Chong Liu

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR.

• PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang
• Postdoc: University of Chicago Data Science Institute (2023)

Research Interests:

• Broad: Machine Learning, Optimization, AI for Science
• Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery
• Applications: Binding affinity prediction, Drug screening, Policy optimization

Recent Article Trends:
His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems.

Scientific Awards:

• 2025: SUNY IITG/OER Impact Grant

Professional Activities:
Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech.

Teaching:
Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.