Uri Shaham

Assistant professor, department of computer science, Bar Ilan university, Israel

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 repesentation learning tools for understanding data, its latent properties, and extraction of meaningful information from it. During the last ten years, my primary technical playground is deep learning, and my group works on a wide range of topics, such as multimodal learning, reinforcement learning, causal inference and generalizable spectral methods. Two current major research interest of mine are multimodal representation learning from unpaired data, and causal reinforcement 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.

Publications

Accepted Publications:

Ben-Ari, Nir, Yacobi, Amitai, and Shaham, Uri. “Generalizable Spectral Embedding with an Application to UMAP”. TMLR 2025.
Kashani, Naama, Cohen, Mira and Shaham, Uri. “P-CAFE: Personalized Cost-Aware Feature Selection for Electronic Health Records”. ECAI 2025.
Yacobi, Amitai , Lindenbaum, Ofir and Shaham, Uri. “Generalizable and Robust Spectral Method for Multi-view Representation Learning”. TMLR 2025.
Katz, Ori , Talmon, Ronen and Shaham, Uri. “Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment”. TMLR 2024.
Svirsky, Jonathan, Shaham, Uri, and Lindenbaum, Ofir. “Sparse Binarization for Fast Keyword Spotting”. Interspeech 2024.
Shaham, Uri, Svirsky, Jonathan, Katz, Ori and Talmon, Ronen. “Discovery of single independent latent variable”. Neurips 2022.
Shaham, Uri, Lindenbaum, Ofir, Svirsky, Jonathan and Kluger, Yuval. "Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features”. Neural Networks 2022.
Lindenbaum, Ofir, Shaham,Uri, Svirski, Jonathan, Peterfreund, Erez, and Kluger, Yuval. “Differentiable Unsupervised Feature Selection based on a Gated Laplacian”. Neurips 2021.
Shaham Uri, Zahavy Tom, Caraballo Cesar, Mahajan Shiwani, Massey Daisy, and Krumholz Harlan. “Learning to Ask Medical Questions using Reinforcement Learning”. Machine Learning in Healthcare 2020.
Katzman, Jared, Shaham, Uri, Cloninger, Alexander, Bates, Jonathan, Jiang, Tingting, and Kluger, Yuval. "DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network". BMC Medical Research Methodology 2018.
Shaham, Uri, and Lederman, Roy. "Learning by Coincidence: Siamese Networks and Common Variable Learning ". Pattern Recognition 2018.
Shaham, Uri, Stanton, Kelly, Li, Henry, Basri, Ronen, Nadler, Boaz, and Kluger, Yuval. “SpectralNet: Spectral Clustering using Deep Neural Networks”. ICLR 2018.
Shaham, Uri, Yamada, Yutaro, and Negahban, Sahand. "Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization". Neurocomputing 2018.
Shaham, Uri, Stanton, Kelly P., Zhao, Jun, Li, Huamin, Raddassi, Khadir, Montgomery, Ruth, and Kluger, Yuval. "Removal of Batch Effects using Distribution-Matching Residual Networks".Bioinformatics 2017.
Li, Huamin, Shaham, Uri, Yao, Yi, Montgomery, Ruth and Kluger, Yuval. "Gating Mass Cytometry Data by Deep Learning”. Bioinformatics 2017.
Mishne, Gal, Shaham, Uri, Cloninger, Alexander, and Cohen, Israel. "Diffusion Nets ". Applied and Computational Harmonic Analysis 2017.
Shaham, Uri, Cloninger Alexander, and Coifman Ronald R. "Provable Approximation Properties for Deep Neural Networks" . Applied and Computational Harmonic Analysis 2016.
Shaham, Uri, Cheng, Xiuyuan, Dror, Omer, Jaffe, Ariel, Nadler, Boaz, Chang, Joseph and Kluger, Yuval. "A Deep Learning Approach to Unsupervised Ensemble Learning".ICML 2016.

Pre-prints:

Shaham, Uri, and Steinberger Stefan. "Stochastic Neighbor Embedding Separates Well-Separated Clusters".
Jiang, Tingting, Shaham, Uri, Parisi, Fabio, Halaban, Ruth, Safonov, Anton, Kluger, Harriett, Weissman, Weismann, Chang, Joseph and Kluger. Yuval. "Methods for detecting co-mutated pathways in cancer samples to inform treatment selection".
Aneja, Sanjay, Shaham, and Krumholz, Harlan. “Deep Neural Network to Predict Local Failure Following Stereotactic Body Radiation Therapy: Integrating Imaging and Clinical Data to Predict Outcomes”.
Shaham, Uri, Garritano, Jim, Yamada, Yutaro, Weinberger, Ethan, Cloninger, Alex, Cheng, Xiuyuan, Stanton, Kelly and Kluger, Yuval. “Defending against Adversarial Attacks using Basis Functions Transformations”.
Shaham, Uri. “Batch Effect Removal via Batch Free Encoding”.
Au, Benjamin, Shaham, Uri, Dhruva, Sanket, Bouras, Georgios, Cristea, Ecaterina, Coppi, Andreas, Warner, Fred, Li, Shu-Xia, and Krumholz, Harlan. ”Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks”.
Shaham, Uri and Svirsky, Jonathan. “Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities”.

Teaching

Seminar on representation learning (Fall 2024)
Mathematical methods for data science (Fall 2024)
Intro to machine learning (Spring 2025)

students

Ph.D. students

Tal Fiskus

M.Sc. students

past students

A note to prospective students

Are you curious about what data can tell us? Are you humble and eager to learn? Do you enjoy challenging yourself? Are you mentally strong enough to continue trying where most others give up? do you belong to the top of your class in math? are you a very good programmer? Are you willing to make a trip to the edge of your capabilities and then extend them further? I am accepting a few M.Sc. and Ph.D. students. Prospective students should be available for full-time research, and will be required to work on campus at least 3 days a week. If you're interested, please email me, tell me about your interests, and attach your CV and transcript.

News

April 2025: "Generalizable and Robust Spectral Method for Multi-view Representation Learning ” was accepted to TMLR. Kudos to Amitai Yacobi!

July 2025: “P-CAFE: Personalized Cost-Aware Feature Selection or Electronic Health Records ” was accepted to ECAI 2025. Kudos to Naama and Mira!

July 2025: “Generalizable Spectral Embedding with an Application to UMAP ” was accepted to TMLR. Kudos to Nir and Amitai!

Contact

Phone: 03-11111111
Email: uri.shaham@biu.ac.il