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.
Recent News and Events
The Women's Intellectual Networking Research Symposium (WINRS) will meet on March 4, 2017, on the Brown University campus at 170 Hope Street. This one day conference will feature plenary talks, students talks, as well as a panel on effective mentoring, and much more! The objective of the conference seeks connect women in similar mathematical fields, to promote collaboration, and to share strategies for addressing issues facing women and other underrepresented groups in mathematics. For more information please contact firstname.lastname@example.org or email@example.com.
Nicolas Garcia Trillos, Prager Assistant Professor, in the Division of Applied Mathematics, has received the Francisco Aranda Ordaz Award for his thesis entitled, "Variational limit of graph cuts on point clouds." The Latin American Congress of Probability and Mathematical Statistics grants this special award to honor the memory of Francisco Aranda-Ordaz, who was a distinguished young Mexican statistician who died tragically in 1991. (Read full story.)
David Lipshutz, a postdoctoral research fellow, Pooja Agarwal, a Ph.D. candidate, both in the Division of Applied Mathematics, won Best Poster Awards at the biennial Stochastic Networks Conference at the University of California, San Diego. Agarwal's research focuses on surveying recent developments, and identifying future research directions in stochastic network models. Her poster described results on the equilibrium behavior of randomized load balancing algorithms. Lipshutz' research focuses on understanding the effects of small changes to parameters that describe stochastic network models.