Welcome New Kruskal Instructors!

September 30, 2020

The Committee is excited to welcome the following Kruskal Instructors:

Pierre Yves Gaudreau Lamarre 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.

Brad Nelson received a Ph.D. in Computational and Applied Mathematics from Stanford University, on a National Defense Science and Engineering Graduate Fellowship in 2020. His research area is in an emerging discipline of applied mathematics now widely called topological data analysis (TDA). In his PhD work, he developed several new methodologies in TDA, established new connections with current machine learning techniques, and created new software to realize his algorithmic ideas.

Ben Palacios received a Ph.D. in Mathematics from University of Washington in 2018. He was most recently a post-doctoral scholar at the University of Chicago, Department of Statistics. He most recently worked on the derivation of Fermi pencil beam approximations to radiative transport and Fokker-Planck equations. This finds applications in the propagation of light in turbulent atmospheres or more general turbid media.

Alexander Strang completed his PhD in Applied Mathematics at Case Western Reserve. His research interests fall mainly in mathematical cellular biology and mathematical ecology, where he invents novel methods for the study of scientific problems arising from these two areas. In the former, he pioneered the use of stochastic shielding in efficient simulations of cellular processes; while in the latter he adapted Hodge decomposition to analyze non-equilibrium ecological systems. 

Zhongjian Wang received a Ph.D. in Mathematics from the University of Hong Kong in 2020. His main area of expertise is the numerical simulation of stochastic differential equations. He developed numerical schemes that preserved important structures of the underlying model, allowing for long term integrations that do not suffer from usual exponential instabilities. His research interests also include the numerical simulation of partial differential equations by means of neural-network-type minimizations.

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