Today we’re joined by Jelani Nelson, a professor in the Theory Group at UC Berkeley.
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In our conversation with Jelani, we explore his research in computational theory, where he focuses on building streaming and sketching algorithms, random projections, and dimensionality reduction. We discuss how Jelani thinks about the balance between the innovation of new algorithms and the performance of existing ones, and some use cases where we’d see his work in action.
Finally, we talk through how his work ties into machine learning, what tools from the theorist’s toolbox he’d suggest all ML practitioners know, and his nonprofit AddisCoder, a 4 week summer program that introduces high-school students to programming and algorithms.
Connect with Jelani Nelson!
- Paper: On Adaptive Distance Estimation
- Paper: Margin-Based Generalization Lower Bounds for Boosted Classifiers
- Paper: Feature Hashing for Large Scale Multitask Learning