Anbang Liu (Andrew)
Master of Computer Science
Northwestern University
Hi! I am Anbang (Andrew) Liu, a Master’s student in Computer Science at Northwestern University, where I am very fortunate to be advised by Prof. Manling Li and mentored by Qineng Wang. My research interests span embodied AI and AI for Science. I am also grateful to work closely with Prof. Siwei Chen and Prof. Junhan Zhao at the University of Chicago, under the mentorship of Zechuan Zhang and Zongxin Yang, on research in AI for Science. In addition, I have had the privilege of collaborating with Prof. David Demeter and Prof. Han Liu at Northwestern University. I feel incredibly thankful for the guidance, support, and encouragement I have received from all of these mentors and collaborators. Before joining Northwestern, I earned my bachelor’s degree in Computer Science from the University of San Francisco. Please feel free to reach out via email!
Education
Research Interests
My research interests span embodied AI and AI for Science, with a particular focus on building reliable, interpretable, and practically useful intelligent systems.
- In embodied AI, I am interested in VLM evaluation, world modeling, spatial intelligence, reasoning and planning, and multi-agent systems, especially in understanding how agents perceive, reason about, and interact with complex environments.
- In AI for Science, I am interested in computational biology, medical AI, and interpretability, with the goal of developing machine learning methods that can support scientific discovery and real-world biomedical applications.
More broadly, I am excited by research that connects foundation models with reasoning, decision-making, and high-impact applications across both physical and scientific domains.
News
- Jun 2026. 🎉 AI in Medicinal Chemistry and Translational Drug Development was accepted to Chemical Society Reviews!
Research Experience
- Designed and implemented 7 Gym-compatible benchmark environments for embodied and image-based VLM evaluation, defining task protocols and reproducible pipelines to assess reasoning, robustness, and planning across diverse visual scenarios.
- Used the VAGEN framework to connect vision-based Gym environments with SFT/RL training pipelines, trained models with RL algorithms, and evaluated pre- and post-training accuracy changes across task settings.
- Designed a human annotation interface and coordinated the external annotation workflow, including guideline preparation, annotation quality review, and response analysis to validate benchmark quality and characterize model failure modes.
- Developed a sequence-based machine learning framework for biomolecular interaction modeling with downstream interpretability analysis for biological investigation.
- Conducted multimodal medical AI research on clinically relevant imaging and related data, developing and evaluating machine learning models for predictive tasks.
- Trained and deployed robotic control policies for manipulation tasks, collected teleoperated demonstrations for image-conditioned offline learning, and evaluated policy performance in simulation and real-world settings for task success, generalization, and sim-to-real transfer.
- Developed a data-centric vision pipeline for self-driving laboratories that integrated automatically collected real-world data with validated synthetic images for robust bubble detection in precision-sensitive pipetting.
Publications
Learning Residue-Level Context for Modeling Protein-Protein Interactions
Zechuan Zhang*, Zongxin Yang*, Anbang Liu*, Kun-Hsing Yu, Junhan Zhao, Yi Yang†, Benjamin Neale†, Siwei Chen†
Under Review at Nature Methods.
MindTopo: Can Foundation Models Reason in Topological Space?
Yunfei Ge*, Anbang Liu*, Qineng Wang*†, Johnalbert Garnica*, Jianwen Lyu, Zihan Wang, Reuben Tan, Jianfeng Gao, Ruohan Zhang, Yining Hong, Jiajun Wu, Manling Li
Submitted to NeurIPS.
AI in Medicinal Chemistry and Translational Drug Development
Kaicheng U†, Sophia Meixuan Zhang†, Ziyu Yu†, Zechuan Zhang, Jianwei Zhang, Chang He, Anbang Liu, Ricki Chen, Stella Wang, Lijie Yan, Shichao Ding, Lavonda Li, Zongxin Yang, Gao Xiao, Xushuai Zhang, Han Wang, Haohan Wang, Athanasios V. Vasilakos*, Junhan Zhao*, Siwei Chen*, Xingcai Zhang*
Accepted by Chemical Society Reviews.
Teaching
Northwestern University
Evanston, IL
Peer Mentor for CS449(Deep Learning), CS461(Deep Learning for NLP)
Industry
Felloc
Seattle, WA
Software Development Engineer
University of San Francisco
San Francisco, CA
USFMobile Usability and Functional Tester
Web Development Engineer