Probably Approximately Correct Learners

Chara Podimata
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Welcome to Probably Approximately Correct Learners, a podcast from the Learning Theory Alliance team. In this podcast, we will dive deep into the minds of leading researchers in Machine Learning! Join us for engaging interviews that explore a diverse range of topics—from groundbreaking research findings to the experiences and insights that shape life beyond academia. Whether you're a seasoned expert or just starting your journey in the field, this podcast is your gateway to understanding the evolving landscape of Machine Learning. Tune in and broaden your perspective with each episode!

Episodes

  1. SEP 12

    Ep. 3: Sam Hopkins

    Welcome to Probably Approximately Correct Learners Episode 3! In this episode, Chara chats with Professor Sam Hopkins. Sam is a theoretical computer scientist and Assistant Professor at MIT, in the Theory of Computing group in the Department of Electrical Engineering and Computer Science, where he holds the Jamieson Career Development Chair. His interests include algorithms, theory of machine learning, semidefinite programming, sum of squares method, and bicycles. Before MIT, he was a Miller fellow in the theory of computing group at UC Berkeley, hosted by Prasad Raghavendra and Luca Trevisan. Before that, he got his PhD at Cornell, advised by David Steurer.

    1h 1m

  2. JUL 7

    Ep. 2: Clément Canonne

    Welcome to our second Probably Approximately Correct Learners episode! In this episode, Chara chats with Professor Clément Canonne. Clément Canonne is a Senior Lecturer in the School of Computer Science of the University of Sydney, an ARC DECRA Fellow, and a 2023 NSW Young Tall Poppy. He obtained his Ph.D. in 2017 from Columbia University, before joining Stanford as a Motwani Postdoctoral Fellow, then IBM Research as a Goldstine Postdoctoral Fellow. His research interests span distribution testing and learning theory; focusing, in particular, on differential privacy, and the computational aspects of learning and statistical inference subject to resource or information constraints. He really likes elephants and wombats.

    40 min

  3. 08/13/2024

    Ep. 1: Jamie Morgenstern

    Welcome to our first ever Probably Approximately Correct Learners episode! In this episode, Chara chats with Professor Jamie Morgenstern (UW). Jamie is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She was previously an assistant professor in the School of Computer Science at Georgia Tech. Prior to starting as faculty, she was hosted by Michael Kearns, Aaron Roth, and Rakesh Vohra as a Warren Center fellow at the University of Pennsylvania. She completed her PhD working with Avrim Blum at Carnegie Mellon University. She studies the social impact of machine learning and the impact of social behavior on ML's guarantees. For example, how should machine learning be made robust to behavior of the people generating training or test data for it? And, how should ensure that the models we design do not exacerbate inequalities already present in society? You can find more information about Jamie and her research on her website: https://jamiemorgenstern.com/. Jamie and Chara talked about this paper: https://arxiv.org/abs/2206.02667.

    43 min
  • 3 Episodes

About

Welcome to Probably Approximately Correct Learners, a podcast from the Learning Theory Alliance team. In this podcast, we will dive deep into the minds of leading researchers in Machine Learning! Join us for engaging interviews that explore a diverse range of topics—from groundbreaking research findings to the experiences and insights that shape life beyond academia. Whether you're a seasoned expert or just starting your journey in the field, this podcast is your gateway to understanding the evolving landscape of Machine Learning. Tune in and broaden your perspective with each episode!

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