete-time models (DTM) of COVID-19 in Cameroon and New York State. We used these DTM to study the lower-than-expected COVID-19 infections in developing countries. We then used error analysis to show that a DTM captures the reported COVID-19 new cases in Cameroon and New York State more accurately than its corresponding continuous-time model. We also developed a new theory of model selection for a `better predictor' relative to either of the two possible epidemiological outcomes, namely, extinction or persistence.
Bio: Dr. Nourridine Siewe is a broadly trained mathematician who enjoys addressing and solving problems that affect humans, animals and nature in general. He obtained his PhD from Howard University in 2016 after which he did a postdoc at NIMBioS (National Institute of Mathematical and Biological Synthesis), then another one at the University of British Columbia before joining RIT where he currently serves as an Assistant Professor since August 2020. Dr. Siewe is also a proud alumnus of the African Institute of Mathematical Sciences (AIMS). My research focuses mainly on within-host mathematical models of cancer therapy and epidemiological models of the control of infectious diseases.
Dr. Shelby M. Scott
Senior Consultant and Health Data Scientist
Title: Humans Make Things Messy
Abstract: Models become notably more complex when stochasticity is introduced. One of the best ways to add frustrating amounts of randomness to your model: incorporate humans. In this talk, I discuss three different ways in which humans have made things messy in my mathematical models, statistical models, and data science work. Despite the fact that humans do, indeed, make things messy, they also make our models so much more realistic, interesting, and intriguing. So while humans make things messy, it is so worth it to bring them into your work. I will then talk about my top tips and tricks for surviving (and maybe even thriving) during graduate school.
Bio: Dr. Shelby M. Scott is a Health Data Scientist and Senior Consultant at Guidehouse. She received her B.S. in Biomathematics from Rhodes College in Memphis, TN, where she used Agent-Based Modeling to study the population dynamics of the Santa Cruz Island Fox. After, she attended the University of Tennessee, Knoxville, where she received her M.S. in Statistics and Ph.D. in Ecology and Evolutionary Biology. During this time, she used mathematical and statistical modeling to study the epidemic spread of gun crime in Chicago, Illinois. Dr. Scott then pursued an alternative-academic route and joined Guidehouse where she works with different federal and commercial clients to determine advanced analytics solutions to problems involving diverse datasets. She continues to serve as the Communications Officer of the Society for Mathematical Biology's Education Subgroup and is also involved in numerous mentorship programs and writing groups.
Dr. Rayanne Luke
Johns Hopkins University / NIST
Title: Improving SARS-CoV-2 diagnostic testing accuracy using higher dimensional probability models
Abstract: Antibody tests are routinely used to identify past infection, with examples including Lyme disease and, of course, COVID-19. However, accurate classification of samples as positive or negative is difficult when the corresponding measurement values overlap. In this talk, I will discuss a way to first separate positive and negative populations and then classify samples. We accomplish this by using more available measurements per person to build probabilistic models that capture structural characteristics of the data. As a bonus, viewing the data in higher dimensions allows us to hold out fewer samples in an indeterminate class.
Bio: Dr. Rayanne Luke is a Postdoctoral Fellow jointly appointed at Johns Hopkins University and the National Institute of Standards and Technology (NIST). She received her B.S. in Applied Mathematics from SUNY Geneseo, where she used image analysis to reduce cancer-monitoring CT scan time while maintaining image quality. She then attended the University of Delaware, where she received her Ph.D. in Applied Mathematics. Her Ph.D. research studied models of the human tear film, which she fit to data from tear film videos to identify clinically relevant parameters that cannot otherwise be estimated. She teaches for Johns Hopkins and does research through NIST, where she works in collaboration with epidemiologists on applied diagnostics problems focused on COVID-19. She has been an active member of the Association for Women in Mathematics (AWM) and in her spare time enjoys being a “pretty serious” runner.
Dr. Lea Popovic
Professor, Department of Mathematics and Statistics
Concordia University, Montreal
Title: Stochastic and spatial modelling of reaction network systems
Abstract: There are many applications (in biology and physics) where particles of different types interact and move in a heterogeneous space. Some of these applications can be modelled by systems of reaction networks, which describe the changes due to individual particles meeting and interacting with each other. There are also many such systems which take place in a spatial domain which is not homogeneous. Models combining spatial dynamics and interactions can be made at different levels of detail using both deterministic and stochastic objects. I will describe the modelling framework at the microscopic level which uses continuous time Markov chains to describe the evolution of the number of particles of different types in different parts of the space. I will discuss various modelling complexities such as multiple time-scale of changes in particle abundances, and the stochastic tools that can be used to reduce the models to a macroscopic descriptions. I will motivate the topic with a basic example of an intracellular mechanism of enzymatic kinetics.
Bio: Dr. Lea Popovic is a professor at Concordia University in Montreal. She was a postdoc at Cornell University and at the Institute of Mathematics and its Applications. She received her PhD from the University of California Berkeley and an Hon BSc degree from the University of Toronto. Her research interests are in probability theory and its applications, in particular, analysis of stochastic processes related to evolutionary biology, genetics, cell and systems biology.
Dr. Isaac Harris
Department of Mathematics
Title: Regularization of the Factorization Method with Applications
Abstract: In this talk, we discuss a new regularized version of the Factorization Method. The Factorization Method uses Picard’s Criteria to define an indicator function to image an unknown region. In most applications, the data operator is compact which gives that the singular values can tend to zero rapidly which can cause numerical instabilities. The regularization of the Factorization Method presented here seeks to avoid the numerical instabilities in applying Picard’s Criteria. This method allows one to image the interior structure of an object with little a priori information in a computationally simple and analytically rigorous way. Here we will focus on an application of this method to diffuse optical tomography which will prove that this method can be used to recover an unknown subregion from the Dirichlet-to-Neumann mapping.
Bio: As an undergraduate, my favorite class was Differential Equations. One of the things that fascinated me about the class was how different areas of mathematics came together to solve interesting problems. Ideas from Calculus and Linear Algebra were no longer separate entities but part of something bigger. Being able to study different applications of mathematics as a McNair Scholar help me cultivate my interest in mathematics. After graduating from the University of Delaware I was a postdoc at Texas A&M university then I joined Purdue’s Mathematics Department in Fall 2018 as an Assistant Professor. This was an exciting prospect where I could join a department with extremely talented mathematicians from many different fields.
Dr. Malena Español
School of Mathematical and Statistical Sciences
Arizona State University
Title: Variable Projection Methods for Separable Nonlinear Inverse Problems
Abstract: Variable projection methods are among the classical and efficient methods to solve separable nonlinear least squares problems. In this talk, I will present the original variable projection method, its use to solve large-scale blind deconvolution problems, and some new variants that preserve the edges in the solution.
Bio: Dr. Malena I. Espanol is an assistant professor in computational mathematics at the School of Mathematical and Statistical Sciences at Arizona State University. She earned her B.S. in mathematics from the University of Buenos Aires, Argentina, and M.S. and Ph.D. in mathematics from Tufts University. After graduation, Dr. Espanol was a postdoctoral scholar at the California Institute of Technology. In 2012, she started a faculty position at The University of Akron where she stayed until 2019, when she joined ASU. Her research interests are in the development, analysis, and application of mathematical models and numerical methods for solving problems arising in science and engineering, with a focus on problems related to materials science, image processing, and medical applications. She leads the Inverse Problems and Imaging research group at ASU and her research has been supported by NSF and DOE. She has been an active member of the Association for Women In Mathematics (AWM) and the Society for Industrial and Applied Mathematics (SIAM), by serving in multiple committees. She is a Math Alliance and AWM mentor, a 2013-2014 MAA Project NExT Fellow, and the 2022 Karen EDGE Fellow.
Past Seminars - Spring 2022
Dr. Daniela Calvetti
The James Wood Williamson Professor
Department of Mathematics, Applied Mathematics, and Statistics
Case Western Reserve University
Title: Multiscale, Multiphysiology Models of the Human Brain
Abstract: The brain is one of the most fascinating and mysterious organs, performing a variety of different tasks. This talk will present a family of models of the human brain that captures the feedback between metabolism, electrophysiology and hemodynamics and highlight the mathematical and computational challenges that had to be addressed. Computed examples illustrating how the model predictions reproduce some of the experimental findings will be also presented.
Bio: Dr. Daniela Calvetti, the James Wood Williamson professor, is an applied mathematician whose work on inverse problems connects mathematical models, scientific computing, Bayesian inference and uncertainty quantification. After receiving her Laurea in Mathematics from the University of Bologna, Italy in 1980, she moved to the University of North Carolina at Chapel Hill, where she completed her PhD in 1989. After holding faculty positions at North Carolina State University, Colorado State University-Pueblo and the Stevens Institute of Technology, she moved to Case Western Reserve University in 1997, where she chaired the department from 2007 to 2013. She has co authored three monographs, Introduction to Bayesian Scientific Computing, Computational Mathematical Modeling and Mathematics of Data Science, with two more coming soon, and approximately 150 peer reviewed papers. Her research has been supported by NSF, NIH and the Simons Foundation. She has graduated 20 PhD students and she has been in the editorial board of several scientific journals, including SIAM Journal on Matrix Analysis and its Applications, Mathematics of Computation, Inverse Problems and SIAM Review. She is currently the Program Director of the SIAM activity group on Uncertainty Quantification.
Dr. Emiliano Brini
School of Chemistry and Materials Science
Rochester Institute of Technology
Title: Physics-based simulations as drug discovery tools
Abstract: The first two steps in the process of rational drug design are (1) determining the structure of a target protein and (2) screening a series of compounds to identify the ones that best bind to the target. Predicting the structure of a protein means determining its most stable conformation. Identifying the best set of binders means identifying the molecules that bind to the protein more stably. Free energy is the physical quantity that describes the stability of chemical and biological systems. Physics-based simulations offer a principled way to estimate free energies, but most simulation approaches are too slow to sample the vast conformational space of protein systems. To get around this limitation, we developed MELD (Modelling Employing Limited Data), a Bayesian framework that leverages external information to reduce the conformational space of the system. In this talk, I will show how MELD leverages information from machine learning to predict the structure of proteins and how we use the MELD framework to compare the binding affinity of ligands.
Bio: Professor Brini got his BS and MS in chemistry at the University of Bologna (Italy). He earned his Ph.D. in physical chemistry at the TU Darmstadt (Germany). He then moved to the US to work as a postdoc first and as a research scientist later in the group of Ken Dill at Stony Brook University (NY). He is now an assistant professor of physical chemistry at RIT. The research in his group focuses on developing new computational tools to characterize the free energy landscape of biologically relevant systems like protein-protein interactions and protein-drug binding. He is also interested in exploring new ways to port these tools to characterize materials' properties.
Dr. Satish Kumar
Distinguished McKnight University Professor
Faculty Director, Industrial Partnership for Research in
Interfacial and Materials Engineering (IPRIME)
Department of Chemical Engineering and Materials Science
University of Minnesota
Title: Dynamic Wetting Failure and Air Entrainment in Coating Flows
Abstract: Dynamic wetting is crucial to processes where liquid displaces another fluid (such as air) along a solid surface, an important example being the deposition of a coating liquid onto a moving substrate. Dynamic wetting failure occurs when the displacement happens too quickly, and this leads to entrainment of the receding fluid into the advancing liquid. In coating processes this entrainment compromises the quality of the final product, so it is desirable to develop a fundamental understanding of the factors that control the onset of dynamic wetting failure. In this talk, I will discuss how the interplay between experiments and modeling has enabled progress in this area. The experiments involve measurements of the critical speed at which wetting failure occurs and flow visualizations of air entrainment. The modeling involves a combination of asymptotic analysis and two-dimensional finite element calculations that link the onset of wetting failure to limit points in families of steady-state solutions. The results reveal the mechanisms responsible for wetting failure and suggest strategies for delaying the onset of air entrainment in coating flows.
Bio: Dr. Satish Kumar is a Distinguished McKnight University Professor at the University of Minnesota where he is on the faculty of the Department of Chemical Engineering and Materials Science. Prof. Kumar received his undergraduate degree from Minnesota (1993), and his master's (1994) and doctoral degrees (1998) from Stanford University, all in chemical engineering. Following postdoctoral work at École Normale Supérieure (Paris) and the University of Michigan, he joined the faculty at Minnesota in 2001. Prof. Kumar currently serves as Faculty Director of the Industrial Partnership for Research in Interfacial and Materials Engineering (IPRIME), a university-industry consortium that has approximately 15 member companies. He is both a Fellow and an Outstanding Referee of the American Physical Society, is Co-Editor-in-Chief of the Journal of Engineering Mathematics, serves on the editorial board of the Journal of Non-Newtonian Fluid Mechanics, is a member of the Executive Committee of the American Physical Society Division of Fluid Dynamics, and is a former president of the International Society of Coating Science and Technology. Prof. Kumar's research involves integration of transport phenomena, colloid and interface science, rheology, applied and computational mathematics, and experiments to address fundamental issues motivated by problems in materials processing. These fundamental investigations, which are described in over 145 journal articles and 24 PhD theses, are frequently inspired by industrial applications such as coating and printing processes, polymer processing, nanofluidics/microfluidics, and energy.
Dr. Marylesa Howard
Scientist / Mathematician
Nevada National Security Site
National Nuclear Security Administration
Title: Monitoring Health of Large Machine Diagnostics Using Machine Learning
Abstract: The Department of Energy employs scientists, engineers, mathematicians, and technicians to work on problems ranging from renewable energy resources to global climate change. However, unbeknownst to many people is the fact that the Department of Energy is also the nation’s overseer of our nuclear weapons program. Experiments are designed and executed at the Nevada National Security Site to support the mission: ensuring the safety, security, and reliability of our nation’s nuclear stockpile. Regularly maintained and operated diagnostic machines are a backbone of data collection. Component failures can result in catastrophic downtime for the diagnostic, affecting schedules, cost, and performance. In this presentation, we will share our research into building models to monitor performance of large machine diagnostics using various measurements, like electrical current and voltage at multiple points on the path of the machine, from which we anticipate the ability to assess health, observe declining performance, and predict failures.
