James Zou     

Assistant Professor
Stanford University

Department of Biomedical Data Science
Department of Computer Science
Department of Electrical Engineering

Email: jamesyzou at gmail dot com      Office: Littlefield 340, 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 designing new models and algorithms) and am especially interested in applications in human genomics. I received my Ph.D. from Harvard University in 2014 and was fortunate to be a member of Microsoft Research New England. Before this, I completed Part III in Mathematics 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
If you are interested in chatting about research, please get in touch!        James Zou                                                                                                                                                                                       

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).