School of Mathematical Sciences
School of
Mathematical Sciences
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 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 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

September 9, 2020
RIT scientists contribute to the first discovery of an intermediatemass black hole
The LIGO Scientific Collaboration and the Virgo Collaboration recently announced the discovery of GW190521, the most massive gravitational wave binary observed to date, and Rochester Institute of Technology scientists played an important role in identifying and analyzing the event.

September 2, 2020
New mathematical method shows how climate change led to the fall of an ancient civilization
Nishant Malik, assistant professor in RIT’s School of Mathematical Sciences, has developed a mathematical method that shows how shifting monsoon patterns led to the demise of the Indus Valley Civilization, a Bronze Age civilization contemporary to Mesopotamia and ancient Egypt.

August 12, 2020
FacultyStudent Collaboration Responsible for COVID Modeling Development
Two RIT students collaborated with faculty and coauthored their first paper which contributes to the body of knowledge surrounding the COVID19 pandemic.
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
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....
Undergraduate Programs
The School of Mathematical Sciences 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.
The applied mathematics major focuses on the study and solution of problems that can be mathematically analyzed. Industry, academia, and government all have a great need for individuals with this type of education. Students gain the knowledge and skills to collaborate on complex problems with scientists, engineers, computer specialists, or other analysts. Some application areas include applied statistics; biology; business; economics; chemistry; electrical, industrial, or mechanical engineering; operations research; and imaging science.
Learn More about Applied Mathematics BSThe applied statistics and actuarial science major provides students with a strong foundation in mathematical and statistical methodology, experience in its applications, a solid background in the use of statistical computing packages, and the skills to communicate the results of statistical analysis. This gives students an advantage in the fields of business, government, and industry, and also prepares them well for advanced study in graduate school.
Learn More about Applied Statistics and Actuarial Science BSComputational mathematics prepares students for a mathematical career that incorporates extensive computer science skills. In this major, much emphasis is given to the use of the computer as a tool to solve mathematically modeled physical problems. Students often pursue positions as mathematical analysts, scientific programmers, software engineers, or systems analysts. Job opportunities in private industry and government abound in this field.
Learn More about Computational Mathematics BSGraduate Programs
The School of Mathematical Sciences 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.
The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. This includes applications of mathematics to problems in management science, biology, portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal is to find computable solutions to realworld problems arising from these types of situations.
Learn More about Applied and Computational Mathematics MSThe advanced certificate in applied statistics is designed for engineers, scientists, analysts, and other professionals who want a solid education in the statistical methods that are most closely related to their work. Courses are available both oncampus and online.
Learn More about Applied Statistics Adv. Cert.The MS program in applied statistics is available to both full and parttime students with courses available both oncampus and online. Cooperative education is optional. The program is intended for students who do not wish to pursue a degree beyond the MS. However, a number of students have attained doctorate degrees at other universities.
Learn More about Applied Statistics MSMathematical modeling is the process of developing mathematical descriptions, or models, of realworld 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.
Learn More about Mathematical Modeling Ph.D.Minor and Immersions
Notes about this immersion:
Learn More about Applied Statistics ImmersionThe 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.
Learn More about Applied Statistics MinorNotes about this immersion:
Learn More about Mathematics ImmersionThe 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 Mathematics Minor