Uri Shaham

Uri Shaham

Assistant professor with the department of computer science at Bar Ilan university

google-scholar linkedin-icon github-icon gmail-icon telephone-icon

About Me

I am an assistant professor at the department of computer science at Bar Ilan university. I received a Ph.D. in statistics from Yale University in 2017, under the supervision of prof. Ronald Coifman (math), prof. Sahand Negahban (statistics) and prof. Yuval Kluger (computational biology). Between my graduation and 2023, I was an assistant professor adjunct at the center for outcome research and evaluation at Yale university. Before and after my Ph.D. studies I worked for several years in the industry in various research, algorithms design and advisory board roles. Apart of my academic endeavors, until very recently I was also a commander of an infantry battalion in reserves in the Israeli Defense Force, and I am an amateur jazz pianist. When time permits, I also like to run marathons. My full CV is here.

Research

Research interests:

My main area of interest is in developing methods for machine learning. In particular, a primary focus of mine lies in development of unsupervised learning tools for understanding data, its latent properties, learning useful representation for it and extraction of meaningful information from it. During the last ten years, my primary technical playground is deep learning, although I have also performed research in other areas of machine learning. Whenever possible, I aim to base my work on a solid mathematical and theoretical basis, and to contribute to the theoretical understanding of machine learning and deep learning. Alongside my contributions to unsupervised learning, I have also contributed to supervised learning, in both theoretical and practical aspects.

Publications

Accepted Publications:

Pre-prints:

Teaching

students

I am accepting new M.Sc. and Ph.D. students. Prospective students need to have a solid mathematical background, excellent coding skills and a lot of curiosity. They should also be available to work on campus 3 days a week. If you're interested, please email me, tell me about your interests, and attach your CV and transcript.

News

Sparse Binarization for Fast Keyrowd Spotting ” was accepted to Interspeech 2024.

June 2024: Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment ” was accepted to TMLR.

Contact

External linkes: