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 NSF CAREER Award, and the Google and Tencent AI awards

Email: jamesz at stanford dot edu                   Office: Packard 258


10/6: TrueImage improves photo quality for telehealth (PSB paper). ALICE shows how to use natural language explanation of contrasts to efficiently teach ML (EMNLP paper).    

9/26: 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 valuationconcrete autoencoderconditional 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. 

© 2019 James Zou