Johann Rudi, Argonne Scholar, MCS/ANL
Inference of Uncertain Parameters in Physical Models Governed by PDEs with Application to Earth’s Mantle Convection

Vivak Patel, Assistant Professor, Department of Statistics, University of Wisconsin – Madison
Practical Random Sketching

Alp Dener, Postdoctoral Appointee, MCS/ANL
Investigating Quasi-Newton Outer Product Representations on GPUs

Barry Smith, Senior Computational Mathematician, MCS/ANL
GPUs, Why They Are So Important and Why Everyone Hates Them

Matthew Otten, Maria Goeppert Mayer Fellowship – Argonne Scholar, NST/ANL
Quantum Machine Learning Using Kernels

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