Brian Van Koten

Brian Van Koten is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on applied probability and numerical analysis, with applications to molecular systems simulation, variance reduction in statistical physics, and numerical methods for saddle point identification. He teaches courses such as Math 652 (Numerical Analysis II), emphasizing computational methods for partial differential equations and optimization in machine learning.

Key research interests include computer simulations of biological and materials systems, rare event sampling techniques, and atomistic/continuum coupling methods. He has presented work at institutions like the Institute for Computational and Experimental Research in Mathematics (ICERM) and the Institute for Pure and Applied Mathematics (IPAM), focusing on topics like stratification for Markov chain Monte Carlo and the string method for minimum energy paths.

Van Koten's work bridges theoretical numerical analysis with practical computational challenges, addressing issues in multiscale modeling and error analysis. His teaching philosophy integrates advanced numerical methods with real-world applications, supported by detailed course materials on topics like finite difference methods and optimization algorithms for large-scale data science problems.