Hi! I am an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. I work on making machine learning more reliable, human-compatible and statistically rigorous, and am especially interested in applications in human disease and health. Several of our algorithms are widely used in tech and biotech industries. I received a Ph.D from Harvard in 2014, and was a member of Microsoft Research, a Gates Scholar at Cambridge and a Simons fellow at U.C. Berkeley. I joined Stanford in 2016 and am excited to be an inaugural Chan-Zuckerberg Investigator and the faculty director of the university-wide Stanford Data4Health hub. I'm also a member of the Stanford AI Lab. My research is supported by the Sloan Fellowship, the NSF CAREER Award, and Google, Amazon and Adobe AI awards.
Email: jamesz at stanford dot edu Office: Packard 258
9/22: 7 new NeurIPS papers on: improving SHAP attribution; human-AI collaboration, sparse data shifts, modality gap, mixReg augmentation, SkinCon, and history of ML API shifts.
8/22: Science Advances paper on disparity in skin cancer AI and new Diverse Derm data; Nature Machine Intelligence article on data-centric AI; analysis of 50 years of Stanford research commercialization published in Patterns.
6/22: new Nature Medicine paper on precision cancer treatment.
4/22: Very honored that Trial Pathfinder is selected as a Top Ten Clinical Research Achievement.
monitors ML performance over time; adapt ML to users with gradual performative gradient.
1/22: Honored to be selected as a Chan-Zuckerberg Investigator for the 2nd term.
9/21: New JAMA Dermatology paper quantifies limitations in datasets used for derm AIs.
4/21: Our Nature paper uses real-world data and AI to make clinical trials more inclusive.
4/21: Our Nature Medicine paper identifies limitations in how medical AI are evaluated.
2/21: Honored to receive the Sloan Research Fellowship.
1/21: 5 new papers at 2021 AISTATS and ICLR: how competition over data affects ML; how to use cheap unlabeled data to make models more robust; efficient data Shapley computation; how to delete data from trained predictors; and mixup as regularization.
7/20: Single-cell characterization of aging effects published in Nature.
6/20: Our AI to generate spatial transcriptome from histology is in Nature Biomedical Engineering.
6/20: Excited and honored to received the NSF CAREER Award!
6/20: New papers: statistical data value (ICML), improving dialogue systems (ACL), learning data alignment (ICLR), deep learning for proteomics (J. Proteomics), RNA-GPS (RNA), linking variants to genes (Bioinformatics), and SARS-CoV-2 subcellular localization (Cell Systems).
3/20: Our video AI system to assess heart function is published in Nature.
1/20: Our interactive ML platform is published in Nature Machine Intelligence.
11/19: Our paper on how sex and gender analysis improves science and engineering is in Nature.
9/19: Our papers on deleting data from ML (spotlight) and learning human meaningful concepts will be presented at NeurIPS.
7/19: Our machine learning for genome editing paper is published in Nature Biotechnology.
5/19: AdaFDR won the RECOMB Best Paper. Extended version in Nature Communications.
1/19: Feedback GAN for protein design published in Nature Machine Intelligence.
11/18: Check out our interactive deep learning for genomics primer in Nature Genetics.
9/18: Excited to receive a NIH Center for Excellence in Genomics and a NIH R21.
7/18: Our paper on designing fair AI is published in Nature.
6/18: Honored to receive a Google Faculty Award and a Tencent AI award.
4/18: NLP reveals 100 years of stereotypes is published in PNAS and highlighted in Science.