Senior Research Scientist / AWS
Agentic RL, LLM post-training, and structured-data foundation models.
About
Ph.D. in Physics. Research scientist in machine learning.
I work on machine learning methods, production-scale systems, and applications in science.
I work on learning systems for reasoning, structured data, and scientific applications.
I am a Senior Research Scientist in AWS AGI Foundations. My current work focuses on agentic reinforcement learning, LLM post-training, and foundation models for structured data, including time series and tables.
Before AWS, I was an AI Researcher at Shell, developing physics-informed and weakly supervised methods for scientific data. I earned my Ph.D. in Physics from Harvard University, where I worked with Xiaowei Zhuang on computational and super-resolution microscopy.
My background spans computational microscopy, physics-informed machine learning, and large-scale foundation models.
Research Scientist / AWS
Foundation-model pretraining, multimodal reasoning, geospatial AI, and model adaptation.
AI Researcher / Shell
Physics-based semi-supervised learning for sparse scientific observations.
Ph.D. in Physics / Harvard University
Machine-learning-driven computational microscopy and quantitative biological discovery.
B.S. in Physics / Shandong University
Foundations in physics, chemistry, and computational science.
Recognition / 01
Awards, talks, and research service.
Area Chair Award
ACL 2024 / CaMML
Invited research talks
ICLR Expo, NeurIPS Expo, ICML Expo, and LBNL AI4Science
Workshop organizer
ICLR AI4Differential Equations and ICML Foundation Models for Structured Data
Research community
Area Chair for ICLR; reviewer for ICML, NeurIPS, KDD, CVPR, ICCV, and ARR
Distinction in Teaching
Harvard University / 2014
Karplus Prize
Harvard University / 2013