Scientific machine learning · Los Alamos National Laboratory
Postdoctoral Researcher
Teaching physics to machines.
I develop scientific machine learning methods to solve forward and inverse problems in high-dimensional spatiotemporal dynamical systems.
Background
I am a Postdoctoral Researcher at Los Alamos National Laboratory. I earned my Ph.D. from the Indian Institute of Science, Bangalore, in June 2023, with a significant portion of my research conducted at Purdue University as a visiting student.
My research focuses on scientific machine learning for forward, inverse, and optimization problems in spatiotemporal dynamical systems. Current work includes PDE foundation models, operator learning, and latent evolution models for particle accelerator dynamics.
Postdoctoral Researcher
Los Alamos National Laboratory
2023–present
Project Engineer (Research)
IIT Kanpur
2018
Visiting Ph.D., Mechanical Engineering
Purdue University
Jan. 2022–June 2023
Ph.D., Aerospace Engineering
IISc Bangalore
2019–2023
Current work
Operational intelligence
Self-supervised foundation models pretrained on large-scale operational telemetry from multiple particle accelerators to learn reusable sensor representations for anomaly detection, fault identification, and missing-data reconstruction.
Explore Sensoformer →Physical systems
Foundation models that learn across data modalities and transfer to physical systems outside the training distribution.
Explore MORPH →Latent evolution models
Forward and inverse modeling, uncertainty quantification, and tuning of charged-particle beam dynamics in particle accelerators.
Explore latent evolution models →Inverse problems
Machine-learning surrogate solvers and optimization methods for mechanics-based inverse problems.
Explore hybrid models →Latest
One Trajectory, Two Requirements accepted at the CoRL 2026 TAG Workshop. Paper →
PFM4Control paper accepted at the ML4PS Workshop. Paper →
Sim+Real paper accepted for an oral presentation at the NeurIPS 2026 FMTS Workshop. Paper →
Foundation models for particle accelerator paper accepted in NeurIPS 2026. Paper →
Research output
CoRL 2026 TAG Workshop
Open research
Software
Dataset
Community
Reviewer for more than 30 articles across journals including Nature Machine Intelligence, Scientific Reports, and IEEE, IoP, Elsevier, ASME, and Springer publications.
Gold reviewer for ICML 2026, Reviewer for NeurIPS, AAAI, ICLR and workshops of top 4 AI conferences.
Prof. Chintakindi V. Joga Rao Medal for best Ph.D. thesis, Indian Institute of Science, 2024.
Overseas Visiting Doctoral Fellowship, SERB-DST, 2022–2023.
Invited talks on generative modeling, particle accelerator dynamics, and data-driven plasma science.
View selected talks →