Tavor Baharav
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Postdoctoral fellow |
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 am on the 2025-26 academic job market; please reach out if you think my work would be a good fit for your department!
My research develops machine learning methods 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 computational 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 accelerating data-science tasks like clustering (leveraging techniques from multi-armed bandits), and reference-free genomic inference. I am currently applying these methods to problems in computational genomics, where I work 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). 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.
Contact
Email: “last name” at broadinstitute dot org
Room M1115, 75 Ames St, Cambridge, MA 02142