July 1, 2020
The Department is excited to welcome the following new hires:
Claire Donnat, assistant professor. She works on statistical methods for high-dimensional structured and unstructured data with application to neuroscience. Her main doctoral research is on graph-structured data and multi-resolution methods. Claire is an Applied Mathematics and Computer Science graduate of Ecole Polytechnique where she obtained an M.Sc. degree, and received her Statistics Ph.D. in 2020 from Stanford University.
Jeremy Hoskins, assistant professor. As an applied mathematician, he has a broad range of research interests. His recent contributions produced noted fast algorithms to solve partial differential equations (PDE), primarily of Helmholtz type, with singular solutions due to complex geometries and non-smooth boundaries. Jeremy comes to us from Yale University where he was a Gibbs Assistant Professor in the Department of Mathematics. He obtained his Mathematics Ph.D. in 2017 at University of Michigan.
Alisa Knizel, assistant professor. Her research interests are in the nascent field of “integrable probability”, which focuses on models in statistical physics, combinatorial probability, and random matrix theory that are amenable to study by algebraic methods. Alisa obtained her Mathematics Ph.D. in 2017 at MIT, and came to us from Columbia University, where she was an NSF Postdoctoral Fellow in the Department of Mathematics.
Yi Sun, assistant professor. His research spans a wide array of problems in representation theory, probability, and neighboring fields, ranging from conformal field theory to machine learning.
Yi joins us after 3 years as a Joseph F. Ritt Assistant Professor in the Department of Mathematics at Columbia University; he was a Simons Junior Fellow at Columbia (2016-2019). Yi obtained his Mathematics Ph.D. at MIT in 2016.
Pierre Yves Gaudreau Lamarre, William H. Kruskal Instructor received a probability PhD at Princeton from the Operations Research and FinancialEngineering Department. His main area of research is in the analysis of partial differential equations (PDEs) with multiplicative Gaussian white noise in space.