My primary research agenda is to develop a mathematical characterization of machine learning (ML) models, their learning/training behavior and the associated precision achieved by them. Towards this end, I study the two broad facets of ML: theory; through the eyes of tools from systems theory, statistics and optimization; and applied; by building AI/ML models to solve key problems in nuclear physics, material science, HPC and more recently climate. I have a strong publication record in the field of ML, HPC and scientific applications with a total of 51 publications. I also have a significant track record of grants and have been involved in 23 grant proposals, many of them multi-institutional. I also have substantial experience in successfully soliciting and managing multi-million dollar multi-institutional proposals as an institutional PI.
News
- Jul 2025 Presentation at International Conference on Continuous Optimization
- May 2025 Presentation at SIAM Dynamical Systems
- Feb 2025 Presentation at SIAM-Computational Science and Engineering
- Oct 2024 Grant awarded: SciDAC-5 Rapids3 Institute
- Sep 2024 Grant awarded: Privacy Preserving Federated Learning