School of Mathematics and Statistics
School of
Mathematics and Statistics
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 School of Mathematics and Statistics
Overview
The School of Mathematics and Statistics 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 realworld 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.
1^{st}
Mathematical modeling Ph.D. program in the nation
3:1
Studenttofaculty ratio
2
NSF Funded Research Experiences for Undergraduates (REU) Programs
Latest News

August 7, 2023
RIT Undergraduate Research Symposium features studentled technology, art, and design solutions
More than 158 students presented research discoveries conducted over the past spring and summer at RIT’s 32nd annual Undergraduate Research Symposium on Aug. 3.

July 20, 2023
AI has secured a footing in drug discovery. Where does it go from here?
PharmaVoice talks to David Longo ’10 (computational mathematics), CEO of drug design company Ordaos, about artificial intelligence in drug development.

June 20, 2023
SignSpeak joins AWS Impact Accelerator
The Rochester Beacon features Nikolas Kelly '20 (supply chain management), cofounder and chief product officer of SignSpeak, and Nicholas Wilkins '19 (computational mathematics), '19 MS (computer science).
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 graphbased models of the brain to study the impact of concussions, improving and developing new graphbased algorithms for hyperspectral image analysis, applying the growing concepts of complex network analysis to domainbased 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, microfluidics 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 MagnetoHydrodynamics. As part of the LIGO Scientific Collaboration, SMS faculty and researchers use statistical signal processing techniques to search for, identify and characterize gravitationalwave 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 multimessenger and multiwavelength 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
Mary Pryor
RIT students from all majors learn creative and interdisciplinary problemsolving through the perspectives of a diverse set of faculty members.
RIT Undergraduates Advance the Technique of Asymptotic Approximants Created by the BarlowWeinstein Group
Nathaniel Barlow
Four undergraduate students presented their research on the analytical solution to the classical FalknerSkan 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 3Dprinted models of mathematical equations to illustrate wave systems and other fluid dynamics concepts as part of their research....
Featured Profiles
Explore Undergraduate Research, Discover a Passion, Land a Career
Tyler Godat ’16 (physics and applied math)
Tyler Godat ’16 chose RIT because of its reputation for undergraduate research, coops, and internships. Now, he is an optical measurements engineer at Corning.
Ph.D. student explores fire through visual art and math modeling
Mathematical Modeling Ph.D. student, Jenna Sjunneson McDanold, explores fire through visual art and mathematical modeling.
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.
Undergraduate Programs
The School of Mathematics and Statistics provides a solid collegiate math education to every RIT undergraduate and offers highlevel 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.
An applied mathematics major focusing on problems that can be mathematically analyzed and solved, including models for perfecting global positioning systems, analyzing costeffectiveness in manufacturing processes, or improving digital encryption software.
Learn more about the Applied Mathematics BS programUsing statistics, probability, and computing, statisticians apply their knowledge of statistical methods—from data collection, and processing, through analysis and interpretation—to a variety of areas, including biology, economics, engineering, manufacturing, medicine, public health, psychology, marketing, and sports.
Learn more about the Applied Statistics and Data Analytics BS programRIT’s computational mathematics major emphasizes problemsolving using mathematical models to identify solutions in business, science, engineering, and more.
Learn more about the Computational Mathematics BS programGraduate Programs
The School of Mathematics and Statistics equips its graduates with a deep understanding of math principles, a toolbox for applying those skills to realworld 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 cuttingedge methodology as a general tool in the study of exciting problems in science, business, and industry.
An applied and computational mathematics master’s degree that is designed for you to create innovative computing solutions, mathematical models, and dynamic systems to solve problems in industries such as engineering, biology, and more.
Learn more about the Applied and Computational Mathematics MS programEngineers, analysts, and other professionals develop a deeper understanding of the statistical methods related to their fields.
Learn more about the Applied Statistics Adv. Cert. programIn this master’s in applied statistics, you’ll learn statistical analysis and apply it to a variety of industries, including insurance, marketing, government, health care, and more.
Learn more about the Applied Statistics MS programDemand is high for professionals skilled in both analytics and computing. Enhance your skill set by learning to manage largescale data sets in this data science master’s.
This degree program is offered jointly between the Golisano College of Computing and Information Sciences and the College of Science.
Learn more about the Data Science MS programThe mathematical modeling Ph.D. enables you to develop mathematical models to investigate, analyze, predict, and solve the behaviors of a range of fields from medicine, engineering, and business to physics and science.
Learn more about the Mathematical Modeling Ph.D. programMinors and Immersions
The actuarial science minor prepares students for work in insurance companies, investment firms, banks, for the government, and in the health care industry where there is a need to analyze the financial consequences of risk. The actuarial science minor prepares students for two exams administered by the Society of Actuaries. Those exams are Exam P: Probability, which assesses a candidate's knowledge of the fundamental probability tools for quantitatively assessing risk, and Exam FM: Financial Mathematics, which assesses a candidate's understanding of the fundamental concepts of financial mathematics and how those concepts are applied in a variety of areas.
Learn more about the Actuarial Science Minor programDeepen your technical background and gain further appreciation for modern mathematical sciences and the use of statistics as an analytical tool.
Learn more about the Applied Statistics Immersion programDeepen your technical background and gain further appreciation for modern mathematical sciences and the use of statistics as an analytical tool.
Learn more about the Applied Statistics Minor programNotes about this immersion:
Learn more about the Mathematics Immersion programThe 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.
Learn more about the Mathematics Minor programUpcoming Math Seminars
This seminar series is focused on all aspects of inverse problems including, but not limited to differential equations, numerical methods, optimization, uncertainty quantification, experimental techniques, data analysis techniques, and computational methods. InvPrS is open to the public, and everyone is welcome to attend.
This is a big tent seminar series for everything discrete: graph theory, combinatorics, combinatorial optimization, applications of discrete mathematics, and computational aspects of all these subjects. DisCoMathS is open to the public, and everyone is welcome to attend.
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. It is open to the public, and everyone is welcome to attend.
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