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 a wide range of problems in machine learning (from proving mathematical properties to building large-scale algorithms) and am especially interested in applications in biotech and health. I received a Ph.D from Harvard in 2014, and was at one time a member of Microsoft Research, a Gates Scholar at the University of 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 AI for Health program. We are also a part of the Stanford AI Lab. My research is supported by the NSF CAREER Award, and the Google and Tencent AI awards.
Email: jamesz at stanford dot edu Office: Packard 258
News
9/26: FrugalML, Neuron Shapley and MOPO are accepted at NeurIPS. FrugalML selected for oral presentation as top 1% of submissions.
7/15: 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.
5/19: At ICML we'll present papers on data valuation, concrete autoencoder, conditional features and adaptive Monte Carlo.
4/19: Check out our two knockoff papers in AISTATS.
2/19: Interpretation of neural network is fragile in AAAI and VetTag in Nature Digital Medicine.
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.