Plenary Speakers
Mark Ainsworth
Brown University
Professor Ainsworth obtained his PhD from the University of Durham, United Kingdom in 1989. He is the Francis Wayland Professor of Applied Mathematics at Brown University and the current Editor in Chief of SIAM Journal on Numerical Analysis.
Yingda Cheng
Virginia Tech
I am interested in high order accurate and structure preserving numerical methods for differential equations, particularly the discontinuous Galerkin schemes. In recent years, my main research focus is in high dimensional scientific computing, where non-conventional numerical methods are developed to handle the curse of dimensionality, bridging data science and numerical analysis. Application domain of my work includes fusion energy, semiconductor device modeling, nonlinear optics and quantum computing.
Robert Gramacy
Virginia Tech
I am a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech/VT) and affiliate faculty in VT’s Computational Modeling and Data Analytics program. Previously I was an Associate Professor of Econometrics and Statistics at the Booth School of Business, and a fellow of the Computation Institute at The University of Chicago. My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty.
Christine Heitsch
Georgia Tech
Heitsch is Professor of Mathematics at Georgia Tech, with courtesy appointments in Biological Sciences and Computational Science & Engineering as well as an affiliation with the Petit Institute for Bioengineering & Bioscience.
She is also Director of the new Southeast Center for Mathematics and Biology (SCMB), an NSF-Simons MathBioSys Research Center, and finishing her tenure directing the GT Interdisciplinary Mathematics Preparation and Career Training (IMPACT) Postdoctoral Program.
Heitsch’s research interests lie at the interface between discrete mathematics and molecular biology, specifically combinatorial problems “as motivated by” and “with applications to” fundamental biomedical questions like RNA folding.
Guowei Wei
Michigan State
Guowei Wei received his Ph.D. degree from the University of British Columbia and is currently an MSU Research Foundation Professor at Michigan State University. His research focuses on the mathematical foundation of biosciences and artificial intelligence (AI). Dr. Wei pioneered mathematical AI paradigms, such as topological deep learning (TDL), that integrate profound mathematical structures with AI to tackle biological challenges. His math AI has led to victories in D3R Grand Challenges, a worldwide annual competition series in computer-aided drug design. Using TDL, genotyping, and computational biophysics, the Wei team unveiled the mechanisms of SARS-CoV-2 evolution and successfully forecast emerging dominant SARS-CoV-2 variants.