Physics-informed Density of States Prediction with Structure-Aware Sequence Decoding and Mass-Conserving Refinement
Nature Computational Science (Under Review)
I am a 4th-year PhD candidate in Computer Science at Cornell University, advised by Carla P. Gomes. I also work closely with Volodymyr Kuleshov and Christopher De Sa. I am affiliated with Cornell AI for Science Institute and AI-LEAF Institute. My research studies knowledge-driven AI with its applications to scientific discovery. I was also student researcher at Amazon Grand Challenge, AWS AI Lab, Microsoft Research, and NEC Lab America, where my research led to impactful papers and patents on computational antibody design, protein language models, time-series foundation models, and theoretical graph learning. My research has been generously supported by Cornell Presidential Life Science Fellowship.
My research centers on developing knowledge-driven AI methods that integrate physical and domain knowledge into modern machine learning techniques to improve their efficacy, efficiency, robustness, and interpretability. Current research topics include:
Nature Computational Science (Under Review)
ICLR 2026 (Under Review)
NeurIPS 2025 ยท MATH-AI
TMLR 2025
AAAI 2025
ICML 2023
Area Chair: NeurIPS (AI4Science).
Conference Reviewer: ICML, NeurIPS, ICLR, AAAI, AISTATS, UAI, CVPR, ICCV, ECCV.
Journal Reviewer: IEEE Transactions on Knowledge and Data Engineering (TKDE), Bioinformatics, Journal of Biomedical Informatics (JBI), Smart Health, Acta Automatica Sinica.