Bio: Dr. Marylesa Howard received her Ph.D. in mathematics from The University of Montana and joined the Nevada National Security Site (NNSS) as a scientist in 2013. She was recently recognized as the only Nevada recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) for her demonstrated research in image segmentation and leadership. She is an influential leader among scientists in Nevada, at the U.S. National Laboratories, and at universities across the country. She leads a team of scientists in data analysis for physics applications and spends time working in underground facilities. Dr. Howard is a champion for women in science, helping to direct graduate research and guide the careers of women around the country. She also works with universities to bring real-world scientific problems to students nationwide.
Dr. Miranda I. Teboh-Ewungkem
Professor of Practice
Title: Mosquito-Borne Diseases: Complexities and Challenges - Using Mathematics to Illuminate how Mosquito Behavioral Dynamics and its Interaction with Humans Enable Disease Transmission Success
Abstract: Many bottlenecks can hamper a successful disease transmission process. For vector-borne diseases, the challenges and bottlenecks are amplified because the disease-causing pathogens have to complete their life cycles in both the human/animal host and the vector. Thus, pathogens enact ways to succeed and ensure continuous success and survivability. To fully understand this, I will focus on the human malaria disease, one of the oldest vector-borne diseases caused by Plasmodium parasites and transmitted by female Anopheles mosquitoes. For a successful transmission of the malaria parasite from one human to another, a susceptible (healthy) adult female mosquito must successfully feed on two distinct humans – one infected with the parasite and the other not, at two distinct sequential time points. In addition, the parasite must be in its transmissible forms in both the human and the mosquito during the feeding encounters. That still does not guarantee transmission success; the feeding encounter may lead to the mosquito’s demise. If, however, the mosquito succeeds in drawing blood, it may not successfully infect the human. The bottlenecks involved illuminate how parasites exploit the human-mosquito interaction necessitated by the evolutionary and reproductive needs of mosquitoes to enhance parasite’s survivability. Therefore, understanding this multiscale complex process, viewed from the lens of transmitting mosquitoes, taking into consideration the mosquito’s own evolutionary need for survival, is essential. Preliminary work has shown that interesting dynamics can be observed even under simple mass action assumptions. Moreover, our methods allow for the incorporation of multiple feeding and mosquito gonotrophic cycles and their contributions to mosquito abundance which then directly and indirectly have consequences for malaria intensity and transmissibility success. It also indicates how a mosquito’s age is linked to disease transmissibility success when we incorporate parasite dynamics into a model that captures the interaction of mosquitoes, humans and the malaria parasite. One by-product of explicitly incorporating the mosquitoes’ gonotrophic cycles is the implicit embedding of the extrinsic incubation period of the disease in the modeling framework. In this talk, I will present some results that have been obtained from a complex system aimed at understanding malaria disease dynamics via the lens of mosquitoes.
Bio: Dr. Miranda Teboh-Ewungkem is a Professor of Practice in the Department of Mathematics at Lehigh University, serving as a faculty member since 2015. She previously served as an Assistant Professor of Mathematics at Lafayette College for about 10 years. Teboh-Ewungkem is also an appointed adjunct faculty member in the department of mathematics and the University of Buea in Cameroon, where she co-supervises PhD students in Mathematics. In addition, Teboh-Ewungkem is a leader and expert in the field of Mathematical Biology with focus on the use of Mathematical methodologies, tools, and Computational Mathematics to understand the key processes of infectious disease dynamics, spread and control, informing ways to achieve effective control. Her work involves a multiscale approach in modeling and analyzing the populations of disease-causing agents and disease-transmitting agents, together with how their complex interactions with humans enable these populations to thrive and grow, enhancing as well as enabling disease propagation. Her research. which has global applications and involves national and international collaborators, have yielded peer reviewed papers on infectious disease modeling, dynamical systems and mathematical analysis of the models developed, published in world regarded journals such as Journal of Mathematical Biology, Mathematical Biosciences, Journal of Dynamics and Differential Equations, Bulletin of Mathematical Biology, Plos One, Journal of Theoretical Biology, Malaria Journal, Lancet and more. She has also co-edited two Springer books. Her research has been supported by various grants through the National Science Foundation (NSF) and other national and international sponsors.
Dr. Teboh-Ewungkem has been invited to present her research work at international and national mathematics meetings and conferences as keynote speaker and as invited contributor. She has also been invited to share her work as a speaker at university mathematics departmental colloquiums and as a contributor at conference minisymposia sessions, both internationally and nationally, in different countries across Africa, Asia, Europe, North America and in New Zealand. She has also co-organized NSF funded international and national Mathematical conferences, workshops, and schools, including the first Buea international conference on mathematical science with representation from the US, Canada, Europe. Africa and a Malaria Modeling workshop with representation from the WHO at NIMBioS. She has co-organized several minisymposia at conferences.
Dr. Teboh-Ewungkem currently serves as the Chair of the Society for Mathematical Biology (SMB) Mathematical Epidemiology subgroup, which has more than 350 student and faculty members across the world, having served as co-chair the previous year. She is a proponent of mentoring the next generation of mathematicians and scientists with a strong adherence to attracting, retaining, and improving the number of students from underrepresented groups into the mathematical and stem fields. Thus, she has engaged undergraduate students in her research; served as an invited speaker /panelist at the 2016 Easton Area Middle School activity on “Connecting Girls with STEM Careers”, as well as the 2008 Kutztown University Sonia Kovalevsky High School Mathematics Day. Most recently, she volunteered and co-organize a math-hands-on session at the 2019 Career Connection Day at Da Vinci Science Center. She is currently serving as a board of trustee member at Lehigh Valley Academy Regional Charter School, having served in different roles as regular member, treasurer, and vice president. She is an appointed fellow of the African Scientific Institute since 2020, was awarded the Unity in Diversity Community Award for women's History Month 2020, 2021, was nominated to the African Academy of Science (AAS) in 2018 and 2019, and was the Mathematically Gifted and Black Honoree in February 2020.
Dr. Pras Pathmanathan
Office of Science and Engineering Laboratories
Center for Devices and Radiological Health
US Food and Drug Administration
Title: Computational Modeling for Medical Devices: Applications in Regulatory Submissions and Credibility Assessment
Abstract: Computational modeling and simulation (M&S) have a myriad of applications in medical devices, from virtual testing of new devices during either internal development or for regulatory review, to M&S algorithms being implemented within medical device software. An enormous range of M&S disciplines are relevant to medical devices, from traditional solid mechanics models to predict fracture risk of implanted devices, electromagnetic models to predict MRI-induced heating of metallic implants, to physiological patients models used to evaluate physiologic closed loop control devices such as automated mechanic ventilators. The Center for Devices and Radiological Health (CDRH) at the US Food and Drug Administration (FDA) is tasked with ensuring the safety and effectiveness of medical devices on the US market. A key question that CDRH has been faced with in recent years, as M&S has become increasingly used in medical device regulatory submissions, is when can M&S be trusted, that is, when is M&S credible? This talk will begin with an overview of M&S at FDA, following by an overview of M&S applications for medical devices. We will then discuss model credibility assessment, including frameworks for assessing models that have recently been developed by the medical devices community, and presenting research and results of assessment of models of the electrical activity in the heart.
Bio: Dr. Pras Pathmanathan is a senior scientist at the Office of Science and Engineering Laboratories (OSEL), the regulatory science research Office within FDA’s Center for Devices and Radiological Health (CDRH). His research focuses on methods for establishing the credibility of computational models, covering the wide range of models relevant to medical devices, with a focus on physiological models including computational cardiac models. He leads or collaborates in numerous initiatives advancing computational modeling in healthcare, including co-founding and co-chairing FDA’s Modeling and Simulation Working Group, and leading OSEL’s regulatory science program on Credibility of Computational Models. He received a BA in mathematics from Cambridge University and an MSc and PhD in computational cardiology from Oxford University.
Dr. Nishant Malik
School of Mathematical Sciences
Rochester Institute of Technology
Title: From Japan to Amazonia: Computational Stories from the Complexity Lab
Abstract: Through short computational stories, this talk will introduce the research we are carrying out in our student-centered research group: The Complexity Lab.
The first story will be about landslides and cherry blossoms in Japan and how we combine geometric and topological methods with machine learning to classify landslides and changing patterns of cherry blossoms dynamics.
We will move to South Asia for the second story and present an algorithm that uses networks and graphs to predict summer monsoon rainfall. We will follow this story with a sequel set in South America. Using similar mathematical techniques as in the prequel, we will identify changes in the connectivity of the Amazon rainforest to the rest of the climate system.
The next set of tales will be brief, where we will introduce new equation-free methods to predict the temporal evolution of chaotic systems and techniques to build mathematical models entirely from data. We will end with a picture story, presenting visualizations of the US political economy.
Bio: Dr. Malik did his Ph.D. work at the Potsdam Institute for Climate Impact Research in Germany under renowned mathematical physicist Juergen Kurths. Subsequently, the Physical Society of Berlin awarded the Carl Ramsauer Prize for 2012 to his Ph.D. work. Before joining RIT, he worked at Dartmouth College and UNC-Chapel Hill as a postdoc. Dr. Malik has a wide range of research interests within the data-driven analysis and mathematical modeling of complex systems. In his research, he employs tools from network science, theory of nonlinear and stochastic dynamical systems, and applied statistics and enjoys working on mathematical problems across natural and social science disciplines.
Dr. Lucia Carichino
School of Mathematical Sciences
Rochester Institute of Technology
Dr. Eli Borrego
Thomas H. Gosnell School of Life Sciences
Rochester Institute of Technology
Title: Mathematical Modeling of Jasmonic Acid Biosynthesis During Plant Stress Responses
Abstract: Crop resilience relies on plant hormones to engage and coordinate the appropriate defense against stresses. These molecules are connected to each other in complex signaling networks that serve as biological algorithms that fine-tune the activation of suites of genes that allow plants to respond to their environments, defend themselves, and optimally utilize resources. Jasmonic acid is an lipid-derived hormone and provides defense against insects and pathogens, however the precise mechanisms regulating the jasmonic acid biochemical reactions in vivo remain to be characterized, especially in agriculturally-relevant crop species.
In this talk we present the interdisciplinary mathematical and biological framework the we are currently developing to study jasmonic acid production during the maize wound response. Mathematical models will be used as “virtual lab” and work in synergy with “wet lab” experiments to iteratively optimize model parameters, isolate and quantify the relative contribution of factors that are difficult to separate in vivo, and to develop and test hypothesis.
Bio: Dr. Lucia Carichino is an Assistant Professor in the School of Mathematical Sciences at RIT. The focus of her research is on mathematical and computational models of multiscale biological systems. Before joining RIT, she was a Postdoctoral Scholar at Worcester Polytechnic Institute. She earned her Bachelor’s and Master’s Degree in Mathematical Engineering from Politecnico di Milano in Italy, and her PhD in Mathematics from Purdue University.
Dr. Eli Borrego was born and raised in the Rio Grande Valley of South Texas. He graduated with a Ph.D. in Plant Pathology in 2014 and joined the Thomas H. Gosnell School of Life Sciences in 2019. His program explores lipid signals in plant interactions with microbes, insects, and other stresses. The research addresses problems such as insect and pathogen resistance, drought tolerance in crop species.
Dr. David S. Ross
School of Mathematical Sciences
Rochester Institute of Technology
Title: A Survey of Math Models in Curtain Coating
Abstract: Curtain coating is used to produce thin coatings on surfaces for various purposes: chemically sensitive coatings for blood tests; light-filtering coatings on windows to protect again ultraviolet rays; paint coatings on machine parts to make them attractive; frosting coatings on doughnuts to make them tastier. In this talk, Professor Ross will discuss the purpose and structure of several models of the fluid mechanics, and one model of the surface chemistry, of coating curtains. He will discuss the importance of these models to the application field, and he’ll also touch on a couple of neat PDE problems.
Bio: Dr. David Ross took his BA from Columbia and his PhD from NYU, both in mathematics. He worked for A.T.&T, Eastman Kodak, and Kaiser Permanente and its spinoff Archimedes before settling in to the School of Mathematical Sciences in 2006. His primary mathematical interests are PDE and dynamical systems, always associated with applications. Earlier in his career, those applications were primarily in fluid mechanics, solid mechanics, and industrial chemistry. These days those applications are mostly in biophysics, biochemistry, and pharmaceutical research.
Dr. Steven J. Weinstein
Professor and Department Head
Department of Chemical Engineering
Rochester Institute of Technology
Title: Modeling of Thin Film Flows
Abstract: Laminar flow of thin liquid films is fundamental to coating processes. Even with modern high-powered computers, numerical solutions of the full set of governing equations are often time-intensive; this is due to the nonlinearities inherent in free surface flows, as well as the small aspect ratios typically involved. It is often the case, however, that the film thickness varies gradually in the flow direction in various regions of a coating process. Consequently, the classical boundary-layer approximation to the Navier-Stokes equation is justified. A standard approach to the boundary layer equations, attractive because of its ease of use, is to integrate the boundary layer equations across the film and introduce an assumed form of the velocity field. This velocity field is typically parabolic and self-similar, in that it does not change its basic shape in the direction of the flow. The result is a simplified equation, henceforth referred to as the film equation, involving only the unknown film thickness. In many cases, the film curvature is small and surface tension terms in the film equation are negligible; the resulting first-order nonlinear differential equation is typically straightforward to solve, oftentimes in analytical form.
