Bubbleformer: Forecasting Boiling with Transformers
NeurIPS 2025 · Spotlight Paper
Ph.D. Candidate, Dept. of EECS
University of California, Irvine · HPCForge Lab
I’m a Ph.D. student in Electrical & Computer Engineering at UC Irvine. My research focuses on scientific machine learning for multiphase physics(especially boiling) and on building surrogate models that remain stable and respect conservation laws over long rollouts.
I’m advised by Dr. Aparna Chandramowlishwaran. My work includes Bubbleformer (NeurIPS 2025 Spotlight) and BubbleML (NeurIPS 2023 Spotlight), and I’ve also done a research internship at Oak Ridge National Laboratory.
Selected publications. For a complete list, see my publications page.
Bubbleformer: Forecasting Boiling with Transformers
NeurIPS 2025 · Spotlight Paper
BubbleML: A Multiphase Multiphysics Dataset and Benchmarks for Machine Learning
NeurIPS 2023 · Spotlight Paper
History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction
Under review
NUCLEUS: A Unified Model of Boiling with Mixture of Experts
Under review (ACM KDD 2026)