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!

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  1. 6월 24일

    Ep. 5: Hamsa Bastani

    Welcome to another episode of Probably Approximately Correct Learners! This time, I'm joined by Hamsa Bastani. Hamsa is an Associate Professor of Operations, Information, and Decisions (OID) and Statistics and Data Science at the Wharton School of the University of Pennsylvania, where she co-directs the Wharton Healthcare Analytics Lab. Her research sits at the intersection of machine learning, operations research, and economics. She studies how to design, deploy, and evaluate AI systems that empower human decision-makers and improve societal outcomes. She aims to combine methodological depth with implementation in consequential environments. She has worked with national governments to deploy algorithms at the country scale for targeted border COVID-19 screening and essential medicine access, and she co-led one of the first large field studies of generative AI tutors in high school mathematics. She studies both the mathematical properties of algorithms and the way people respond to them. Her research has been published in leading outlets including Nature, Management Science, Operations Research, and PNAS, and has garnered numerous recognitions, including the Wagner Prize for Excellence in Operations Research, the INFORMS Pierskalla Award for best healthcare paper, and the George Nicholson Prize. Previously, she graduated summa cum laude from Harvard in 2012 with an A.M. in physics and an A.B. in physics and mathematics, completed her PhD in Stanford's Electrical Engineering department under the supervision of Mohsen Bayati, and spent a year as a Herman Goldstine postdoctoral fellow at IBM Research. Outside academia, she serves on the Workday AI Advisory Board.

    56분

  2. 4월 2일

    Ep. 4: Nicole Immorlica

    Welcome to Probably Approximately Correct Learners, episode 4! In this episode, Chara chats with Prof. Nicole Immorlica. Nicole Immorlica is a Professor of Computer Science at Yale University and a Researcher at Microsoft.  She received her BS in 2000, MEng in 2001 and PhD in 2005 in theoretical computer science from MIT in Cambridge, MA.  She joined MSR NE in 2012 after completing postdocs at Microsoft in Redmond, WA and Centruum vor Wiskunde en Informatics (CWI) in Amsterdam, Netherlands, and a professorship in computer science at Northwestern University.  Nicole’s research interest is in the design and operation of sociotechnical systems. Using tools and modeling concepts from both theoretical computer science and economics, Nicole hopes to explain, predict, and shape behavioral patterns in various online and offline systems, markets, and games. She is known for her work on social networks, matching markets, and mechanism design.  She is the recipient of a number of fellowships and awards including ACM Fellow, the Sloan Fellowship, the Microsoft Faculty Fellowship and the NSF CAREER Award.  She has been on several boards including SIGecom, SIGACT, the Game Theory Society, and OneChronos; is an associate editor of Operations Research and Transactions on Economics and Computation, and was program committee member and chair for several ACM, IEEE and INFORMS conferences in her area. Nicole and I talked about this paper: https://arxiv.org/pdf/2502.20783.

    47분

  3. 2025. 09. 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.

    1시간 1분

  4. 2025. 07. 07.

    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.

    40분

  5. 2024. 08. 13.

    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.

    43분
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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!

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