Under conditions of negligible surface tension, the film equation can exhibit a singularity called a critical point, which is related to a change in the direction of the underlying wave propagation. In many problems, the singularity may be eliminated by a particular choice of parameters, and a physically correct model of the flow may be obtained. Singularity elimination can occur in configurations where the fluid accelerates in the primary flow direction (such as in weir flows or rapid dip coating) and the wave propagation thus undergoes a subcritical-to-supercritical transition. On the other hand, a non-removable singularity arises in configurations where the fluid is decelerating in the primary direction of flow, and the wave propagation thus transitions from supercritical to subcritical. In this talk, we specifically examine decelerating flow on an inclined plane, which is perhaps the simplest configuration that can exhibit a non-removable singularity. Finite element predictions and numerical solutions of the full boundary layer equations indicate that interface solutions having small slopes should exist under conditions where the film equation fails. Using both physical and mathematical arguments, we demonstrate that the film equation must be modified for a velocity profile of changing shape. The resulting predictions are found to compare favorably with finite element solutions, the full boundary layer equations, and experiments.
Bio: Dr. Steve Weinstein received his B.S. in Chemical Engineering from the University of Rochester and his MS/PhD in Chemical Engineering from the University of Pennsylvania. He worked for Eastman Kodak Company for eighteen years after receiving his PhD, and joined RIT in January of 2007. Dr. Weinstein is well published in the field of coating and interfacial fluid mechanics, and has focused on the dynamics and hydrodynamic stability of thin liquid films, curtain flows (flows in thin sheets of liquid), die manifold design via asymptotic methods, and web dynamics; he also has seven patents in these areas. He founded the Department of Chemical Engineering at RIT in the fall of 2008 and has continued as its department head. Dr. Weinstein has worked with colleague Nate Barlow at RIT to develop and demonstrate a novel approximant method to sum divergent series solutions that naturally arise in the analysis of various problems in mathematical physics. Dr Weinstein routinely provides mathematical analysis to support collaborators beyond his core research areas, and thus has a publication record that spans disparate fields.
Dr. Amitrajeet Batabyal
Arthur J. Gosnell Professor of Economics
Department of Economics
Rochester Institute of Technology
Title: A Political-Economy Perspective on Mayoral Elections and Urban Crime
Abstract: We provide a political-economy analysis of crime prevention in an arbitrary city in the United States. City residents (voters) elect mayors (politicians) and elected mayors determine the resources to be allocated to crime prevention. Between the two time periods, there is an election. Politicians are either honest or dishonest. The marginal cost of public monies 𝜓 measures how efficiently an elected mayor converts tax receipts into crime prevention. Voters have identical per period utility functions. We ascertain the equilibrium outcome and per period voter well-being. Second, we show that an increase in 𝜓 reduces the equilibrium allocation of resources to crime prevention and voter well-being. Third, a dishonest politician can delay the revelation of his dishonesty. A critical value of 𝜓, 𝜓∗, exists such that a dishonest incumbent separates and loses the election if and only if 𝜓 > 𝜓∗ and he pools and is re-elected otherwise. Finally, we note that an increase in 𝜓 can raise voter well-being when politicians are more likely to be dishonest.
Bio: Dr. Amit Batabyal is Arthur J. Gosnell Professor of Economics at the Rochester Institute of Technology (RIT). He obtained a B.S. with Honors and Distinction in Applied Economics and Business Management from Cornell University in 1987, a M.S. in Agricultural and Applied Economics from the University of Minnesota in 1990, and a Ph.D. in Agricultural and Resource Economics from the University of California at Berkeley in 1994. He uses microeconomic theory and mathematical techniques to model and better understand problems in natural resource, environmental, and regional economics. He is the recipient of numerous awards including the Geoffrey J. D. Hewings Award from the North American Regional Science Council in 2003, the Moss Madden Memorial Medal from the British and Irish Section of the Regional Science Association International in 2004, the Outstanding Achievement in Research Award from the Society for Range Management in 2006, the Trustees Scholarship Award from the RIT Board of Trustees in 2007, and the Mattei Dogan Foundation Prize from the International Social Science Council in 2013. He is an Honorary Member of the Regional Science Association International’s Japan Section and a Fellow of the Regional Studies Association and Regional Science Association International.
Dr. Mirjeta Pasha
School of Mathematical and Statistical Sciences
Arizona State University
Title: Computational and learning methods for large-scale inverse problems
Abstract: Inverse problems are ubiquitous in many fields of science such as engineering, biology, medical imaging, atmospheric science, and geophysics. Three utmost challenges on obtaining meaningful solutions to large-scale and data-intensive inverse problems are ill-posedness of the problem, large dimensionality of the parameters, and the complexity of the model constraints. In this talk, we use a combination of tools from numerical linear algebra, optimization, parameter estimation, and statistics to overcome computational challenges that arise in data-intensive inverse problems. In particular, we describe computationally efficient methods that learn optimal lp and lq norms for Lp-Lq regularization and learn optimal parameters for regularization matrices defined by covariance kernels. Further we describe some efficient methods for computing solutions with preserved edges to dynamic inverse problems, where both the quantities of interest and the forward operator change at different time instances. Numerical examples such as tomographic reconstruction and image deblurring illustrate the performance of the discussed approaches in terms of both accuracy and efficiency. Current and potential future research directions will conclude the talk.
Bio: Dr. Mirjeta Pasha is a Postdoctoral Associate at Arizona State University. She obtained a B.S. and a M.S. in Engineering Mathematics and Computer Science from University of Tirana (Albania) in 2012 and 2014, respectively. She was a tenure-track faculty at Polytechnic University of Tirana from 2014 to 2016. In 2019 she received a M.S from Kent State University (KSU) and a Ph.D. in Applied Mathematics in 2020. After completing her Ph.D. for a semester, she was a Visiting Assistant Professor of Computer Science at John Carroll University.
Dr. Pasha uses her computational skills to solve problems that arise in many applications in science and engineering with a particular interest in medical imaging and large-scale data analysis. She develops algorithms and numerical methods for large-scale inverse problems. Her research is strongly focused on numerical linear algebra, but she also uses techniques and tools from statistics, numerical optimization, and partial differential equations. Beyond inverse problems, she works on advancing research in areas such as tensor decompositions, learning methods, and computational statistics. She has a strong interest in developing curricula and teaching students computational math and data science skills.
Past Seminars - Fall 2021
Dr. Jacob Bedrossian
Professor of Mathematics
Center for Scientific Computation and Mathematical Modeling
University of Maryland
Title: Hydrodynamic stability at high Reynolds number
Abstract: The stability of equilibria solutions of the incompressible Euler and Navier-Stokes equations at high Reynolds number has been studied since the 1800s with the work of Kelvin, Rayleigh, Reynolds and others. However, only in recent years have we started to get a firm mathematical understanding of this field, even for the deceptively simple case of shear flows and vortices. I will outline some of the many recent advances in the area, including inviscid damping, enhanced dissipation, subcritical transition, vortex axi-symmetrization, and the local well-posedness of vortex filaments.
Bio: Dr. Jacob Bedrossian is a Professor of Mathematics at the University of Maryland, College Park. He earned his PhD from UCLA in 2011. His research focuses on the analysis of deterministic and stochastic PDEs arising in fluid mechanics and plasma physics, especially on understanding stability, mixing, chaos, and turbulence. He has earned the 2019 SIAG/APDE prize (joint with Nader Masmoudi), the 2019 IMA prize, the 2020 Peter Lax Award, a 2020 Simons Fellowship, and is an invited speaker at the 2022 ICM.
Dr. Howard Qingsong Tu
Assistant Professor of Mechanical Engineering
Rochester Institute of Technology
Title: Multi-physical Modeling in Clean Water and Energy
Abstract: Access to the affordable and sustainable clean water and energy are becoming increasingly important for modern society. Many cutting-edge techniques aim to provide more efficient and low-cost clean water and energy, including reverse osmosis (RO) for water purification and solid-state batteries for energy storage. However, the lack of understanding to many fundamental problems hinder the practical applications of these techniques, which usually originate from the complicated multi-physical couplings spanning across multi-scale. In this talk, I will share my research in the effort of investigating electro-chemo-mechanical problems laying behind both applications. I will share my research on the heterogeneous transport and reactions of electrons and Li-ions in the solid-state batteries, including the understanding of dendrite problem at the anode side and the optimization of composite cathode in order to increase the energy density. I will also talk about my new research directions including: building a unified toolkit to solve multi-physical problems, developing a data-driven method with the machine learning technique to bridge up the nanoscale and continuum-scale modeling, and screening new membrane material for more efficient water purification.
Bio: Dr. Howard Qingsong Tu is an assistant professor in the mechanical engineering department, and runs the clean energy and water lab (www.CewLab.org). He got his Ph.D. degree from the University of California at Berkeley in 2017. He was a postdoc researcher in the material science division at Lawrence Berkeley National Lab from 2017-2021. His research has focused on solving electro-chemo-mechanical problems in energy storage systems (such as solid-state batteries) and water desalination systems (such as reverse osmosis), with the close-loop data-simulation-experiment approach.
Dr. Olivia Prosper
Assistant Professor of Mathematics
University of Tennessee
Title: Multi-scale Modeling of Malaria Parasite Diversity
Abstract: Malaria, a parasitic disease spread by mosquitoes, imposes an enormous health and economic burden across the globe. The Ross-Macdonald mathematical framework for the transmission dynamics of malaria, developed in the early 20th century, has informed control policies for this disease and provided the basis for numerous population-level models for vector-borne disease of varying complexity. In the world of infectious disease modeling, there has been an increased interest in linking within-host pathogen dynamics to between-host transmission. I will introduce a multi-scale model of malaria that tracks parasite life cycle dynamics and parasite sequences within each mosquito and each human, as well as the transmission of these genetically diverse parasites between these two populations. The degree of parasite diversity has important implications for the transmissibility of a malaria infection and the severity of the disease for the infected human. We investigate how this diversity changes over time, and how it differs based on differences in environmental and epidemiological characteristics of the system.
Bio: Dr. Prosper's research interests lie at the interface between mathematics and biology, with much of her work focused on developing and analyzing mathematical models of infectious disease dynamics to better understand the interplay between different types of heterogeneities affecting disease dynamics and disease control measures. In particular, her current work focuses on four main topics: (1) linking within-host pharmacokinetics and pharmacodynamics to population-level vector-borne disease transmission and the implications for the spread of drug resistant pathogens, (2) modeling the generation of within-vector parasite diversity and the subsequent spread of genetically distinct parasite populations, (3) understanding population-level dynamics arising from heterogeneity in spatial transmission patterns and host movement, and (4) model identifiability.
Dr. Raissa D'Souza, Professor
Department of Computer Science
Department of Mechanical and Aerospace Engineering
Graduate Group in Applied Math
Graduate Group in Physics
Complexity Sciences Center
University of California, Davis
Title: Complex networks with complex nodes
Abstract: Real world networks -- from brain networks to social networks to critical infrastructure networks -- are composed of nodes with nonlinear behaviors coupled together via highly non-trivial network structures. Approaches from statistical physics study how behaviors arise in collections of simple elements connected together in complex structures such as modular or scale-free networks. They provide understanding about massive networks, revealing implications that network structure can have on network function and resilience. In contrast, approaches from dynamical systems and control theory typically study small systems of nonlinear nodes connected together in simple networks. This talk presents recent work bridging the gap of complex networks with complex nodes. First is considering nonlinear phase-amplitude oscillators coupled together by simple ring networks and how the interplay of nodal dynamics and coupling structure gives rise to emergent long-range order and its stability properties. Next is increasing the structural complexity from dyadic networks to hypergraphs to capture higher-order interactions and study cluster synchronization. The focus will then turn to social networks, starting from modeling humans as nodes with underlying attributes coupled in complete graphs, and moving on to real-world multiplex social networks in macaque monkey societies. We reveal the tensions between the forces of homophily and social balance, as well as developing a meaningful multiplex ranking method that takes into account the heterogeneous characteristics and functions of the distinct layers in the multiplex.
Bio: Dr. Raissa D'Souza is Professor of Computer Science and of Mechanical Engineering at the University of California, Davis, as well as an External Professor at the Santa Fe Institute. She uses the tools of statistical physics and applied mathematics to develop models capturing the interplay between the structure and function of networks. The general principles derived provide insights into the behaviors of real-world networks such as infrastructure networks and social networks, and opportunities to identify small interventions to control the self-organizing, collective behaviors displayed in these systems. She is a Fellow of the American Physical Society, a Fellow of the Network Science Society, and has received several honors such as the inaugural Euler Award of the Network Science Society and the 2018 ACM Test-of-Time award. She is currently Lead Editor at Physical Review Research and on the Board of Reviewing Editors at Science. She was a member of the World Economic Forum's Global Agenda Council on Complex Systems and served as President of the Network Science Society, 2015-18.
Dr. Katie Morrison
Associate Professor of Mathematical Sciences
University of Northern Colorado
Title: Predicting neural network dynamics from graph structure
Abstract: Neural networks often exhibit complex patterns of activity that are shaped by the intrinsic structure of the network. For example, spontaneous sequences of neural activity have been observed in cortex and hippocampus, and patterned motor activity arises in central pattern generators for locomotion. In this talk, we will focus on a simplified neural network model known as Combinatorial Threshold-Linear Networks (CTLNs) in order to understand how the pattern of neural connectivity, as encoded by a directed graph, shapes the emergent nonlinear dynamics of the corresponding network. We will see that important aspects of these dynamics are controlled by the stable and unstable fixed points of the network, and show how these fixed points can be determined via graph-based rules. We will then apply these theoretical results to produce a model CPG for quadruped gaits.
