Research

Preprints

• George Henderson*, Adam Gudys*, Tavor Z. Baharav, Punit Sundaramurthy, Marek Kokot, Peter L. Wang, Sebastian Deorowicz, Allison Carey, Julia Salzman. “Ultra-efficient, unified discovery from microbial sequencing with SPLASH and precise statistical assembly”, 2024.

• Marek Kokot*, Roozbeh Dehghannasiri*, Tavor Z. Baharav, Julia Salzman, Sebastian Deorowicz. “NOMAD2 provides ultra-efficient, scalable, and unsupervised discovery on raw sequencing reads”, 2023. [code]

• Roozbeh Dehghannasiri*, George Henderson*, Rob Bierman, Kaitlin Chaung, Tavor Z. Baharav, Peter Wang, Julia Salzman. “Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells”, 2022.

Journal Papers

• Tavor Z. Baharav, David Tse, Julia Salzman. “OASIS: An interpretable, finite sample valid alternative to Pearson's X2 for scientific discovery”. Proceedings of the National Academy of Sciences (PNAS) direct submission, 2024. [code]

• Kaitlin Chaung*, Tavor Z. Baharav*, George Henderson, Ivan Zheludev, Peter L. Wang, Julia Salzman. “SPLASH: A statistical, reference-free genomic algorithm unifies biological discovery”. Cell, 2023. [code], [updated codebase]

• Tavor Z. Baharav, Tze Leung Lai, “Adaptive Data Depth via Multi-Armed Bandits”. Journal of Machine Learning Research (JMLR), 2023. [code]

• Vivek Bagaria*, Tavor Z. Baharav*, Govinda M. Kamath*, David N. Tse, “Bandit-Based Monte Carlo Optimization for Nearest Neighbors”. IEEE Journal on Selected Areas in Information Theory, 2021.

• Ilai Bistritz, Tavor Z. Baharav, Amir Leshem, Nicholas Bambos, “One for All and All for One: Distributed Learning of Fair Allocations with Multi-player Bandits”. IEEE Journal on Selected Areas in Information Theory, 2021.

• Tavor Z. Baharav*, Govinda M. Kamath*, David N. Tse, Ilan Shomorony, “Spectral Jaccard Similarity: A new approach to estimating pairwise sequence alignments”. Cell Patterns, 2020.

Conference Papers

• Yifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao, David Tse. “Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits”. Advances in Neural Information Processing Systems (NeurIPS), 2022.

• Tavor Z. Baharav, Gary Cheng, Mert Pilanci, David Tse. “Approximate Function Evaluation via Multi-Armed Bandits”. International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

• Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, and Inderjit S. Dhillon. “Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification”. ACM International Conference on Information and Knowledge Management (CIKM), 2021; oral presentation.

• Govinda M. Kamath*, Tavor Z. Baharav*, Ilan Shomorony, “Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment”. Advances in Neural Information Processing Systems (NeurIPS), 2020. [code]

• Ilai Bistritz, Tavor Z. Baharav, Amir Leshem, Nicholas Bambos, “My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits”. International Conference on Machine Learning (ICML), 2020.

• Tavor Z. Baharav*, Govinda M. Kamath*, David N. Tse, Ilan Shomorony, “Spectral Jaccard Similarity: A new approach to estimating pairwise sequence alignments”. International Conference on Research in Computational Molecular Biology (RECOMB), 2020. [code]

• Tavor Z. Baharav, David N. Tse, “Ultra Fast Medoid Identification via Correlated Sequential Halving”. Advances in Neural Information Processing Systems (NeurIPS), 2019. [Poster] [code].

• Tavor Baharav, Kangwook Lee, Orhan Ocal, Kannan Ramchandran, “Straggler-proofing massive-scale distributed matrix multiplication with d-dimensional product codes”. International Symposium on Information Theory (ISIT), 2018.

• Tavor Baharav, Mohini Bariya, Avideh Zakhor, “In Situ Height and Width Estimation of Sorghum Plants from 2.5d Infrared Images”. Electronic Imaging (EI) 2017.

Miscellaneous

Presentations and talks

• “A statistical reference-free genomic algorithm subsumes common workflows and enables novel discovery.” Cold Spring Harbor Laboratory: Biological Data Science meeting 2022. Platform presentation.

• “Bandit-based Monte Carlo Optimization.” Cornell ORIE Young Researcher's workshop 2021. Poster.

• “Bandit-based Monte Carlo Optimization for Nearest Neighbors.” Baylearn 2020 (Symposium). Poster.

• “Adaptive Monte Carlo Optimization: Ultra Fast Medoid Identification via Correlated Sequential Halving.” Baylearn 2019 (Symposium). Poster.

• “DAMN fast: DNA Alignment using Multi-armed baNdits: Spectral Jaccard Similarity for long-read alignment.” Intelligent Systems for Molecular Biology (ISMB/ECCB 2019). Poster.

• “Ultra Fast Medoid Identification via Correlated Sequential Halving.” 2019 North American School of Information Theory (School). Poster.

Teaching

• Information Theory (Stanford, EE276/Stats376a): Winter 2021-2022

• Probability & Random Processes (Berkeley EECS 126): TA in Spring 2017, Head TA in Spring 2018

• Discrete Math and Probability, Efficient Algorithms and Intractable Problems (Berkeley CS70 and CS170): reader in Fall 2015, Spring 2016 respectively