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Statistics & Data Science Seminar
Data and Deep Learning
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Speaker: Elchanan Mossel (MIT) Professor of Mathematics Massachusetts Institute of Technology Monday, September 14, 2026 3:30PM - 5:00PM 3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13th floor, there will be light snacks and beverages in the kitchen area.
Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=92b0159c-6a07-4ed4-bbf1-b4a400f552b1 |
Abstract: Deep learning challenges much of the traditional thinking in statistics and machine learning, in particular the assumption that data is i.i.d. from families of distributions that are easy to describe. In the first (more scientific) part of the talk I will review some models of hierarchical or correlated data that may explain the need for depth and the efficiency of gradient based methods. In the second (more philosophical) part of the talk I will discuss the importance of data in assessing the extent to which the extraordinary performance of LLMs may be explained by novel reasoning versus plagiarism.
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