My research combines computational physics, machine learning, and high-performance computing. The objective is to discover structure in data to drastically reduce the cost of simulating nonlinear dynamical systems at extreme scale.
My recent plenary talk at the ICERM Workshop on Scientific Machine Learning summarizes my work.
Recent publication highlights
- Our preprint on model reduction on nonlinear manifolds using deep convolutional autoencoders is available on the arXiv; slides available here. (Joint work with K. Lee)
- Our preprint on online adaptive basis refinement and compression is available on the arXiv. (Joint work with P. Etter)
- Our preprint on statistical closure modeling using the ROMES method is available on the arXiv. (Joint work with S. Pagani and A. Manzoni)
- Our preprint on autoencoders and dynamics learning to recover missing CFD data for high-order discretizations is available on the arXiv. (Joint work with A. Jameson, M. Kochenderfer, J. Morton, L. Peng, and F. Witherden)
- Our preprint on using reduced-order models and sparse grids for stochastic optimization is available on the arXiv. (Joint work with M. Zahr and D. Kouri)
- Our paper on machine-learning error models has been published in Computer Methods for Applied Mechanics in Engineering. (Joint work with B. Freno)
- Our paper on space–time nonlinear model reduction with least-squares Petrov–Galerkin projection has been published in the SIAM Journal on Scientific Computing. (Joint work with Y. Choi)
- Our group currently has job openings for both postdocs (apply here) and staff (apply here) in the areas of reduced-order modeling, scientific machine learning, high-performance computing, and uncertainty quantification.
- I am excited to welcome new postdoc Yukiko Shimizu (PhD 2019, University of Michigan) to our research group.
- I gave a talk at the SIAM Conference on Computational Science & Engineering in Spokane, Washington on Thursday, February 28 (slides).
- I gave a talk at the Department of Aeronautics and Astronautics at MIT on Friday, February 22 (slides).
- I gave a talk at Lawrence Berkeley National Laboratory on Friday, February 15.
- I gave an invited talk at the ICERM Workshop on Scientific Machine Learning at Brown University January 28–30, 2019 (slides). Talk video accessible here.
- I am excited to have joined the Editorial Board of the SIAM Journal on Scientific Computing (SISC) as an Associate Editor.
- I gave a talk at the SAMSI MUMS Opening Workshop in Durham, North Carolina August 20–24, 2018.
- I taught Introduction to Mathematical Optimization at the ICME Summer Workshops: Fundamentals of Data Science on August 14, 2018.
- I gave a minisymposium plenary talk at the 13th World Congress in Computational Mechanics in New York, New York July 23–27, 2018.
- I gave a talk at the Workshop on Research Challenges and Opportunities at the interface of Machine Learning and Uncertainty Quantification at The University of Southern California in Los Angeles, California, June 4–6, 2018.
- I gave a talk at the Workshop on Digital Twins and ROMs at The Boeing Company in Bellevue, Washington on May 15, 2018.
- I gave a talk to the UQ Lab at Stanford University in Stanford, California on May 3, 2018.
- I gave a talk to the Reduced Models for the Cardiovascular System at Emory University in Atlanta, Georgia on April 26, 2018.
- I gave a talk to the Pixar Research Group at Pixar Animation Studios in Emeryville, California on April 24, 2018 (slides).
- I gave a talk at the SIAM Conference on Uncertainty Quantification in Orange County, California April 16–19, 2018.
- I gave a talk and presented a poster at the Model Reduction of Parametrized Systems (MoRePaS) IV in Nantes, France April 10–13, 2018.
- I gave a talk at the Advanced Modeling & Simulation Seminar Series at NASA Ames on March 29, 2018 (slides). Talk video here.