 Assistant Professor Stanford University Department of Biomedical Data Science Department of Computer Science Department of Electrical Engineering Email: jamesyzou at gmail dot com Office: MSOB X325, Packard 253 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 deploying large-scale algorithms) and am especially interested in applications in genomics and computational health. I received a Ph.D. from Harvard in 2014 and was a member of Microsoft Research New England. Before this, I completed Part III in math at the University of Cambridge and was a Simons fellow at U.C. Berkeley. I joined Stanford in Fall 2016 and am excited to be an inaugural Chan-Zuckerberg Investigator. I lead the Stanford Laboratory for Machine Learning, Genomics and Health. We are also a part of the Stanford AI Lab.
Open postdoc positions in both ML and compbio: please send CV and a cover letter of research interest to my email above.
NEWS 5/31/18: Look for CoVeR at ICML! 5/3/18: contrastive PCA is published in Nature Communications and we have received a Human centered AI grant. 4/8/18: Word embedding reveals 100 years of stereotypes is published in PNAS and is highlighted in Science and Stanford News. 3/13/18: we have won a Tencent AI research award! 2/23/18: we have won a Google Faculty Research Award! 2/15/18: Machine learning reveals malaria plastid proteome. See our new paper. 1/16/18: Why does adaptively collected data have negative bias? See our AISTATS paper. 11/6/17: Interpretation of neural network is fragile. See our paper for gory details. 10/19/17: Our paper on the effects of memory replay in reinforcement learning won Best Poster at Bay Area ML Symposium. 10/17/17: We have been selected to be a pilot project for the Human Cell Atlas. 9/26/17: Have you used PCA? Then you need to use our contrastive PCA. 9/6/17: Deep learning for multiple hypothesis testing? Check out our new paper on NeuralFDR to appear in NIPS 2017. 7/19/17: our bias in adaptive data collection paper wins Best Paper Award! 7/17/17: our multi-sense word embedding paper won Best Paper Award! 7/17/17: excited to participate in the Microsoft AI Faculty Summit. 5/24/17: talk at UCLA Computational Genomics Summer Institute. 5/24/17: two new papers in ICML 2017. 3/13/17: I will give a deep learning short course at Northwestern University. 3/5/17: We have been awarded a NSF research initiative (CRII) award! 1/20/17: Invited talks at Berkeley and U Penn. 9/21/16: I'm co-organizing Machine Learning in Compbio workshop at NIPS. Please submit your awesome papers! 9/20/16: Excited to teach CS273B: Deep learning for genomics and bio-medicine. 9/16/16: Our NIPS paper on gender stereotype in word embedding is covered in NPR and MIT TechReview. 9/12/16: What can we say about unobserved mutations? Check out our UnseenEst paper (to appear in Nature Communications). 9/10/16: How good is your approximate diffusion? Check out our new paper (w/ Jonathan Huggins). |
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