Tavor Baharav
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Postdoctoral fellow
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Recent News
Excited to announce I'll be joining Johns Hopkins University as an assistant professor in Biomedical Engineering in January 2027, with a secondary appointment in Computer Science!
“Unique molecular identifiers don't need to be unique” presented at the RECOMB-seq 2026 proceedings track.
“The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification” presented at AISTATS 2026 (spotlight, top 3% of submissions).
“Stacked SVD or SVD stacked?” received the Best Poster award at the Conference on Statistics in Genomics and Genetics (STATGEN 2026).
Brief Bio
I am a third-year postdoctoral fellow at the Eric and Wendy Schmidt Center at the Broad Institute, working with Professor Rafael Irizarry. I will be starting as an assistant professor in Biomedical Engineering at Johns Hopkins University in January 2027, with a secondary appointment in Computer Science, affiliated with the Data Science and AI institute (DSAI).
My research develops machine learning methods for computational genomics that co-design the full data science pipeline, from data collection through measurement system design to statistical inference, rather than optimizing these stages in isolation. Through first-principles probabilistic modeling, I develop algorithms that achieve both rigorous theoretical guarantees and practical efficiency by adapting to problem structure: learning which data to collect, how to allocate computation, and how to account for upstream design choices in downstream inference.
This approach spans diverse applications: online learning, adaptive randomized algorithms for data-science primitives, and reference-free genomic inference. My recent work has focused on fundamental theoretical questions in data integration, and practical applications in T-cell repertoire analysis through close collaboration with experimental biologists and clinical researchers (immunology). More broadly, I work to identify problems in computational genomics, and leverage tools from machine learning, statistics, and optimization to characterize their fundamental theoretical limits and develop statistical methods and algorithms that provably achieve them. Feel free to reach out if any of these topics interest you; I'm always happy to chat!
In 2023 I completed my Ph.D. in Electrical Engineering at Stanford University, where I worked with David Tse and Julia Salzman. At Stanford, I was gratefully supported by the NSF Graduate Research Fellowship and the Stanford Graduate Fellowship (SGF). Previously, I completed my undergraduate studies in EECS at UC Berkeley, where I was fortunate to have the opportunity to work with Kannan Ramchandran on coding theory and its applications to distributed computing.
Disambiguation
I am not the only Baharav working in EECS in the Boston area! If you're interested in computational social choice, you may be looking for my sister Carmel Baharav who works down the street.
Contact
Email: “last name” at broadinstitute dot org
Room M1115, 75 Ames St, Cambridge, MA 02142