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Machine Learning
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Jenna Wiens on “Careers in Machine Learning and Healthcare”
About the event: Join Jenna Wiens, PhD, Professor of Computer Science and Engineering at the University of Michigan, for a conversation about building a career at the intersection of machine learning and healthcare. Dr. Wiens leads the Machine Learning for Data-Driven Decisions research group, working with clinicians to develop methods that support clinical decision-making. The…
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FDS Colloquium: Purnamrita Sarkar (UT Austin), “The Surprising Power of Simple Algorithms for Sparse PCA”
Talk description: Sparse principal component analysis (PCA) is a fundamental problem in high-dimensional statistics, where the goal is to estimate a leading eigenvector of a covariance matrix under a sparsity constraint. Despite its statistical appeal, sparse PCA is computationally challenging, and existing efficient algorithms often require restrictive assumptions on the covariance matrix or a good…
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FDS Workshop: AI for Social Science Research Methods
Social scientists are increasingly incorporating AI into their designs, data collection, analyses, and workflows. Alongside rapid adoption, important methodological questions remain: What are the principled approaches to validating measurements via AI tools? To what extent are AI-generated observations interchangeable with those from human respondents — and what does that mean for the future of survey…
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FDS Seminar: Lingjing Kong (CMU), “Causal AI for Transferable, Interpretable, and Controllable Machine Learning”
Zoom Password: 123 Abstract: Foundation models are rapidly becoming capable assistants for knowledge work, but their deployment in real settings is limited by three gaps: they do not transfer reliably across environments, their internal reasoning is opaque, and their behavior is hard to precisely control. In this talk, I argue that these limitations are not…
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FDS Colloquium: Cynthia Rudin (Duke), “Many Good Models Leads To…”
Abstract: As it turns out, many good models leads to amazing things! The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, but mostly magic. In…
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Lu Lu on MIT Technology Review’s Innovators Under 35 list
We are proud to announce that Prof. Lu Lu has been named to the MIT Technology Review’s Innovators Under 35 list for the Asia Pacific region. This award recognizes his groundbreaking work in operator learning and significantly improved accuracy, efficiency, and generalization ability of the model in specific fields. You can learn more about this…