I am a second-year PhD student in Computer Science at the University of Massachusetts Amherst, being co-advised by Dr. Madalina Fiterau at the Information Fusion lab and Dr. Anna Green at the Sequence Analysis and Genomics (SAGE) lab. Now I am a Machine Learning Co-op (research intern) at Biogen, Synthetic Medicine Design.

My research focuses on developing data-efficient and generalizable machine learning methods for modeling complex biomedical and scientific data under real-world constraints. I study multimodal representation learning and transfer, including learning from heterogeneous and weakly paired data and adapting pretrained models to downstream tasks. I also investigate how learned representations can support scalable and reliable decision-making in scientific applications, such as virtual screening and molecular generation. My work combines methodological development with empirical analysis to assess robustness, generalization, and real-world applicability across domains such as healthcare and molecular science.

Before coming to UMass Amherst, I completed my Master’s degree with distinction in Applied Computational Science and Engineering from Imperial College London in 2024. I accomplished my master’s thesis “Hybrid CNN with Multimodal Data for Early Alzheimer’s Disease Forecasting” with the supervision of Dr. Madalina Fiterau and Dr. James Percival. I obtained a B.E. degree with distinction in Computer Science and Technology at China Agricultural University in 2023, and exchanged to the University of California San Diego in 2021 (University and Professional Studies program).

News

2026.4 My work at my internship in Biogen “Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance” is available in arXiv.

2026.3 Our paper “Mycopermenet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student self-disTillation” has been published. All the data, code, and pretrained checkpoints are available in our GitHub repo. Also presented this work in ACS Spring 2026, Machine Learning and AI for Organic Chemistry session.

2026.1 — Started internship at Biogen in Cambridge as a Machine Learning Research Intern (Co-op).

2026.1 - My co-first-author paper, “Mycopermenet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student self-disTillation,” has been accepted for publication in Journal of Chemical Information and Modeling. Also accepted for an oral presentation at ACS Spring 2026 (Machine Learning and AI for Organic Chemistry Session).

2025.01 - Awarded Paul Utgoff Memorial Graduate Scholarship in Machine Learning at UMass Amherst.

2024.11 - Present a poster at New England Computer Vision (NECV) Workshop 2024, Yale University, New Haven, CT.