I am a Master’s student in Computer Science at Northwestern University, where I am fortunate to be advised by Prof. Manling Li and mentored by Qineng Wang. My research interests lie in embodied AI and AI for Science. I also work closely with, and am advised by, 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 collaborate closely with Prof. David Demeter and Prof. Han Liu at Northwestern University. I feel very grateful to have the opportunity to work with such supportive 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.
Research Experience
- Designed benchmark tasks and evaluation protocols for embodied and image-based settings, built reproducible evaluation pipelines, and analyzed VLM reasoning, robustness, and planning behavior across diverse visual tasks.
- 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.
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