Auditable AI Systems for Biomedicine
I am a Postdoctoral Researcher at Stanford University, working with Prof. James Zou and Prof. Lei Xing. I earned my PhD in Data Science from New York University, where I was fortunate to work with Carlos Fernandez-Granda, Narges Razavian, and Kyunghyun Cho.
My research builds reliable and auditable AI systems that can reason, adapt, and act in complex real-world environments. I develop methods for robust learning, controllable inference, and agentic AI, with a particular focus on biomedicine—where I am interested in turning heterogeneous evidence into trustworthy decisions, scientific discoveries, and ultimately better paths to treatment.
Outside of academia, I play tennis and am a certified scuba diver and surfer. Some of that lives on the gallery page.
Latest News
View allOrganizing a workshop on AI and healthcare at ICCV 2025 in Hawaii.
Co-authored work on a trainable multi-agent system is online — over 900 stars on GitHub.
Work on unsupervised manifold learning from dynamic brain data accepted at Nature Computational Science.
Paper on reasoning and hallucination in MLLMs accepted at NeurIPS 2025.
Invited talk at the Symposium on AI Medicine, Stanford University.
From Autopilot to Copilot
Robustness
Handle noisy, imperfect data, and bias — learning that stays reliable when the data is not.
Steerability
Adapt to feedback and evolving context at inference time, without retraining.
Agency
Reason and act on complex, real-world tasks as a collaborative partner.
Interested in collaboration?
We are actively seeking students and collaborators with a background in machine learning, large foundation models, AI agents, or AI for medicine.
Send an Inquiry