Hi, I’m a PhD student in the Department of Quantitative and Computational Biology at the University of Southern California, advised by Vsevolod Katritch and Yan Liu. Prior to my PhD, I obtained my bachelor’s degree in Mathematical and Computational Biology at Harvey Mudd College. Between my bachelor’s studies, I returned to Korea to serve my mandatory military service and worked as a software engineer at various companies, including Krust Universe and Krafton.

Research

My research focuses on building “reliable” computational drug discovery, primarily through uncertainty quantification and fragment-based improvement. Recently, I have been expanding into geometric neural networks and Bayesian optimization for drug discovery.

I’m always happy to chat with anyone interested in the computational drug discovery field — please reach out!

Contact

Email: hongyong [at] usc [dot] edu

News

• May 2026: Our paper “Trustworthy Protein–Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion” was accepted at KDD 2026!
• May 2026: Our paper “GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction” was accepted at KDD 2026!
• April 2026: Our paper “PLATE-VS: a web server for protein–ligand assay curation and cross-target virtual screening datasets” was accepted at the Nucleic Acids Research (NAR) webserver issue!
• February 2026: Gave a poster presentation at the 2026 Drug Discovery Innovation Workshop.
• January 2026: Received the NVIDIA Academic Grant for “Fine-Tuning Pose-Predicting Foundational Models with Docking Context for Reliable Virtual Screening”.

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