Romit Maulik, Argonne Scholar, LCF/ANL
Machine Learned Reduced-Order Models for Advective Partial Differential Equations

Scott Dawson, Assistant Professor, Mechanical, Materials and Aerospace Engineering Department, Illinois Institute of Technology
Accurate and Efficient Methods for Reduced-Complexity Modeling in Fluid Mechanics

Anirudh Subramanyam, Postdoctoral Appointee, MCS/ANL
Data-Driven Methods for Robust Optimization Under Uncertainty

Navjot Kukreja, PhD Student, Imperial College London
Full Waveform Inversion with a Finite Difference DSL

Kevin Carlberg, AI Research Science Manager, Facebook
Nonlinear Model Reduction: Using Machine Learning to Enable Rapid Simulation of Extreme-Scale Physics Models

Rebecca Morrison, Assistant Professor, University of Colorado
Learning Sparse Non-Gaussian Graphical Models

Johannes Brust, Postdoctoral Appointee, MCS/ANL
Limited Memory Structured Quasi-Newton Methods

Elizabeth Qian, PhD Student, MIT
Lift & Learn: A Scientific Machine Learning Framework for Learning Low-Dimensional Models for Nonlinear PDEs

Javad Lavaei, Associate Professor, Department of Industrial Engineering and Operations Research, UC Berkeley
Computational Techniques for Nonlinear Optimization and Learning Problems

Samy Wu Fung, Adjunct Professor, UCLA
A GAN-Based Approach for Solving High-Dimensional Stochastic Mean Field Games