Assistant Professor Department of Computer Science State University of New York at Albany UAB 426, Albany, NY 12222 cliu24 AT albany.edu PhD in Computer Science, UC Santa Barbara, 2023 ICML'25 AISTATS'25 Area Chair / IEEE-TNNLS Associate Editor / Private Pilot

[Biography] [Publications] [Talks] [Services] [Blogs] [Explorations]

Research Interests

• Machine Learning: Bayesian optimization, bandit algorithms, active learning.

• AI for Drug Discovery: experimental design, drug screening, binding affinity prediction.

Teaching

• CSI 501 Computational Algebra and Optimization [Fall 2025 Syllabus]

• CSI 436/536 Machine Learning [Spring 2025] [Fall 2024]

News

• Jul. 2025: Happy to organize the "AI Virtual Cells and Instruments: A New Era in Drug Discovery and Development" (AI4D3-2025) workshop at NeurIPS-2025! Based on FDA's new plan to phase out animal testing requirement, our workshop encourages more dicussion on AI virtual celles and instruments. Besides me, our organizers are Quanquan Gu (UCLA), Michelle M Li (Harvard), Xuefeng Liu (UChicago), Abhishek Pandey (AbbVie), Natasa Tagasovska (Prescient Design, Genentech), and Marinka Zitnik (Harvard). Our invited speakers are Linda Goodman (FaunaBio), Tommi S. Jaakkola (MIT), Rick Stevens (UChicago & Argonne NL), Mengdi Wang (Princeton), Eric Xing (MBZUAI, GenBio, & CMU), Jinbo Xu (TTIC & Molecule Mind), and Alex Zhavoronkov (Insilico Medicine). We will call for papers soon. Look forward to meeting you in San Diego this December!

• May 2025: Happy to receive the SUNY IITG/OER Impact Grant from the State University of New York! Congrats to my colleagues from the Buffalo campus!

• May 2025: I'm co-organizing a session "Preference Learning in Large Language Models" together with Fan Yao (UNC) at INFORMS-2025. I'll also give a talk in the "Bayesian Optimization" session on our recent UAI-2025 work at INFORMS-2025. Look foward to meeting you in Atlanta this fall!

• May 2025: Paper "Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?" is accepted by UAI-2025 conference! In practice, exact acquisition function maximizer can be hardly obtained in Bayesian optimization, and our paper systematically studies this problem in theory. We find a condition under which the performance of GP-UCB and GP-TS can still be no-regret even without exact acquisition function maximization. Congrats to co-authors Hwanwoo Kim (Duke) and Yuxin Chen (UChicago)!

• Apr. 2025: Happy to give a talk in the Department of Computer Science and Engineering at the University at Buffalo!

• Apr. 2025: Flying to Chicago to co-organize the 2025 Midwest AI for Drug Discovery and Development Workshop (AI4D3-Midwest-2025). Happy to meet you in Chicago!

• Mar. 2025: Happy to visit Department of Computer Science and give a talk in the Computer Science Seminar at Rutgers University!

• Mar. 2025: Happy to release a paper preprint "Quantum Non-Linear Bandit Optimization"! In this paper, we propose a new non-linear bandit algorithm that enjoys quantum speed-up!

• Mar. 2025: Invited to review for JMLR and AIJ.

• Feb. 2025: I'll give a talk on our recent AISTATS-2025 paper "Constrained Multi-Objective Bayesian Optimization through Optimistic Constraints Estimation" at the 2025 Information Theory and Applications Workshop (ITA-2025). Happy to meet you in San Diego!

• Feb. 2025: Happy to visit and give a talk at Prescient Design, Genentech in New York City!

• Jan. 2025: Paper "Constrained Multi-Objective Bayesian Optimization through Optimistic Constraints Estimation" is accepted by AISTATS-2025 conference! Many drug discovery problems are focusing on hitting multiple target performance criteria of a certain drug. In this paper, we model it as the constrained MOBO problem and solve it via optimistic constraints estimation. Congrats to co-authors Diantong Li (CUHK), Fengxue Zhang (UChicago), and Yuxin Chen (UChicago)!

• Jan. 2025: Happy to release a paper preprint "No-Regret Linear Bandits under Gap-Adjusted Misspecification"! This paper extends my UAI-2023 paper by proposing a new algorithm to relax a key assumption of it and providing a new gap dependent analysis.

• Jan. 2025: Happy new year! Wrote a blog sharing my computer science faculty job hunting experience in 2022-2023 market. Feel free to share if you found it interesting or helpful!

• Dec. 2024: Invited to serve as an Area Chair of ICML-2025 conference.

• Dec. 2024: Paper "Black-Box Optimization with Implicit Constraints for Public Policy" is accepted by AAAI-2025 AI for Social Impact Track for an oral presentation! In this paper, we apply Bayesian optimization help police departments make decisions on police districting. Congrats to co-authors Wenqian Xing (Stanford), Jungho Lee (CMU), and Shixiang Zhu (CMU)!

• Oct. 2024: Invited to serve as an Area Chair of AISTATS-2025 conference.

• Oct. 2024: I'm co-organizing the "Generative AI for Decision Making" session with Shixiang Zhu (CMU) at INFROMS-2024! Also, I'll give an invited talk at the "Bayesian optimization" session. Happy to meet you in Seattle!

• Oct. 2024: Happy to release a review preprint "Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches"!

• Aug. 2024: Hello Albany, New York! I've moved from Chicago to Albany to start my faculty career!