School of Mathematical Sciences
School of
Mathematical Sciences
Overview
The School of Mathematical Sciences is recognized for its contributions to research and applications of mathematical and statistical science, and it’s also known for expertise in mathematical and computational modeling, data science, and scientific inference. Since mathematics is at the root of many social, technical, medical, and environmental issues faced by society today, we equip our graduates with a deep understanding of mathematical and statistical principles, tools to apply those skills to real-world problems, and the ability to express complex ideas in everyday language. We provide our students with research and experiential learning opportunities and nurture curiosity and creativity.
1st
Mathematical modeling Ph.D. program in the nation
3:1
Student-to-faculty ratio
2
NSF Funded Research Experiences for Undergraduates (REU) Programs
Latest News
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September 1, 2022
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Scientists find the social cost of carbon is more than triple the current federal estimate
After years of robust modeling and analysis, a multi-institutional team including researchers from RIT has released an updated social cost of carbon estimate that reflects new methodologies and key scientific advancements.
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July 12, 2022
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Ph.D. student applies imaging science to preventing disasters
Kamal Rana, an imaging science Ph.D. student from India, has been using his skills to help identify landslide triggers and develop models for forecasting landslides.
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June 28, 2022
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College of Science Dean Sophia Maggelakis to become provost of Wentworth Institute of Technology
Dean Sophia Maggelakis will be leaving RIT to become the senior vice president for academic affairs and provost at Wentworth Institute of Technology. Maggelakis joined RIT as an assistant professor in 1990, became head of the School of Mathematical Sciences in 2001, and became dean of the College of Science in 2010.
Research
Current research in the unit involves developing mathematical frameworks to discern properties of a system by working backward from known effects. Application areas include medicine, engineering, finance, earth science and imaging and the focus is on investigating the impact of uncertainty in data, identification of cancer in soft tissues, estimation of material properties, identification of market volatility, and developing fast and reliable methods for large scale computational optimization.
Research Active Faculty:
Current work in the unit includes research and consulting in biostatistics, machine learning, data science, predictive analytics, signals processing, statistical education, and statistical/scientific inference with applications to biology, astrophysics, and engineering.
Research Active Faculty:
Current research in the unit involves developing improved mathematical models of physiological systems; gaining new insights into mechanisms of physiological behavior; improving techniques for diagnosing and treating diseases; and devising advanced algorithms for analyzing physiological measurements.
Research Active Faculty:
Current work in the unit involves developing graph-based models of the brain to study the impact of concussions, improving and developing new graph-based algorithms for hyper-spectral image analysis, applying the growing concepts of complex network analysis to domain-based scientific problems, and applying algebraic techniques and methods to problems in cybersecurity.
Research Active Faculty:
Current work in the unit involves applying mathematical techniques of nonlinear dynamical systems to problems in fluid dynamics, climate modeling, population modeling, cell signaling dynamics, and more; developing mathematical models of thin film and interfacial flows with application to biological fluids, micro-fluidics devices, and industrial coating processes; gaining insights that lead to better prediction of hydrodynamic instabilities, such as turbulence, liquid fuel atomization, and liquid film breakup; devising novel computational methods to simulate fluid transport phenomena; and improving the current understanding of polymer flows and viscoelastic fluids.
Research Active Faculty:
Current work in the unit includes applications of differential geometry, numerical solutions of partial differential equations, and statistical inference to problems related to general relativity and celestial mechanics. Einstein's general theory of relativity is studied as a description of the geometry of spacetime. Advanced numerical and computational techniques are used to solve the coupled, nonlinear, system of PDEs of General Relativity and Magneto-Hydrodynamics. As part of the LIGO Scientific Collaboration, SMS faculty and researchers use statistical signal processing techniques to search for, identify and characterize gravitational-wave signals from astrophysical systems.
Research Active Faculty:
Current work in the unit involves developing new mathematical techniques to study problems of geophysical fluid dynamics, climate modeling, extreme weather, coastal and natural hazards, and other complex systems arising in the study of Earth and environmental systems.
Research Active Faculty:
RIT faculty conduct observational and theoretical research across a wide range of topics in multi-messenger and multi-wavelength astrophysics, utilizing a combination of observations spanning the electromagnetic spectrum, data from gravitational wave detectors, and supercomputer simulations. Current areas of research include numerical relativity and relativistic magnetohydrodynamics, gravitational wave data analysis, compact object binaries, accretion disks and jets, supernovae, and pulsars. RIT is a member of the Large Synoptic Survey Telescope Corporation and faculty are involved in several major collaborations including the Laser Interferometer Gravitational Wave Observatory Scientific Collaboration, the NANOGrav Pulsar Timing Array Consortium and the Laser Interferometer Space Antenna.
Research Centers:
Center for Computational Relativity and Gravitation
Research Active Faculty:
Featured Work
Climate Change Course: Complex Teams Solving Complex Problems
RIT students from all majors learn creative and interdisciplinary problem-solving through the perspectives of a diverse set of faculty members.
RIT Undergraduates Advance the Technique of Asymptotic Approximants Created by the Barlow-Weinstein Group
Nathaniel Barlow
Four undergraduate students presented their research on the analytical solution to the classical Falkner-Skan equation that describes boundary layer flow over a wedge.
3D Models of Math Equations
Nate Barlow and Steve Weinstein
Assistant Professor Nate Barlow and Professor Steve Weinstein made 3D-printed models of mathematical equations to illustrate wave systems and other fluid dynamics concepts as part of their research....
Featured Profiles
Computational Mathematics and a Future in Cryptography
Keegan Kresge ‘22 (computational mathematics)
Keegan Kresge loves math and programming, making him the perfect fit for cryptography. After completing his degree in computational mathematics, he plans to work at the Department of Defense.
How Mathematicians Move Business Forward
Emily (Kiesel) Wert ’18 (applied & computational mathematics)
Emily (Kiesel) Wert has shown business leaders how valuable mathematicians are on any data analysis and problem-solving team.
RIT Alum Uses Math for NBA Analytics
Calvin Floyd ’17 (applied & computational mathematics)
Calvin Floyd developed the strong technical skills needed to succeed in an NBA analytics department during his masters degree program in applied and computational mathematics at RIT.
Undergraduate Programs
The School of Mathematical Sciences provides a solid collegiate math education to every RIT undergraduate and offers high-level specializations such as statistical forecasting, digital encryption, and mathematical modeling. We prepare our graduates to be successful, whether they choose immediate employment upon graduation or to attend graduate school in pursuit of advanced degrees.
Graduate Programs
The School of Mathematical Sciences equips its graduates with a deep understanding of math principles, a toolbox for applying those skills to real-world problems, and the ability to easily express complex ideas. Our graduate programs introduce students to rigorous advanced applied mathematical and statistical methodology. Students realize the potential for that cutting-edge methodology as a general tool in the study of exciting problems in science, business, and industry.
