IACS Announces New Lecturer & Postdoc | IACS

September 14, 2017
IACS Announces New Lecturer & Postdoc | IACS
IACS is pleased to announce the appointments of David Sondak as a lecturer in computational science, and Weiwei Pan, as a postdoctoral researcher.

David Sondak received his PhD in Aeronautical Engineering in 2013 from Rensselaer Polytechnic Institute during which time he was funded through the Department of Energy Office of Science Graduate Fellowship program. He received a Master's degree in Applied Mathematics, also from RPI, in 2011 and a Bachelor's degree in Mechanical Engineering from Lehigh University in 2008.
 
Prior to joining IACS, David was a postdoctoral research fellow in the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin where he conducted research on representing uncertainties in models of chemical kinetics. In that work, he made extensive use of Bayesian statistical techniques to provide meaningful interpretations of uncertainties. Before joining ICES, David was a Van Vleck Visiting Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison. David's ongoing research focuses on the development of physics-based, data-driven models to enable predictive capabilities in problems of engineering and scientific interest including magnetohydrodynamics, thermal convection, and chemical kinetics. This fall, David will teach Systems Development for Computational Science (CS207).
 
Weiwei Pan joins current IACS Post-Docs Niv Dayan and Harikrishna Narasimhan. Weiwei received her Ph.D. in pure math at Wesleyan University, where she specialized in higher categorical structures in algebraic topology. Her post-doctoral work at Goettingen University involved categorification of knot invariants. At Harvard, Weiwei works with Professor Finale Doshi-Velez on developing principled methods for addressing non-identifiability in machine learning models. She also works with IACS Scientific Program Director Pavlos Protopapas on a number of data science projects.