Bio: Dr. Katie Morrison is an Associate Professor in the School of Mathematical Sciences at University of Northern Colorado. She received her BA from Swarthmore College, double majoring in mathematics and psychology, and her PhD in mathematics from the University of Nebraska. Her dissertation work was in algebraic coding theory, but she has since transitioned into mathematical neuroscience. Dr. Morrison’s current research focus is on the mathematical theory and analysis of neural networks and neural codes, using tools from algebra, discrete mathematics, differential equations, and topology. This work has been supported by an NIH BRAIN Initiative grant as well as two NSF mathematical biology grants.
Dr. Rodman Linn
Computational Earth Science Group
Earth and Environmental Sciences Division
Los Alamos National Laboratory
Title: Fire/Atmosphere Modeling: Opportunities and Challenges
Abstract: Wildland fires continue to pose risk to lives and property and thus practitioners and scientists continue to work to gain better understanding and ability to predict their behavior. Simultaneously, wildland fire decision makers are working towards more proactive approaches to managing the risk of wildfire, such as fuels treatments and prescribed fire. Executing such measures requires the ability to explore the ramification of such treatments as well as ensure that prescribed fires will meet their objectives. Experiments and observations have demonstrated that the two-way feedbacks between fires and atmosphere play critical roles in determining how fires spread or if they spread. Advancements in computing and numerical modeling have generated new opportunities for the use of models that couple process-based wildfire models to atmospheric computational fluid dynamic (CFD) models. These process-based coupled fire/atmosphere models, which simulate critical processes such as heat transfer, buoyancy-induced flows and vegetation aerodynamic drag, are not practical for operational faster-than-real-time fire prediction due to their computational and data requirements. However, these process-based coupled fire-atmosphere models make it possible to represent many of the fire-atmosphere feedbacks and thus have the potential to complement experiments, add perspective to observations, bridge between idealized-fire scenarios and more complex and realistic landscape fire scenarios, allow for sensitivity analysis that is impractical through observations and pose new hypothesis that can be tested experimentally. Additionally, coupled wildfire/atmosphere modeling opens new possibilities for understanding the sometime counterintuitive impacts of fuel management and exploring the implications of various prescribed fire tactics. Certainly, there need to be continued efforts to validate the results from these numerical investigations, but, even so, they suggest relationships, interactions and phenomenology that should be considered in the context of the interpretation of observations, design of fire behavior experiments, development of new operational models and even risk management. One additional goal for the use of CFD based coupled fire/atmosphere models is to highlight the necessary phenomenology that is necessary in fast running models.
Bio: Dr. Rodman Linn is a senior scientist in the Earth and Environmental Sciences Division at Los Alamos National Laboratory and a Professor in the Halıcıoğlu Data Science Institute at the University of California San Diego (Joint UCSD/LANL appointment). For over two decades, he has served as principal investigator for a process-based coupled fire/atmosphere model, FIRETEC. Dr. Linn leads LANL efforts to use next-generation process-based wildfire models for the study of fundamental wildfire behavior, evaluation of prescribed fire tactics, understanding influences of complex environmental conditions on fire behavior and wildfire’s interaction with other landscape disturbances such as insects or drought. Dr. Linn is the co-lead developer of the new fast-running coupled fire-atmosphere model QUIC-Fire.
Dr. Margaret Cheney, SIAM Fellow
Yates Chair and Professor of Mathematics
Colorado State University
Title: Introduction to Synthetic-Aperture Radar Imaging
Abstract: Radar imaging is a technology that has been developed, very successfully, within the engineering community during the last 50 years. Radar systemson satellites now make beautiful images of regions of our earth and of other planets such as Venus. One of the key components of this impressive technology is mathematics, and many of the open problems are mathematical ones. This lecture will explain, from first principles, some of the basics of radar and the mathematics involved in producing high-resolution radar images.
Bio: Dr. Margaret Cheney is the Yates Chair and Professor of Mathematics at Colorado State University. She was elected as a SIAM Fellow in 2009 "for contributions to inverse problems in acoustics and electromagnetic theory"
Dr. Juan Restrepo
Distinguished Member of the R&D Staff
Mathematics in Computation Section
Computer Science and Mathematics Division
Oak Ridge National Laboratory
Title: Research and Opportunities in the Mathematical Sciences at Oak Ridge National Laboratory
Abstract: I will present a general overview of Oak Ridge National Laboratory research in mathematics and computing. A brief description of my own initiatives and research will be covered as well. I will also describe opportunities for students, postdocs, and professional mathematicians.
Bio: Dr. Juan M. Restrepo is a Distinguished Member of the R&D Staff at Oak Ridge National Laboratory, and he is a fellow of SIAM and APS. He holds professorships at U. Tennessee and Oregon State University. Prior to ORNL, he was a professor of mathematics at Oregon State University and at the University of Arizona. He has been a frequent IMA visitor. His research focuses on data-driven methods for dynamics, statistical mechanics, transport in ocean and uncertainty quantification in climate science.
Dr. Alexander Hoover
Assistant Professor of Mathematics
The University of Akron
Title:From Pacemaker to Vortex Ring: Using Mathematical Modeling to Demystify Medusan Biomechanics
Abstract: For an organism to have a robust mode of locomotion, their neuromuscular organization must be adaptable in a constantly evolving environment. In jellyfish, this robustness emerges from the interaction of pacemakers with a motor nerve net that communicates directly with the musculature. A set of independently-firing pacemakers alter their firing frequency in response to environmental cues, forming a distributed mechanism to control a jellyfish's muscular contractions and gives insight into how the first multicellular organisms organized muscle-driven propulsion.
In this talk, we examine this biomechanical system with a model jellyfish bell immersed in a viscous fluid and use numerical simulations to describe the interplay between active muscle contraction, passive body elasticity, and fluid forces. We examine some of the biological paradoxes that eluded marine biologists, and how mathematical modeling can lend more insight into their unraveling. We then use this model to explore the interplay between material and neural time scales present in medusan biomechanics and the emergence of neuromechanical wave resonance as an evolutionary design principle and constraint. The results here have many potential implications for the actuation and design of soft-body robotics and tissue-engineered pumps.
Bio: Dr. Hoover is an Assistant Professor at the University of Akron in the Department of Mathematics. His research focuses on the interplay of fluids, mechanics, and behavior in organism pumping, flying and swimming. In particular, he is interested in how their interaction influence the production of efficient and robust mechanisms of fluid transport and locomotion. He is generally interested in mathematical biology, biofluids, computational modeling, and applied mathematics.
Dr. Z. John Zhai
Professor in Architectural Engineering
Department of Civil, Environmental and Architectural Engineering
University of Colorado at Boulder
Title: Infection Risk of Airborne Respiratory Disease: Modeling Principles and Applications
Abstract: Infection risk is commonly used to predict potential health impacts of airborne respiratory diseases (ARD) such as “SARS-CoV-2” and associated environment conditions and mitigation measures. The Wells-Riley model is one most popular model to predict a single mean infection risk of a specific indoor ARD event, which relies on the assumption of perfect air-mixing in the space. Detailed distribution of infection risk, especially for large spaces such as large lecture hall, indoor stadium, and ballroom, will be highly desired for evaluating indoor risks and improvement performance of mitigating strategies. This talk presents the development of new formulae for calculating the spatial and temporal distribution of infection risk, stemming from the original Wells-Riley concept but integrating the spatial and temporal distribution of pathogen concentrations. Case studies are showed for typical large public spaces (e.g., restaurant and ballroom). Distributed infection risks are predicted with and without mitigation measures, upon which critical parameters of air cleaners can be optimized. The method can be employed for estimating local infection risks of airborne respiratory diseases using either measured or simulated pathogen concentration.
Bio: Dr. John Zhai is a Professor in the Department of Civil, Environmental and Architectural Engineering (CEAE) at University of Colorado at Boulder (UCB). He has a unique and integrated background in both Mechanical and Architectural Engineering with an Engineering Doctor degree in Fluid Mechanics (Tsinghua University, 1999) and a Ph.D. in Building Technology (MIT, 2003). Dr. Zhai's research and teaching interests and expertise include: building thermal and environmental systems; indoor and outdoor environmental quality; sustainable and immune buildings. Dr. Zhai is a Fellow of The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), The International Society of Indoor Air Quality and Climate (ISIAQ), and The International Building Performance Simulation Association (IBPSA).
Dr. Paul E. Barbone
Professor of Theoretical Acoustics & Applied Mechanics
Department of Mechanical Engineering
Division of Material Science & Engineering
Title: Thoughts on Shear Waves and Shear Waves on Thoughts
Abstract: Isotropic solid materials can support the propagation of both dilatational waves and distortional waves, the latter better known as elastic shear waves. Propagation of shear waves in soft tissues is a subject of considerable current interest in the biomechanics and biomedical imaging communities. These applications present many fun and exotic situations to study shear waves, including in the presence of strong magnetic fields, superposed large poroelastic deformations, strong material property gradients, and medium activation. We use mathematical models to study elastic shear wave propagation in these scenarios.
Bio: Dr. Barbone is a Fellow of the American Institute for Medical and Biological Engineering and a Fellow of the Acoustical Society of America. He uses tools from applied mathematics to study forward and inverse problems in (bio)mechanics, (bio)acoustics, medical imaging, and other areas. He has studied and made contributions in the areas of structural acoustics; waves in elastic media, piezoelectric media, layered media, periodic media, and in media with microstructure; vibration of infinitely complicated structures, hybrid asymptotic/numerical methods, optimal finite element methods, algebraic eigenvalue problems, nonlinear acoustic propagation, multiphase (bubbly) flow and ultrasound imaging. Dr. Barbone's most recent interests lie in the areas of inverse elasticity problems as applied to medical imaging (elastography) and soft-tissue biomechanics.
Dr. Maia Martcheva
Professor of Mathematics
Affiliated Professor of Biology
University of Florida
Title: A Network Immuno-epidemiological Model of HIV and Opioid Epidemics
Abstract: We introduce a network immuno-epidemiological model of HIV and opioid epidemics where the jointly affected class is structured by the within-host dynamics. We fit the within-host model to data, collected in monkeys. We compute the reproduction numbers of the HIV and opioid epidemics. We show that the disease-free equilibrium is locally stable if both reproduction numbers are below one, and unstable if at least one of the reproduction numbers is above one. The HIV-only equilibrium exists if the reproduction number of HIV is larger than one. The opioid-use only equilibrium exists if the reproduction number of opioid use is larger than one. The HIV-only equilibrium is locally asymptotically stable if the invasion number of the opioid epidemic is below one and unstable if the invasion number of opioid epidemic is above one. The opioid-only equilibrium is locally asymptotically stable if the invasion number of the HIV epidemic is below one and unstable if the invasion number of HIV epidemic is above one. We fit the no-network model to within-host and between host data to determine parameters. We perform structural and practical identifiability of the within-host model. Simulation suggest that that coexistence of HIV infected, opioid affected and co-affected individuals is possible.
Bio: Dr. Maia Martcheva is a professor of mathematics at University of Florida. She obtained her PhD at Purdue University in 1998. After that she was a postdoc at IMA, University of Minnesota, Arizona State university and an NSF Advance Fellow at Cornell University in 2002-2003. Since 2003 she has been an Assistant, Associate and Full Professor at the Department of Mathematics, University of Florida. Dr. Martcheva has published around 120 papers. She has also published 3 books: Gender Structured Population Modeling (2005, SIAM), An Introduction to Mathematical Epidemiology (2015, Springer), and Age Structured Population Modeling (2020, Springer). Her research has been supported by the National Science Foundation. In 2016-2018, Dr. Martcheva was a Managing Editor of Journal of Biological Systems. Currently, she serves on the editorial boards of Journal of Biological Systems, Journal of Biological Dynamics and Journal of Difference Equations with Applications.
Past Seminars - Spring 2021
Assistant Professor of Applied Mathematics
Department of Mathematics
The University of Alabama
Title: Effective Models of Flow in Highly Heterogeneous Multiscale Naturally Fractured Porous Media
Abstract: Given the contrast in properties of the multiscale network of fractures and the matrix in naturally fractured reservoirs, several flow regimes may form at microscale. For low contrasts, the conductivities of the matrix and fractures are close enough that the medium behaves as a homogenous medium. For higher contrasts, the matrix will not be conductive enough to be a part of the overall flow process in the reservoir, so it behaves as a source of fluid to fractures. For very high contrasts, the matrix is almost not conductive, consequently, the overall flow will be governed by the flow in the fracture network. Other intermediate cases may also occur.
The macroscopic pressure behavior of multi-scale fractured media is investigated using homogenization technique. This approach captures the details of the flow both within and between the porous matrix and fracture. A general equivalent macroscopic model is proposed. The equivalent porosity and the equivalent permeability of the averaged medium are derived. The memory effect in the medium is included in the averaged model through an integro-differential term. The memory effect represents the difference between the response time of matrix and fracture to the same pressure drop. Depending on the ratio of the fracture to matrix diffusivity, the fracture to matrix volume ratio, and the number of scales of nonhomogeneity in the medium, several memory terms are obtained. The impact of the presence of additional scale of the fracture network, such as secondary and tertiary fractures on the equivalent model, and pressure transient behavior is highlighted. The macroscale model for an arbitrary number of scales and its limit for the infinite hierarchy is obtained. The kernel of the memory operator is the solution of a nonlinear integro-differential equation. Pressure transient behavior of the multi-scale model is compared to classic double-porosity model.
