Our top-quality faculty bring deep experience and teaching excellence to the program.
Aleksandr Aravkin is an assistant professor in the Department of Applied Mathematics, a data science fellow at the UW eScience Institute and an adjunct professor of mathematics and statistics. Aravkin works on theoretical and practical problems connected to data science, including theoretical work in convex and variational analysis, robust statistical modeling and algorithm design with applications to high-dimensional inference, machine learning and inverse problems. He was a postdoctoral fellow from 2010 to 2012 at the University of British Columbia, where he worked on robust approaches for seismic inverse problems. He then served as a research staff member at the IBM Thomas J. Watson Research Center and as an adjunct professor in computer science and industrial engineering and operations research at Columbia University. He joined the UW in 2015. Aravkin earning his Ph.D. in mathematics (optimization) at the University of Washington.
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Chris Bretherton is a professor in the Department of Applied Mathematics and the Department of Atmospheric Sciences. His teaching interests include ordinary and partial differential equations, dynamical systems, fluid dynamics, cloud physics and mesoscale meteorology. He has conducted research on a variety of topics, including numerical fluid dynamics applied to atmospheric convection and cloud-topped boundary layers, applications of fractals to fluid dynamics, and linear and nonlinear wave propagation in geophysical contexts. Bretherton is an elected fellow of the American Meteorological Society. He has a Ph.D. from the Massachusetts Institute of Technology.
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Bernard Deconinck is professor and chair of the Department of Applied Mathematics and an adjunct professor in the Department of Mathematics. He is interested in applying mathematics to physical problems, especially nonlinear wave phenomena. His research has included the study of problems related to Bose-Einstein condensates, fluid mechanics, plasma physics and lattice dynamics using a variety of mathematical techniques from such fields as integrable systems and solitons, dynamical systems, Hamiltonian dynamics, Riemann surfaces and algebraic geometry, Lie algebras, complex variables, asymptotics and perturbation theory.
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Craig Gin is a research associate in the Department of Applied Mathematics. His research interests include the hydrodynamic stability of multi-layer Hele-Shaw and porous media flows, and the application of deep learning to partial differential equations. He earned a Ph.D. from Texas A&M University.
Anne Greenbaum is a professor in the Department of Applied Mathematics. She focuses her research in the area of numerical analysis, especially numerical linear algebra and matrix theory. She previously worked as a mathematician at Lawrence Livermore National Laboratory and as a research professor at the Courant Institute at New York University. Greenbaum is a fellow of the Society for Industrial and Applied Mathematics. She is the author of Iterative Methods for Solving Linear Systems and the coauthor of Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms. She has a Ph.D. from the University of California at Berkeley.
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Mark Kot is an associate professor in the Department of Applied Mathematics and a faculty member in the interdisciplinary graduate program in Quantitative Ecology and Resource Management. His research is at the interface of applied mathematics and population ecology. He models the dynamics of biological populations and has worked on the behavior of integrodifference equations – discrete-time, continuous-space models for the growth and spread of biological populations. Kot is the author of two books, Elements of Mathematical Ecology and A First Course in the Calculus of Variations. He has an M.S. in theoretical and applied mechanics from Cornell University, an M.S. in applied mathematics from the University of Arizona and an M.S. and Ph.D. in ecology and evolutionary biology from the University of Arizona.
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J. Nathan Kutz
Nathan Kutz is a professor in the Department of Applied Mathematics and an adjunct professor in the Department of Electrical Engineering and the Department of Physics. He is especially interested in a unified approach to applied mathematics that includes modeling, computation and analysis. His recent research focuses on phenomena in the optical sciences: laser dynamics and mode-locking in fiber lasers; soliton propagation and mode-coupling dynamics for optical fiber communications; and pattern formation and stability of optical structures in optical parametric oscillators. Kutz has a Ph.D. in applied mathematics from Northwestern University.
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Randy LeVeque is a professor in the Department of Applied Mathematics and an adjunct professor in the Department of Mathematics and the Department of Earth and Space Sciences. He teaches courses on subjects that include numerical analysis, partial differential equations and nonlinear phenomena. His research interests span many areas, including numerical analysis; computational fluid dynamics; nonlinear partial differential equations; mathematical theory of conservation laws; and software development, including the CLAWPACK software for solving conservation laws and other hyperbolic systems modeling wave propagation. LeVeque is a fellow of the Society for Industrial and Applied Mathematics and a fellow of the American Mathematical Society. He has a Ph.D. from Stanford University.
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Matt Lorig is an assistant professor and graduate program coordinator in the Department of Applied Mathematics. Lorig's research focuses on solving problems that arise in the financial industry, and he has written on such topics as derivative pricing, hedging, implied volatility and portfolio management. His research combines tools from stochastic analysis, spectral theory and perturbation methods for PDEs. Of late, Lorig has been particularly interested in model-free approaches to pricing and hedging path-dependent derivative assets. He earned his Ph.D. in physics from the University of California at Santa Barbara.
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Andrew Lumsdaine is the chief scientist at the Northwest Institute for Advanced Computing (NIAC), as well as a laboratory fellow in the Applied Mathematics, Computing and Data Division at the Pacific Northwest National Laboratory (PNNL). He also serves as an affiliate professor in the Paul G. Allen School of Computer Science & Engineering. Lumsdaine is an internationally recognized expert in the area of high-performance computing and has made contributions in the areas of HPC systems, programming languages, software libraries, performance modeling, computational photography and plenoptic cameras. He has also contributed important software artifacts to the research community, especially in the area of message passing interface (MPI). He holds a Ph.D. in computer science and electrical engineering from MIT.
Eric Shea-Brown is an associate professor in the Department of Applied Mathematics and an adjunct associate professor in the Department of Physiology and Biophysics. His interests span a wide set of topics in mathematical neuroscience and biological dynamics. His recent work focuses on optimal signal processing and decision making in simple neural networks, dynamics of neural populations in interval timing tasks, correlations and reliability in simple neural circuits, and properties of oscillator networks with generalized symmetries. Previously Shea-Brown was a postdoctoral fellow in mathematical neuroscience at the Courant Institute and Center for Neural Science at New York University. He studied engineering physics at the University of California, Berkeley, and earned a Ph.D. from the Department of Applied and Computational Mathematics at Princeton University.
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Eli Shlizerman is an assistant professor in the Department of Applied Mathematics. His research combines dynamical systems theory with data analysis to produce realistic data-driven dynamical models. In particular, his focus is on developing methods for inference of network architecture and modeling dynamics of networks; his investigations are at the interface of development of generic computational approaches and modeling actual biological and physical systems. He has a Ph.D. in applied mathematics from the Weizmann Institute of Science.
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Ka-Kit Tung is a professor in the Department of Applied Mathematics and an adjunct professor in the Department of Atmospheric Sciences. He is the chief editor of the Journal of the Atmospheric Sciences, a fellow of the American Meteorological Society and a fellow of the Royal Meteorological Society. Tung previously worked as an associate professor of applied mathematics at Massachusetts Institute of Technology. He received his bachelor’s and master's degrees in aeronautical engineering from the California Institute of Technology and earned a Ph.D. in applied mathematics at Harvard University.
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