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.
Mathematical modeling Ph.D. program in the nation
NSF Funded Research Experiences for Undergraduates (REU) Programs
Statistical machine learning is at the core of modern-day advances in artificial intelligence, but RIT professor Ernest Fokoué argues that applying it correctly requires equal parts science and art. Fokoué emphasized the human element of statistical machine learning in his primer on the field that graced the cover of a recent edition of Notices of the American Mathematical Society.
Getting internships wasn't always easy, but Reid Kamhi never gave up. He knew the importance of adding project experience to his resume. In this spotlight, he shares his story and offers tips to other RIT students looking for internship opportunities.
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.
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.
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.
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.
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.
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.
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.
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.
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....
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.
A focus on the study of problems that can be mathematically analyzed and solved, including models for perfecting global positioning systems, analyzing cost-effectiveness in manufacturing processes, or improving digital encryption software.
Using calculus, statistics, algebra, and computer science, statisticians apply their knowledge of statistical methods—the collection, processing, and analysis of data and its interpretation—to a variety of areas, including biology, economics, engineering, medicine, public health, psychology, marketing, and sports.
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.
Mathematical modeling is the process of developing mathematical descriptions, or models, of real-world systems. These models can be linear or nonlinear, discrete or continuous, deterministic or stochastic, and static or dynamic, and they enable investigating, analyzing, and predicting the behavior of systems in a wide variety of fields. Through extensive study and research, graduates of this program will have the expertise not only to use the tools of mathematical modeling in various application settings, but also to contribute in creative and innovative ways to the solution of complex interdisciplinary problems and to communicate effectively with domain experts in various fields.
The applied statistics minor provides an opportunity for students to deepen their technical background and gain further appreciation for modern mathematical sciences and the use of statistics as an analytical tool.
The mathematics minor is designed for students who want to learn new skills and develop new ways of framing and solving problems. It offers students the opportunity to explore connections among mathematical ideas and to further develop mathematical ways of thinking.