Bio: Dr. Mojdeh Rasoulzadeh is an assistant professor in the Department of Mathematics, adjunct faculty in the Department of Mechanical Engineering, and affiliated to the Center for Complex Hydro Systems at The University of Alabama, Tuscaloosa. She received her Ph.D. in Mechanics and Energetics from Lorraine University in France. Before joining the University of Alabama, she was a research engineer and postdoc in the oil and gas industry, Schlumberger and Total France, working on flow and geomechanical investigation of highly heterogeneous formations. She has about ten years of experience in the research focused on obtaining effective models of flow in highly heterogeneous reservoirs such as multiscale fractured and vuggy carbonates. She obtained the closed-form of transient flow behavior and transfer functions for multiscale fractured porous media as an integro-differential equation averaged over all the scales of heterogeneities. Dr. Rasoulzadeh has obtained novel models to investigate the role of meso-scale inhomogeneities such as vugs and cavities on the overall flow and geochemical behavior of the subsurface reservoirs. She is currently conducting research on coupled Hydro-Mechanical-Chemical processes and particulate flow in carbonate reservoirs and karst formations.
Dr. Derek Moulton
Mathematical Institute University of Oxford
Title: Morphorods: A Modelling Framework for Growing Slender Structures
Abstract: Filamentary structures are ubiquitous in nature, and can be found at all scales, from microscopic chains of molecules to vines and elephant trunks to braided magnetic flux tubes in solar flares. A key feature prevalent in biological filaments is growth, a critical element underlying pattern formation and also utilized to generate movement or provide mechanical support. And defined more broadly as a change in reference shape or size, growth is a common and important feature in non-biological materials as well. Due to their inherent slenderness, the mechanical behaviour of growing filaments is well-characterised by a one-dimensional continuum representation. In this talk I will outline a modelling framework for describing the mechanical behavior of growing slender elastic structures, which we term morphoelastic rods, or simply morphorods. I will demonstrate the utility of the theory via a number of diverse applications, from pattern formation in seashells to the fascinating tropic growth of plants.
Bio: Dr. Derek Moulton is an Associate Professor in the Mathematical Institute at the University of Oxford. He received his PhD in Mathematical Sciences from the University of Delaware in 2008. He was a postdoc at the University of Arizona and then at Oxford, before taking his current faculty position in 2013. His research focuses on the development and analysis of physics-based mathematical models, using continuum mechanics to understand problems in morphogenesis, growth, pattern formation, physiology, biomimetics, and biomedical devices.
Dr. Abba B. Gumel
Foundation Professor of Mathematics
School of Mathematical and Statistical Sciences & Barrett Honors College Faculty
Arizona State University
Title: Mathematics of the Dynamics and Control of the COVID-19 Pandemic
Abstract: The novel coronavirus that emerged in December of 2019 (COVID-19), which started as an outbreak of pneumonia of unknown cause in the city of Wuhan, has become the most important public health and socio-economic challenge humans have faced since the 1918 H1N1 influenza pandemic. Within weeks of its emergence, COVID-19 spread to every part of the world, accounting for over 103 million confirmed cases and 2.2 million deaths (as of February 1, 2021), in addition to incurring severe economic burden, social disruptions and other human stresses, globally. In this talk, I will discuss our work on the mathematical modeling and analysis of the spread and control of COVID-19, with emphasis on the assessment of the population-level impact of the three currently-available anti-COVID vaccines (namely, the Pfizer-BioNTech, Moderna and Oxford-AstraZeneca vaccines). Specifically, we will explore conditions for the elimination of the pandemic using the vaccines (vis a vis achieving vaccine-derived herd immunity) and/or their combination with other nonpharmaceutical interventions, such as face masks usage and social-distancing.
Bio: Dr. Abba Gumel is a Foundation Professor of Mathematics at the School of Mathematical and Statistical Sciences, Arizona State University. He uses mathematical modeling approaches and analysis to study the transmission dynamics of emerging and re-emerging diseases of public health importance.
Dr. Erica J. Graham
Assistant Professor of Mathematics
Bryn Mawr College
Title: Discovering Reproductive Phenotypes: a Comprehensive Approach
Abstract:A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The complex endocrine system creates a challenge for identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. In this talk, I will discuss a new endocrine model that limits model complexity and an algorithm for comprehensive model analysis through Monte Carlo and statistical methods. I will also discuss how this approach can be used to identify mechanisms that differentiate regular and irregular phenotypes.
Bio: Dr. Erica J. Graham is an assistant professor of mathematics at Bryn Mawr College. She holds a bachelor’s degree in mathematics from Bryn Mawr College and master’s and doctoral degrees in mathematics from the University of Utah. Her research in mathematical biology focuses on applications to endocrinology and physiology. Her particular research interests include cellular mechanisms of type 2 diabetes progression, anticoagulant (blood thinner) effectiveness, immune-mediated mechanisms of blood clotting, and reproductive hormone regulation as related to ovulatory dysfunction. Professor Graham is a co-founder of the Mathematically Gifted & Black website, which in 2021 partnered with SIAM to create a new early-career fellowship for historically excluded groups. She is committed to redefining mathematical and academic spaces with a vision toward equity, inclusion and anti-racism.
Dr. David Hu
Professor of Mechanical Engineering and Biology Adjunct
Professor of Physics
Georgia Institute of Technology
Title: Elephant Olfaction and a Wombat's Cubic Feces
Abstract: Elephants eat 200 kg of food per day, equating to 200 grams every minute. To locate food quickly, they have more olfactory genes than any other animal, and they also employ periodic sniffs with their trunk. In this talk, I'll show how the frequency of sniffing changes with body size. We will apply the fluid mechanics of cardiovascular flows to calculate the time for odors to diffuse to the elephant's receptors. We visualize flows using experiments with GROMIT, a bellows-driven device that inhales air at the sniffing frequencies of animals, and was awarded third place in a cheese-sniffing machine olfaction competition in Montreal. Lastly, we will present our experiments and modeling of a wombat's cubic feces. The flat faces are created through a drying process analogous to the formation of columnar joints in Giant's Causeway, Ireland; the corners are sculpted by non-uniform material properties in the intestinal wall. Audiences will learn how to use apply mathematical principles to study natural phenomena.
Bio: Dr. David Hu is a mechanical engineer who studies the interactions of animals with water. His team has discovered how dogs shake dry, how insects walk on water, and how eyelashes protect the eyes from drying. Originally from Rockville, Maryland, he earned degrees in mathematics and mechanical engineering from M.I.T., and is currently Professor of Mechanical Engineering and Biology and Adjunct Professor of Physics at Georgia Tech. He is a recipient of the National Science Foundation CAREER award for young scientists, the Ig Nobel Prize in Physics, and the Pineapple Science Prize (the Ig Nobel of China). He serves on the editorial board of Nature Scientific Reports, PLoS One, and The Journal of Experimental Biology. His work has been featured in The Economist, The New York Times, Saturday Night Live, and Highlights for Children. He is the author of the book "How to Walk on Water and Climb Up Walls: Animal Motion and the Robots of the Future" published by Princeton University Press. He lives with his wife and two children in Atlanta, Georgia. His profile is in the New York Times: https://www.nytimes.com/2018/11/05/science/hu-robotics.html
Dr. Robert Stewart
Senior Scientist, GeoAI Group Oak Ridge National Laboratory and Joint Faculty, Geography
University of Tennessee
Title: Bayesian Occupancy Estimation in Heterogenous and Uncertain Data Environments
Abstract: Understanding how humans occupy the built environment is critical to a wide array of applications, including urban resiliency, natural hazards loss analytics, energy efficiency, transportation, and population distribution modeling. Globally, efforts to understand building occupancy continue to rely on a patchwork of disparate resources that vary widely in availability and sophistication. To use these effectively still requires expert judgment to harmonize the data and produce meaningful occupancy estimates. Despite the extraordinary societal impacts of big data, including IoT, social media, cell tracking, webcams, imagery exploitation, and a wide array of open-source data, the challenge of estimating population is still not a “solved problem”. The problem largely remains an open interdisciplinary challenge requiring the qualitative and quantitative contributions of social scientists, population scientists, remote sensing practitioners, computer scientists, and data scientists. Against this backdrop, there is a unique opportunity to leverage the qualities of Bayesian reasoning to explicitly harmonize disparate data, capture process uncertainty, and intuitively convey occupancy estimates to a wide range of consumers. In this talk, I present a Bayesian model that: 1) provides an explicit, systematic approach to engaging data, data harmonization techniques, and expert judgment in the production of occupancy estimation, 2) probabilistically evolves and refines estimates over time as new data and expertise emerge, and 3) retains and characterizes uncertainty emerging from expert judgment, data, and inference. I also provide insight for working and interning at Oak Ridge National Laboratory (DOE's largest science and energy laboratory).
Bio: Dr. Robert Stewart is a senior scientist in the GeoAI group at the Oak Ridge National Laboratory (ORNL) and adjunct associate professor of Geography at the University of Tennessee. He leads projects engaged in a wide array of R&D including machine learning, spatio-temporal analytics, data mining, big data workflows, simulation, visualization, and tool development. His work is informed by and applied to a wide range of use cases emerging from population dynamics, maritime safety, geomatics, urban dynamics, security, energy-water nexus, health, environmental risk and many others. His own research is focused on applied mathematical, statistical, and computational methods in the areas of spatio-temporal analytics, probability modeling, and uncertainty quantification with an emphasis on risk and decision support. As a faculty member at UT, Dr. Stewart engages graduate students in geography, mathematics, and the Bredesen Center Data Science Ph.D. program. He regularly serves on thesis committees, advises students, and facilitates internships at ORNL.
Dr. Emma Pierson
Senior Researcher, Microsoft Research
Incoming Assistant Professor of Computer Science
Title: Data Science for Social Equality
Abstract: Our society remains profoundly unequal. This talk presents several vignettes about how data science and machine learning can be used to reduce inequality in healthcare and public health, focusing on applications in women's health, COVID-19, policing, and pain.
Bio: Dr. Emma Pierson is a senior researcher at Microsoft Research and an incoming assistant professor of computer science at Cornell Tech. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, and Forbes 30 Under 30 in Science. She has written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.
Dr. Smita Krishnaswamy
Department of Genetics, Yale School of Medicine Department of Computer Science,
Yale School of Applied Science and Engineering Yale University
Title: Geometric and Topological Approaches to Representation Learning in Biomedical Data
Abstract: High-throughput, high-dimensional data has become ubiquitous in the biomedical, health and social sciences as a result of breakthroughs in measurement technologies and data collection. While these large datasets containing millions of observations of cells, peoples, or brain voxels hold great potential for understanding generative state space of the data, as well as drivers of differentiation, disease and progression, they also pose new challenges in terms of noise, missing data, measurement artifacts, and the so-called “curse of dimensionality.” In this talk, I will cover data geometric and topological approaches to understanding the shape and structure of the data. First, we show how diffusion geometry and deep learning can be used to obtain useful representations of the data that enable denoising (MAGIC), dimensionality reduction (PHATE), and factor analysis (Archetypal Analysis Network) of the data. Next we will show how to learn dynamics from static snapshot data by using a manifold-regularized neural ODE-based optimal transport (TrajectoryNet). Finally, we cover a novel approach to combine diffusion geometry with topology to extract multi-granular features from the data (Diffusion Condensation and Multiscale PHATE) to assist in differential and predictive analysis. On the flip side, we also create a manifold geometry from topological descriptors, and show its applications to neuroscience. Together, we will show a complete framework for exploratory and unsupervised analysis of big biomedical data.
Bio: Dr. Smita Krishnaswamy is an Associate professor in Genetics and Computer Science. She is affiliated with the applied math program, computational biology program, Yale Center for Biomedical Data Science and Yale Cancer Center. Her lab works on the development of machine learning techniques to analyze high dimensional high throughput biomedical data. Her focus is on unsupervised machine learning methods, specifically manifold learning and deep learning techniques for detecting structure and patterns in data. She has developed algorithms for non-linear dimensionality reduction and visualization, learning data geometry, denoising, imputation, inference of multi-granular structure, and inference of feature networks from big data. Her group has applied these techniques to many data types such as single cell RNA-sequencing, mass cytometry, electronic health record, and connectomic data from a variety of systems. Specific application areas include immunology, immunotherapy, cancer, neuroscience, developmental biology and health outcomes. Smita has a Ph.D. in Computer Science and Engineering from the University of Michigan.
Dr. Alexandra Jonko
Staff Scientist, ATEAM Team Leader Computational Earth Science Group
Los Alamos National Laboratory
Title: Capturing the Sensitivity of Wildfire Spread to Small Perturbations in Atmospheric Conditions Using a Computational Fluid Dynamics Model of Wildfire Behavior
Abstract: Atmospheric forcing and interactions between fire and atmosphere are primary drivers of wildland fire behavior. The atmosphere is known to be a chaotic system which, although deterministic, is very sensitive to small perturbations to initial conditions. We assume that as a result of the tight coupling between fire and atmosphere, wildland fire behavior, in turn, should also be sensitive to small perturbations in atmospheric initial conditions. Observations from experimental burns suggest that low-intensity fire in particular is susceptible to small perturbations in the wind field, which can significantly alter fire spread. Here we employ a computational fluid dynamics model of coupled fire-atmosphere interactions to answer the question: How sensitive is fire behavior to small variations in atmospheric turbulence? We perform ensemble simulations of fires in homogenous grass fuels. The only difference between ensemble members is the state of the turbulent atmosphere provided to the model throughout the simulation. We find a wide range of outcomes, with area burned ranging from 2212 m2 to 11236 m2 (>400% change), driven by sensitivity to variability in both initial and boundary atmospheric conditions during the initial 30 seconds of simulation. Our results highlight the need for ensemble simulations, especially when considering fire behavior in marginal burning conditions, such as during prescribed fire application.
