Seminar

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

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