James Zou

James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards.

Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley.

Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making.

Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature, Science, and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine.

Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.