Bio: Dr. Alex Jonko is an atmospheric scientist interested in modeling wildland fire behavior and fire-climate interactions. Alex is a staff member in the Computational Earth Science Group at Los Alamos National Laboratory. She has a B.S/M.S. equivalent degree in Meteorology from the University of Bonn, Germany, and a Ph.D. in Atmospheric Science from Oregon State University. In her free time, she enjoys exploring the outdoors around Los Alamos on foot, bike, and skis, fermenting vegetables and baking sourdough bread.
Dr. Lai-Sang Young
Henry Lucy Moses
Professor of Science
Professor of Mathematics and Neural Science Courant
Institute of Mathematical Sciences New York University
Title: A Dynamical Model of the Visual Cortex
Abstract: In the past several years, I have been involved in building a biologically realistic model of the monkey visual cortex. Work on the input layer of the primary visual cortex (V1) is now nearly complete, and I would like to share some of that with you. I will divide by time between the following two topics: (1) Local circuits, the dynamics of which I will describe in some detail, including an emergent rhythm detected all over the brain. (2) The wiring that confers upon the visual cortex the ability to identify contours, following the well-known theory of Hubel and Wiesel. I will present a large-scale mechanistic model that incorporates the ideas discussed, and show simulations of the visual cortex computing in real time, producing activity maps that are analogous to fMRI but on the neuronal level, with arbitrarily high spatio-temporal resolutions.
Bio: Dr. Lai-Sang Young is a Professor of Mathematics at the Courant Institute and Henry and Lucy Moses Professor of Science at NYU. She is currently also a Distinguished Visiting Professor at the Institute for Advanced Study, Princeton, holding a joint appointment between the School of Mathematics and the School of Natural Sciences. Young started her career in pure Mathematics, with a specialization in Dynamical Systems. She is best known for her work on the theory of chaos. In the last 20 years she has expanded her research to include applications of dynamical systems ideas to Mathematical Physics and to Computational Neuroscience. Young has delivered plenary lectures in the International Congress of Mathematicians and the International Congress on Mathematical Physics. She is a member of the National Academy of Sciences and a member of the American Academy of Arts and Sciences.
Dr. Jenny Suckale
Geophysics Member, Institute for Computational and Mathematical Engineering (ICME) Center Fellow,
Stanford Woods Institute for the Environment Stanford University
Title: The Causes and Consequences of the Large Uncertainty in Near-Term Sea-Level Rise
Map of the remotely sensed surface speed of ice based on data from Eric Rignot, NASA Jet Propulsion Laboratory that changed the way we think about ice-sheet dynamics. Figure credit: Cooper Elsworth
Abstract: The Antarctic Ice Sheet exhibits astonishing spatial and temporal variability in ice flow rate and associated mass loss. Rapid ice flow is concentrated in narrow corridors called ice streams that together with outlet glaciers account for the majority of the current mass loss from the ice-sheet interior to the ocean. The response of the ice streams to climatically induced perturbations is highly consequential for future projections of sea level, but our understanding of the physical processes governing ice-stream dynamics is limited. This limitation is particularly concerning in light of observations indicating that in the past, the position, width, and flow speed of ice streams have varied notably on decadal to centennial time-scales. In this talk, I will present a sequence of mathematical models that shed light on the physical processes that control potential adjustments of ice streams in the near future. Our analysis suggests that existing data and models are too incomplete to reliably assess ice loss from the two ice sheets under changing climatic conditions. However, I argue that the uncertainty itself is a valuable scientific contribution for informing climate adaptation planning and briefly lay out an avenue for making progress on science-based, equitable adaptation in the San Francisco Bay Area.
Bio: Dr. Jenny Suckale is an Assistant Professor in Department of Geophysics at Stanford University. She is co-appointed at the Institute of Computational and Mathematical Engineering, the Department of Environmental Engineering, and the Woods Institute for the Environment. Before joining Stanford, she was a Lecturer in Applied Mathematics and a Ziff Environmental Fellow at Harvard. She holds a PhD from the Massachusetts Institute of Technology and an MPA from the Harvard Kennedy School. The goal of her research is to understand the processes that govern extreme events in different natural systems and leverage this understanding to increase resilience. She pursues this goal by developing mathematical methods that are tested against observational data from a broad spectrum of scales. Applications include volcanic eruptions, ice-sheet instability, permafrost disintegration, coastal flooding, and induced earthquakes. She was recently awarded the Presidential Early Career Awards for Scientists and Engineers, the highest honor bestowed by the United States Government on science and engineering professionals in the early stages of their independent research careers.
Dr. J. Nathan Kutz
Robert Bolles and Yasuko Endo Professor, Department of Applied Mathematics
Adjunct Professor of Physics, Mechanical Engineering, and Electrical Engineering University of Washington
Title: Data-Driven Model Discovery and Physics-Informed Learning
Abstract: A major challenge in the study of dynamic systems and boundary value problems is that of model discovery: turning data into reduced order models that are not just predictive, but provide insight into the nature of the underlying system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete measurements. For systems with full state measurements, we show that the recent sparse identification of nonlinear dynamical systems (SINDy) method can discover governing equations with relatively little data and introduce a sampling method that allows SINDy to scale efficiently to problems with multiple time scales, noise and parametric dependencies. For systems with incomplete observations, we show that the Hankel alternative view of Koopman (HAVOK) method, based on time-delay embedding coordinates and the dynamic mode decomposition, can be used to obtain a linear models and Koopman invariant measurement systems that nearly perfectly captures the dynamics of nonlinear systems and boundary value problems. Neural networks are used in targeted ways to aid in the model reduction process. Together, these approaches provide a suite of mathematical strategies for reducing the data required to discover and model nonlinear multiscale systems.
Bio: Dr. Nathan Kutz is the Yasuko Endo and Robert Bolles Professor of Applied Mathematics at the University of Washington, U.S.A. having served as chair of the department from 2007–2015. He received the BS degree in physics and mathematics from the University of Washington in 1990 and the PhD in applied mathematics from Northwestern University in 1994. He was a postdoc in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control.
Past Seminars - Fall 2020
Dr. Samir Bhatt
Senior Lecturer (Associate Professor) Faculty of Medicine School of Public Health
Imperial College London
Title: Comparing the Responses of the UK, Sweden and Denmark to COVID-19 Using Counterfactual Modelling
Abstract: The UK and Sweden are among the top five worst affected European countries ranked by total per-capita mortality due to COVID-19. Sweden stands out among European countries for its greater reliance on voluntary rather than mandatory control measures. Here we explore how the timing and effectiveness of the different policies adopted on COVID-19 transmission in the UK, Sweden and Denmark shaped the mortality seen in each country, using a counterfactual assessment of what might have been the impact of each of those countries adopting the others' policies.
Bio: Dr. Samir Bhatt is an Associate Professor at Imperial College London. His interests lie in the development and application of inferential mathematical models to address policy-relevant questions about infectious diseases. His main areas of research concern Malaria and HIV. Methodologically, he is interested in spatial statistics, Gaussian processes, kernel machines and machine learning.
Dr. Loni Tabb
Associate Professor Epidemiology and Biostatistics Dornslife School of Public Health
Title: Assessing the Cases and Deaths Attributed to COVID-19 and its Impact on Racial/Ethnic Inequities at the County Level: Does the Coronavirus Discriminate?
Abstract: On January 20, 2020, the first confirmed case of the novel coronavirus (COVID-19) was identified in the United States (US). As of July 27, 2020, there were over 4 million confirmed cases and over 145,000 related deaths. Months into this global pandemic, many communities, especially communities of color, were disproportionately impacted by this virus. The goal of this research is to describe the geographic patterning of coronavirus cases and deaths in the US, and to estimate the associated racial/ethnic inequities.
Both exploratory and inferential spatial data analysis methods were utilized to further assess these potential racial/ethnic inequities. Descriptive statistics and choropleth maps were used to graphically display and measure the patterning of coronavirus cases and deaths across all US counties (N = 3139) with and without counties in New York (N = 62), as a special case. We then fit spatially varying zero inflated negative binomial regression models to estimate racial/ethnic inequities in coronavirus cases and deaths. These models were fit under a Bayesian statistical framework and relied on integrated nested Laplace approximation methods to obtain estimates and 95% credible intervals of the racial/ethnic inequities present.
Choropleth maps of US counties and New York counties showed significant clusters of coronavirus cases and deaths. Counties with higher percentages of Black residents in both the US and New York alone had 56% and 41%, respectively, more coronavirus cases. In New York, counties with more Hispanic residents had a nearly 3-fold increased risk of coronavirus cases. Densely populated counties in New York had significantly more deaths than less densely populated counties.
Our findings highlight the disproportionate burden of COVID-19 on communities of color, particularly in counties with increasing percentages of Blacks and Hispanics. Given this evidence, local, state, and national policymakers can further identify which counties and populations will need additional access to resources that include, but are not limited to, testing, treatment, education, and support in the recommended guidelines for social distancing practices.
Bio: Dr. Loni Philip Tabb is an Associate Professor of Biostatistics in the Department of Epidemiology and Biostatistics at Drexel University’s Dornsife School of Public Health in Philadelphia, PA. She received her PhD in Biostatistics from Harvard University in 2010 where she developed novel statistical methods to address zero inflation in longitudinal count data – with applications to environmental health and health disparities research. More specifically, she developed a marginalized zero-altered Poisson model to map and measure premature mortality and the effect of census tract poverty in the greater Boston area. Upon completion of this doctoral training, she returned as a tenure-track faculty member to her undergraduate and graduate alma mater – she obtained her B.S. (2003) and M.S. (2005) in Mathematics from Drexel.
Since her arrival at Drexel University, she has collaborated as a Co-Investigator on several National Institutes of Health, National Science Foundation, Annie E. Casey Foundation, and Sidney Kimmel Cancer Center funded projects. These projects range from extending her doctoral work with zero inflation in genome sequencing data to her work on examining the spatial distribution of alcohol outlets in Philadelphia. In 2013, she was awarded as the Principal Investigator (PI) of a Robert Wood Johnson Foundation New Connections grant for Junior Investigators, entitled “Examining the impact on alcohol-related violence of increased liquor outlets under privatization of sales”.
Most recently, Dr. Tabb has used spatial statistics and spatial epidemiology methods in the area of cardiovascular disease, with a focus on assessing the spatial patterning of cardiovascular health here in the US between blacks and whites. She was awarded a K01 Career Development Award (2017) from the National Heart, Lung, and Blood Institute, entitled “Assessing the spatial heterogeneity in cardiovascular risk factors within and between blacks and whites”. As the PI of this grant, Dr. Tabb has mapped and measured the varying disparities locally in major cities as well as nationally, with hopes of providing this evidence to inform policy makers, health officials, and the communities affected in improving cardiovascular health in this country.
Dr. Tabb has also taught several courses at Drexel, which include Biostatistics, Survival Data Analysis, Advanced Statistical Computing, and Bayesian Data Analysis. In addition to her in-classroom instruction, Dr. Tabb also mentors significantly, both formally and informally. Her goal with mentoring is to “pay-it-forward” and to help connect mentees and mentors alike.
Dr. Tabb is an active member of several biostatistics and public health professional societies – including current co-chair of the Fostering Diversity in Biostatistics Workshop for the Eastern North American Region of the International Biometric Society Annual Spring Meetings.
Sir Michael Brady FRS FREng FMedSci HonFIET FInstP FBCS PhD
Professor of Oncological Imaging Department of Oncology
University of Oxford
Title: Quantitative Imaging of the Liver and Other Organs
Abstract: There is a World-wide pandemic in liver and metabolic diseases, driven in the West by obesity and in Asia (primarily) by the prevalence of hepatitis B. MRI potentially offers a way to assess organs such as the liver, kidneys, and pancreas; however, MRI is currently qualitative, in the sense that the brightness values in a typical scan have little or no intrinsic meaning. Hence, the interpretation of an MRI relies upon clinician judgment; but, the inter- and intra-rater variation is typically 35%.
In a University of Oxford start-up, Perspectum https://perspectum.com/, we have used mathematical modelling to determine the amount of fat at each voxel of the liver and the amount of iron, and we measure the degree of fibroinflammation. The resulting FDA-cleared product LiverMultiscan is the basis for many pharmaceutical trials (from the San Francisco office) and for a clinical service (from the Dallas office). Further, we have developed methods to extract and measure the biliary tree in a liver cancer resection decision support tool, Hepatica. More recently, we have extended LiverMultiscan to a product, Atlas, that can assess organ damage following COVID-19.
Bio: Professor Sir Michael Brady is currently Professor in Oncological Imaging in the Department of Oncology at the University of Oxford, having recently retired as Professorship in Information Engineering (1985-2010). Prior to Oxford, he was Senior Research Scientist in the Artificial Intelligence Laboratory at MIT, where he was one of the founders of the Robotics Laboratory. Mike is the author of over 400 articles and 35 patents in computer vision, robotics, medical image analysis, and artificial intelligence, and the author or editor of ten books, including: Robot Motion (MIT Press 1984), Robotics Science (MIT Press 1989), Robotics Research (MIT Press 1984), Mammographic Image Analysis (Kluwer, January 1999) and Images and Artefacts of the Ancient World (British Academy, 2005) and the International Workshop on Digital Mammography (Springer 2006). He was Editor of the Artificial Intelligence Journal (1987-2002), and founding Editor of the International Journal of Robotics Research (1981-2000). Mike is co-Director of the Oxford Cancer Imaging Centre, one of four national cancer imaging centres in the UK.
