TITLE
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 at one time 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 AI for Health program. We are also a part of the Stanford AI Lab. My research is supported by the Sloan Fellowship, the NSF CAREER Award, and Google, Amazon and Tencent AI awards.
Email: jamesz at stanford dot edu Office: Packard 258
News
6/21: New Nature Biotech paper uses patent citations to quantify research translational impact. Study of GPT-3 biases published in Nature Machine Intelligence.
5/21: ICML papers on performative gradient descent and task augmentation for meta-learning.
4/21: BABEL published in PNAS and new single-cell aging score published in eLife.
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.
10/20: TrueImage improves photo quality for telehealth (PSB paper). ALICE shows how to use natural language explanation of contrasts to efficiently teach ML (EMNLP paper).
9/20: FrugalML, Neuron Shapley and MOPO are accepted at NeurIPS. FrugalML selected for oral presentation as top 1% of submissions.
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.
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.