Welcome to the Mathematical Modeling Seminar at RIT! This seminar series is focused on all aspects of mathematical modeling, including the development, analysis, refinement, and validation of mathematical models in a wide variety of applications. During the Spring 2021 semester, this seminar will be held weekly from 2:00pm - 2:50pm on Tuesdays via Zoom. It is open to the public, and everyone is welcome to attend.
April 27 - Dr. Mojdeh Rasoulzadeh, The University of Alabama
Time: 2:00 PM - 2:50 PM Eastern
Dr. Mojdeh Rasoulzadeh
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.
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 utilised 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.
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
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
Assistant Professor, Geophysics
Member, Institute for Computational and Mathematical Engineering (ICME)
Center Fellow, Stanford Woods Institute for the Environment
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.
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
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.
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 judgement; 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
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
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
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
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.
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.
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.
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.