Mike has been elected a Fellow of the Royal Society, Fellow of the Royal Academy of Engineering, Membre Associé Etranger of the Académie des Sciences, Honorary Fellow of the Institution of Engineering and Technology, Fellow of the Institute of Physics, Fellow of the Academy of Medical Sciences, and Fellow of the British Computer Society. He was awarded the IEE Faraday Medal for 2000, the IEEE Third Millennium Medal for the UK, the Henry Dale Prize (for “outstanding work on a biological topic by means of an original multidisciplinary approach”) by the Royal Institution in 2005, and the Whittle Medal by the Royal Academy of Engineering 2010. Mike was knighted in the New Year’s honours list for 2003. He has been awarded honorary doctorates by the universities of Essex, Manchester, Liverpool, Southampton, Oxford Brookes, York, and Paul Sabatier (Toulouse, France), and has been appointed an Honorary Professor at the Chongqing University of Posts and Telecommunications and Chongsha's South China University. He was named Alumnus of the Year in 2019 by the Australian National University and awarded an honorary doctorate in 2020. Mike was Chairman of the publications board of the Royal Society 2010-2016.
Mike has a continuing strong commitment to commercialisation of science, in particular to entrepreneurial activity. He was a non-executive director and Deputy Chairman 1994-September 2014 of the FTSE 250 company Oxford Instruments plc (http://www.oxinst.com/), and from 1994-2004 a non-executive director of AEA Technology plc. He resigned from the latter position because one of his companies, Mirada Solutions Ltd, was acquired in 2003 by CTI Molecular Imaging, a NASDAQ quoted company, and he was invited to serve on the board of CTI Mirada. Mike is a founding Director of the start-up companies: Volpara Healthcare Technologies (http://volparasolutions.com/) mammographic image analysis; Perspectum Diagnostics liver image analysis by MRI (http://perspectum-diagnostics.com/ ); and Mirada Medical Limited (http://www.mirada-medical.com/) which develops medical image analysis software (installed in almost 6000 hospitals worldwide). He was also Founder Chairman of Guidance (http://www.guidance.eu.com/) which sold its Monitoring Division (offender tagging) to G4S in 2011, its Industrial Division in 2016, and the remaining Marine Division in 2017. Most recently he has co-founded ScreenPoint bv (http://www.screenpoint-medical.com/), which provides decision support for breast cancer (2D/3D mammography, MRI) and Optellum (http://www.optellum.com/), which analyses lung CT images.
Dr. Linda Cummings
Professor Complex Flows and Soft Matter Group
Department of Mathematical Sciences
New Jersey Institute of Technology
Title: The Mathematical Problems in Industry (MPI) Workshop and the Modeling It Inspires
Abstract: The Mathematical Problems in Industry (MPI) workshop is an annual week-long interactive workshop, normally held in the North-Eastern USA. Participants from industry present real problems facing their industries; academic participants then break into groups to brainstorm, formulate and solve mathematical models and, at the end of the week, present the industry participants with summaries and recommendations. I will give an overview of MPI and the many similar events that are held across the globe, and discuss a few of the wide range of industrial problems that have been considered over the years. I will conclude with a more in-depth look at the problem of modeling flow and fouling in membrane filters, which was first brought to MPI in 2013 and has made many reappearances since.
Bio: Dr. Linda Cummings is Professor of Mathematics and Associate Dean for Research and Graduate Education in the College of Science and Liberal Arts at the New Jersey Institute of Technology (NJIT). She earned her Bachelor's and Doctoral degrees in Mathematics at St. Catherine's College, University of Oxford, and held postdoctoral positions at the Technion (Israel) and the Laboratoire de Physique Statistique de l'Ecole Normale Superieure, Paris (France) prior to faculty positions at the University of Nottingham, then NJIT. Her research lies in mathematical modeling of real-world physical systems with a particular focus on fluid dynamics. As a co-organizer of the Mathematical Problems in Industry (MPI) workshops since 2009, she has a special interest in industrial mathematics. Current research projects include mathematical modeling of filtration, and modeling flow and instabilities of nematic liquid crystal films, both funded by the National Science Foundation.
Dr. Morgan Craig
Assistant Professor Department of Mathematics and Statistics
Université de Montréal Researcher, Research Centre of the Sainte-Justine University Hospital
Title: Understanding the Orchestration of Immune Responses Through Quantitative Approaches
Abstract: An efficient and effective immune system is critical to good health. For this, both local and long-distance signalling are necessary for communication amongst cells. Cytokines are small proteins expressed by blood cells and other key organs that act to up- or down-regulate key processes within the immune system. The sheer number of cell/cytokine interactions complicates our ability to understand, at a broad scale, the totality of relationships within the immune system, and the pathophysiology of acute and chronic immune disorders. A central challenge is translating observational understanding (patient symptoms, measurements of biomarkers etc.) to the mechanistic and causal. To begin to unravel the complexity of immune responses, we applied a collection of novel quantitative techniques and models to a variety of diseases, including a rare blood disorder called cyclic thrombocytopenia and COVID-19. Our results help to rectify the transmission of signals in the immune system both cell-to-cell and distally, refining our understanding of how immune responses are mounted. This is helpful pre-clinically and clinically for designing improved therapies and novel diagnostic tools, and establishing effective therapeutic schedules to help treat disease.
Bio: Dr. Morgan Craig is a Researcher at the Research Centre of the Sainte-Justine University Hospital Centre and an Assistant Professor in the Department of Mathematics and Statistics at the University of Montréal. She received her Ph.D. in Pharmaceutical Sciences from the University of Montréal and was recruited from the Department of Organismic and Evolutionary Biology at Harvard University where she did her postdoc. Her Quantitative and Translational Medicine Laboratory focuses on the application and implementation of quantitative approaches, particularly computational biology, to study biologically-relevant questions of large medical importance, particularly the optimization of treatment strategies for a variety of diseases. Current projects include understanding pre-leukemic hematopoietic stem cell dynamics, PBPK/PD models of antiretroviral drugs to support the design of a novel sustained-release delivery device for improved HIV treatment design and HIV cure strategies, unravelling immunological networks during rare diseases, and quantifying the impact of heterogeneity in glioblastoma, melanoma, and non-small cell lung cancer tumours on resistance pathways and immunotherapeutic success. Dr. Craig’s research is highly multidisciplinary and is conducted in close collaboration with experimentalists and clinicians.
Dr. Adriana Dawes
Associate Professor Department of Mathematics
The Ohio State University
Title: Pushing and pulling: Centrosome positioning in polarized cells
Abstract: Asymmetric cell division, where daughter cells inherit unequal amounts of specific factors, is critical for development and cell fate specification. In polarized cells, where specific factors are segregated to opposite ends of the cell, asymmetric cell division occurs as a result of positioning the centrosomes along the polarity axis. Using an individual-based stochastic model of centrosome-associated microtubule dynamics and experiments in early embryos of the nematode worm C. elegans, we explore potential sources of force generation and demonstrate the role of both cortical and centrosomal asymmetries for recapitulating the in vivo dynamics and proper positioning of the centrosomes prior to first division.
Bio: Dr. Adriana Dawes is an Associate Professor at The Ohio State University, with a joint appointment in the Department of Mathematics and the Department of Molecular Genetics. Dr. Dawes earned her PhD in mathematics from the University of British Columbia, and focused on learning techniques in experimental biology as a postdoctoral fellow at the University of Washington's Center for Cell Dynamics. Dr. Dawes' research tightly weaves experimental and theoretical approaches to better understand how biochemical, mechanical and geometric features interact and regulate each other to give rise to a functional cell. Dr. Dawes is the recipient of an NSF CAREER award, and has received funding from the NIH and private foundations including the Gordon and Betty Moore Foundation.
Dr. Mohamed Samaha
Associate Professor Department of Mechanical and Industrial Engineering
Title: Thin Film Flow Along a Partially Immersed Rotating Cylinder
Abstract: The steady-state withdrawal of a two-dimensional liquid film from a horizontal and partially immersed rotating cylinder in a pool is examined theoretically. As such flows are an essential element of more sophisticated roll-coating operations, its study is warranted. A boundary layer form of the Navier-Stokes equations is coupled with essentially-hydrostatic pressure variations induced by the interface under conditions of negligible capillarity. Following the approach of von-Karman and Polhausen, these simplified equations are integrated to obtain an integro-differential equation; subsequently, an assumed parabolic velocity profile is inserted to obtain an approximate first-order nonlinear-ordinary differential equation (the film equation) that governs the film thickness. A removable critical point singularity (Weinstein & Ruschak 1999, Chem. Eng. Sci. 54 (8)) arises in the film equation at the location where inertia and gravitational effects balance, and removal of this singularity sets the volumetric flow rate and the height of the film as a function of azimuthal location along the cylinder. The interface location can be determined by integrating the equation upstream and downstream starting from the critical point. The azimuthal location of the critical point location is linked to the submerged depth of the roller. Whereas the film equation is designed to enable the film equation to approach a horizontal pool away from the roller, it is unable to do so. This is an unexpected result, as film equations developed using parabolic velocity profiles that describe flows along stationary surfaces meet this horizontal condition (Ruschak 1978 AIChE J. 24 (4)). We find that the deficiency in the film equation is a result of parabolic profile used in its development. In this study, we utilize full numerical solutions of the Navier-Stokes equations to guide the choice of velocity profile that enables an accurate approximate solution of the interface shape along the roller. Interface predictions from the resulting film equation are in good agreement with numerical solutions evaluated at different Reynolds numbers, cylinder radii and static pool height.
Bio: Dr. Mohamed A. Samaha is currently an associate professor and graduate program advisor, Mechanical Engineering, at RIT’s campus in Dubai. He joined RIT-Dubai in 2014 as an assistant professor, then, promoted to the associate rank in 2019. His research focuses on experimental, numerical and theoretical approaches in thermofluids with applications in active and passive flow control for saving energy and promoting convection heat transfer. In addition, his research spans methods of harvesting renewable energy including wind turbines and solar panels. Mohamed also worked in advancing relatively low-cost micro/nanofabrication of slippery superhydrophobic and omniphobic surfaces for drag-reduction purposes. He also contributed to other areas such as turbulence modeling of the flow through hydraulic capsule pipelines.
Dr. Samaha is collaborating with a team at RIT-Dubai on research projects funded from the United Arab Emirates government and industry such as Emirates Global Aluminium (EGA), Dubai Silicon Oasis Authority (DSOA) and Smart4Power company. The projects include: (1) passive natural convection enhancement around a horizontal cylinder using a novel shroud–chimney configuration (SCC) with applications in industrial systems including heat exchangers, boilers and electronics cooling systems; (2) development of energy storage systems using phase-change materials; (3) characterization of the accumulated dust layer on solar panels; and (4) design optimization of high capacity ground-coupled heat exchanger. Starting in January 2020, Dr. Samaha is collaborating with a research group from the RIT main campus at Rochester and co-advising a Ph.D. student, Nastaran Naghshineh, with her main advisor, Professor Steven J. Weinstein, the chair of the Department of Chemical Engineering at RIT. They are advancing a theoretical model to simulate the configuration of a thin viscous flow around a rotating partially immersed cylinder in a pool to form a stable roller coating. Also, Professor Brian T. Helenbrook, the chair of the Department of Mechanical and Aeronautical Engineering, Clarkson University, Potsdam, NY is collaborating and part of the research team.
Prior to joining RIT-Dubai, Dr. Samaha was a postdoctoral research associate for two years (2012 - 2014) at Princeton University, NJ, working on the grant of Multidisciplinary University Research Initiative (MURI), Office of Naval Research (ONR) jointly with other groups from Harvard, MIT, Stanford, Johns Hopkins, Michigan, Minnesota, and others. They were advancing a new slippery omniphobic coating for drag reduction purposes. During his doctoral thesis (2009 - 2012) at Virginia Commonwealth University (VCU), VA, he was working in the area of experimental and computational characterization of superhydrophobic slippery surfaces fabricated using AC-electrospinning and random particle deposition, funded from the Defense Advanced Research Projects Agency (DARPA). During his master thesis (2002 - 2007) at Alexandria University, Egypt, he has advanced CFD turbulence models to simulate hydraulic capsule pipeline flow. He received Dr. Essam A. Salem’s award for the best Master of Science in Fluid Mechanics, Alexandria University, 2007. From Alexandria University, he also received his B.Sc. in Mechanical Engineering in 2002 with a cumulative grade of Distinction with the grade of Honor (summa cum laude).
Over the course of his career, Dr. Samaha has been awarded twelve academic honors and professional recognition that demonstrate excellence in education, teaching, research, thesis and publications. So far, Mohamed has published 19 journal articles, one more submitted, one more in preparation and 24 conference papers and abstracts. His articles have been published in very prestigious journals including Physics of Fluids, Langmuir, Journal of Colloid and Interface Science, Review of Scientific Instruments, Measurement Science and Technology, Comptes Rendus de l'Académie des Sciences: Mécanique, the ASME Journal of Fluids Engineering, the AIAA Journal of Thermophysics and Heat Transfer and others. Mohamed is a member of the American Physical Society (APS) and the advisor of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Student chapter at RIT-Dubai. Dr. Samaha taught variety of graduate and undergraduate classes at RIT-Dubai, Virginia Commonwealth University and Alexandria University including Computational Fluid Dynamics, Sustainable Energy Management, Aerodynamics, Turbomachinery, Heat Transfer, Fluid mechanics, Thermodynamics, Engineering Measurements Lab.
