The Division of Applied Mathematics


The mission of the Division of Applied Mathematics rests on research, education, and scholarship. We focus our research and teaching on a wide range of areas from applied and algorithmic problems to the study of fundamental mathematical questions. In particular, we explore the connections between mathematics and its applications in biology, chemistry, engineering, geosciences, neuroscience, physics and other disciplines at the research and educational levels. Our educational efforts are realized primarily through our graduate PhD program and our four undergraduate concentrations.  

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Kavita Ramanan was elected as a Fellow of the AAAS 

Kavita RamananKavita RamananKavita Ramanan has been elected a Fellow of the AAAS (American Association for the Advancement of Science), having received this honor "for her research in applied probability and efforts to increase the number of women in the sciences."  Professor Ramanan has made fundamental contributions to several distinct areas of probability theory, stochastic processes and their applications.  She has developed novel techniques to obtain mathematically rigorous and computationally tractable approximations to complex interacting random processes that model phenomena in diverse fields ranging from operations research and biology to statistical physics.  She has also made significant contributions to the study of large deviations, or the study of rare events, and the study of high-dimensional distributions through their low-dimensional projections, which is of interest to both pure mathematicians interested in convex geometry and asymptotic functional analysis, as well as statisticians working with high-dimensional data.  Professor Ramanan stated, "I am deeply honored to have received this distinction for my research, and am grateful for the many wonderful collaborators, postdoctoral fellows and students that have joined me on my research adventures.  I also hope to continue to contribute to mathematical education at all levels via outreach initiatives such as the Math CoOp and to support underrepresented groups to create a more diverse research environment."  (Read more.)

Deep Learning Expands Study of Nuclear Waste Remediation

"A research collaboration between Lawrence Berkeley, Pacific Northwest National Laboratory, Brown University, and NVIDIA as achieved exaflop performance in the Summit supercomputer with a deep learning application used to model subsurface flow in the study of nuclear waste remediation."  Professor Karniadakis, Professor of Applied Mathematics at Brown is co-author of the article describing their findings (read more).

A new computer model could help test new sickle cell drugs

 A newly developed computer model captures the dynamics of the red blood cell sickling process and could provide help in evaluating drugs for treating sickle cell disease.  Sickle cell disease is a genetic disorder that affects millions of people around the world.  (Read more.)