Dr. Ted Galanthay
Associate Professor Department of Mathematics
Title: Evolutionary games we play: Hawks, Doves, and More
Abstract: In the 1960's, ecologists began to use game theory to study evolutionary questions on topics such as animal aggression, the sex ratio, and altruism. Further study led to the genesis of evolutionary game theory which seeks to describe changes in the frequency of strategies over repeated iterations of a game. In this talk, I will introduce evolutionary game theory and describe recent mathematical modeling efforts to integrate population dynamics and evolutionary game theory models to answer questions about the evolution of animal aggression.
Bio: Dr. Ted Galanthay is an Associate Professor of Mathematics at Ithaca College where he has been teaching since receiving his doctorate in Applied Mathematics from the University of Colorado-Boulder in 2013. His teaching interests are varied and include introductory statistics, differential equations, linear algebra, and environmental mathematics. Outside the classroom, he has supervised several groups of undergraduate students in the COMAP Mathematical Contest in Modeling, and he is a co-organizer of Ithaca College's annual IC Women in Math Day, a one-day program designed for high school women and their families to attract and retain women in mathematics. In his spare time, he enjoys vegetable gardening, hiking, reading, and spending time with family.
Dr. Lorin Crawford
RGSS Assistant Professor of Biostatistics
Center for Computational Molecular Biology Center for Statistical Sciences
Title: Statistical Frameworks for Mapping 3D Shape Variation onto Genotypic and Phenotypic Variation
Abstract: The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). In this talk, we present SINATRA: a statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our method takes in two classes of shapes and highlights the physical features that best describe the variation between them. The SINATRA pipeline implements four key steps. First, SINATRA summarizes the geometry of 3D shapes (represented as triangular meshes) by a collection of vectors (or curves) that encode changes in their topology. Second, a nonlinear Gaussian process model, with the topological summaries as input, classifies the shapes. Third, an effect size analog and corresponding association metric is computed for each topological feature used in the classification model. These quantities provide evidence that a given topological feature is associated with a particular class. Fourth, the pipeline iteratively maps the topological features back onto the original shapes (in rank order according to their association measures) via a reconstruction algorithm. This highlights the physical (spatial) locations that best explain the variation between the two groups. We use a rigorous simulation framework to assess our approach, which themselves are a novel contribution to 3D image analysis. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.
Bio: Dr. Lorin Crawford is the RGSS Assistant Professor of Biostatistics, and a core faculty member of the Center for Statistical Sciences and Center for Computational Molecular Biology at Brown University. His scientific research interests involve the development of novel and efficient computational methodologies to address complex problems in statistical genetics, cancer pharmacology, and radiomics (e.g., cancer imaging). Dr. Crawford has an extensive background in modeling massive data sets of high-throughput molecular information as it pertains to functional genomics and cellular-based biological processes. His most recent work has earned him a place on Forbes 30 Under 30 list, The Root 100 Most Influential African Americans list, and recognition as an Alfred P. Sloan Research Fellow. Before joining Brown, Dr. Crawford received his PhD from the Department of Statistical Science at Duke University and received his Bachelor of Science degree in Mathematics from Clark Atlanta University.
Dr. Sara Del Valle
Los Alamos National Laboratory
Title: Real-time Data Fusion to Guide Disease Forecasting Models
Abstract: Globalization has created complex problems that can no longer be adequately understood and mitigated using traditional data analysis techniques and data sources. As such, there is a need for the integration of nontraditional data streams and approaches such as social media and machine learning to address these new challenges. In this talk, I will discuss how our team is applying approaches from the weather forecasting community including data collection, assimilating heterogeneous data streams into models, and quantifying uncertainty to forecast infectious diseases. In addition, I will demonstrate that although epidemic forecasting is still in its infancy, it’s a growing field with great potential and mathematical modeling will play a key role in making this happen.
Bio: Dr. Sara Del Valle is a scientist and Deputy Group leader for the Information Systems and Modeling Group at Los Alamos National Laboratory, where she works on the development of mathematical and computational models for infectious diseases. She received her B.S./M.S. in Applied Mathematics from the New Jersey Institute of Technology in 2000/2001 and her Ph.D. in Applied Mathematics and Computational Sciences from the University of Iowa in 2005. Her research focuses on using mathematical and computational models to improve our understanding of human behavior and the spread of infectious diseases. She has developed epidemiological models for many diseases including smallpox, anthrax, HIV, pertussis, MERS-CoV, malaria, dengue, influenza, Ebola, zika, chikungunya, and COVID-19. She has also worked on investigating the role of Internet data streams on monitoring emergent behavior during outbreaks and forecasting infectious diseases. Most recently, her team is investigating the role of large-scale data analytics such as satellite imagery, Internet data, climate, and census data on detecting, monitoring, and forecasting infectious diseases.
Dr. Abdul-Aziz Yakubu
Professor Department of Mathematics
Title: Strong Allee Effect and Basins of Attraction in Discrete - Time Infectious Disease Models
Abstract: Motivated by the Feline Immunodeficiency Virus, the virus that causes AIDS in cat populations, in this talk, we will use discrete-time infectious disease models with demographic strong Allee effect to examine the impact of the fatal susceptible - infected (SI) infections on two different types of density dependent growth functions: Holling type III or modified Beverton-Holt per-capita growth function (compensatory dynamics), and Ricker per-capita growth function with mating (overcompensatory dynamics). The occurrence of the strong Allee effect in the disease-free equation renders the SI population model bistable, where the two coexisting locally asymptotically stable equilibrium points are the origin (catastrophic extinction state) and either another fixed point or an intrinsically generated demographic period k > 1 population cycle. We will use the basic reproduction number, R0, and the spectral radius, λk, to examine the structures of the coexisting attractors. In particular, we will show that the fatal disease is not only capable of enlarging or shrinking the basin of attraction of the catastrophic extinction state, but it can also fracture the basins of attraction into several disjoint sets. Thus, making it difficult to specify the asymptotic SI disease outcome in terms of all initial infections. The complexity of the basins of attractions appear to increase with an increase in the period of the demographic population cycles.
Bio: Dr. Abdul-Aziz Yakubu is a Professor in the Department of Mathematics at Howard University. He has been a faculty member in this department for several years and served as Department Chair from 2004-2014. Yakubu is a leading researcher and expert in mathematical biology. His specific research interests are in mathematical applications to the biological sciences with global applications that include the prevention and control of the spread of infectious diseases, and the sustainability of exploited fisheries. His numerous research publications include papers on analysis and applied dynamical systems. He lectures widely on his research in North America, Africa, Asia, and Europe.
Yakubu has held visiting positions at Cornell University, North Carolina State University, the Ohio State University, and Botswana International University of Science and Technology. He has served and continue to serve as a committee member of several professional mathematics organizations and national mathematics institutes. He was the Chair of the World Outreach Committee of the Society for Mathematical Biology from 2007-2016.
To date, Yakubu has directed 7 PhD dissertations to successful completion (all students who belong to underrepresented groups). He is a proponent of diversity and inclusion in the mathematical sciences and the institutional engagement of Historically Black Colleges and Universities (HBCU) in such initiatives. From 2000–2006, Yakubu directed and taught in REU projects at the Mathematical and Theoretical Biology Institute of Cornell University and Los Alamos National Laboratory. In the Fall of 2015, in collaboration with Dr. Avner Friedman of the Ohio State University and Dr. Michael Reed of Duke University, Yakubu initiated the biannual Howard University Mathematical Modeling in Biology and Medicine Workshop Series.
Title: Harnessing the Blessings of the Statistical and Stochastic Contributions to Mathematical Modelling
Abstract: The quintessential motivation of this talk is to share with my audience what I perceive as a rich and vast array of paradigms or at least methods, concepts and techniques that inherently reside at the interface of deterministic and non-deterministic Mathematical modelling, and that providentially constitute a potent field for the creation and development of far superior and more useful and impactful mathematical models. Borrowing from themes like Gaussian Processes in Statistical Machine Learning, nonhomogeneous Poisson processes in reliability analysis to Bayesian estimation and inference for a wide class of models based on differential equations just to name a few, I intend to kindle the awareness of my audience on the inextricable links among various sub-paradigms of mathematical modelling often mistakenly treated as non-overlapping. A latent (secondary) intention of my talk lies in my hope to contribute to the healing or at least the bridging of the schism or chasm that I perceive among branches of mathematical modelling, hopefully substituting divisiveness with the more noble spirit of collaborative exploration that naturally contains the seed for a technically and methodologically more diverse and more inclusive, and topically far richer mathematical modelling experience for both faculty and their students.
Throughout this talk, I will endeavor to focus on the intuitive appeal of the concepts and ideas, but I will occasionally make use of technical details and derivations wherever needed and will definitely make a lot of epistemological allusions!
Bio: Dr. Ernest Fokoué is a Professor of Statistics with the School of Mathematical Sciences in the College of Science at Rochester Institute of Technology. His areas of research interest include Theoretical Statistics, Statistical Methodology, Bayesian Statistics, Statistical Learning Theory, Data Science, Statistical Machine Learning, Computational Statistics and Statistical Computing. Despite being a bold and zealous statistical evangelist and apologist, he is a mathematical universalist who naturally sees and joyfully embraces the beautiful interconnectedness of all the branches and members of the mystical body of mathematics, forever aware of their undivided and indivisible unity. Epistemology also occupies a place of choice in his array of scholarly interests, along with linguistics and mysticism. He is the current President of the Rochester Chapter of the American Statistical Association and the Founder of the Data Science Research Group.
Dr. Andrea Bertozzi
Distinguished Professor of Mathematics and Mechanical and Aerospace Engineering
Betsy Wood Knapp Chair for Innovation and Creativity
Director of Applied Mathematics University of California Los Angeles
Title: On a Theory for Undercompressive Shocks in Tears of Wine
Abstract: I will revisit the tears of wine problem for thin films in water-ethanol mixtures and present a model for the climbing dynamics. The formulation includes a Marangoni stress balanced by both the normal and tangential components of gravity as well as surface tension which lead to distinctly different behavior. I will review basic theory of shock dynamics in conservation laws and talk about how the tears of wine problem can be modeled by such equations. With bulk surface tension we have a conservation law with a nonconvex flux and higher order diffusion.
Such models can exhibit nonclassical shocks that are undercompressive. We present basic theory that allows one to identify the signature of an undercompressive wave. We observe both compressive and undercompressive waves in new experiments, and we argue that, in the case of a preswirled glass, the famous “wine tears” emerge from a reverse undercompressive shock originating at the meniscus. The talk will include a live demonstration.
Audience members are invited to come to the talk with their own equipment to follow along with home demo. This would include room temperature spirit or higher alcohol wine (e.g. port wine or a full body red wine, or darker colored whisky), a wine/beverage glass (made of glass or crystal, not plastic), a flashlight (could be on your phone) and a cover for the glass.
Bio: Dr. Andrea Bertozzi is an applied mathematician with expertise in nonlinear partial differential equations and fluid dynamics. She also works in the areas of geometric methods for image processing, social science modeling, and swarming/cooperative dynamics. Bertozzi completed all her degrees in Mathematics at Princeton. She was an L. E. Dickson Instructor and NSF Postdoctoral Fellow at the University of Chicago from 1991-1995. She was the Maria Geoppert-Mayer Distinguished Scholar at Argonne National Laboratory from 1995-6. She was on the faculty at Duke University from 1995-2004 first as Associate Professor of Mathematics and then as Professor of Mathematics and Physics. She has served as the Director of the Center for Nonlinear and Complex Systems while at Duke. Bertozzi moved to UCLA in 2003 as a Professor of Mathematics. Since 2005 she has served as Director of Applied Mathematics, overseeing the graduate and undergraduate research training programs at UCLA. In 2012 she was appointed the Betsy Wood Knapp Chair for Innovation and Creativity. Bertozzi's honors include the Sloan Research Fellowship in 1995, the Presidential Early Career Award for Scientists and Engineers in 1996, and SIAM's Kovalevsky Prize in 2009. She was elected to the American Academy of Arts and Sciences in 2010 and to the Fellows of the Society of Industrial and Applied Mathematics (SIAM) in 2010. She became a Fellow of the American Mathematical Society in 2013 and a Fellow of the American Physical Society in 2016. She won a SIAM outstanding paper prize in 2014 with Arjuna Flenner, for her work on geometric graph-based algorithms for machine learning. Bertozzi is a Thomson-Reuters/Clarivate Analytics `highly cited' Researcher in mathematics for both 2015 and 2016, one of about 100 worldwide in her field. She was awarded a Simons Math + X Investigator Award in 2017, joint with UCLA's California NanoSystems Institute (CNSI). Bertozzi was appointed Professor of Mechanical and Aerospace Engineering at UCLA in 2018, in addition to her primary position in the Mathematics Department. In May 2018 Bertozzi was elected to the US National Academy of Sciences. In July 2019 she was awarded SIAM's Kleinman Prize, which recognizes contributions that bridge the gap between high-level mathematics and engineering problems. The award is based on the quality and impact of the mathematics.
Bertozzi has served on the editorial boards of fourteen journals: SIAM Review, SIAM J. Math. Anal., SIAM's Multiscale Modeling and Simulation, Interfaces and Free Boundaries, Applied Mathematics Research Express (Oxford Press), Applied Mathematics Letters, Mathematical Models and Methods in the Applied Sciences (M3AS), Communications in Mathematical Sciences, Nonlinearity, and Advances in Differential Equations, Journal of Nonlinear Science, Journal of Statistical Physics, Nonlinear Analysis Real World Applications; and the J. of the American Mathematical Society.
She served as Chair of the Science Board of the NSF Institute for Computational and Experimental Research in Mathematics at Brown University from 2010-2014 and previously on the board of the Banff International Research Station. She served on the Science Advisory Committee of the Mathematical Sciences Research Institute at Berkeley from 2012-2016.
To date she has graduated 39 PhD students and has mentored over 40 postdoctoral scholars.