{"id":55798,"date":"2026-02-25T11:09:20","date_gmt":"2026-02-25T16:09:20","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=55798"},"modified":"2026-02-25T11:09:20","modified_gmt":"2026-02-25T16:09:20","slug":"ams-special-seminar-series-eitan-levin","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-special-seminar-series-eitan-levin\/","title":{"rendered":"AMS Special Seminar Series | Eitan Levin"},"content":{"rendered":"

Location: <\/strong>Wyman N425<\/p>\n

When:<\/strong> March 4th at 12:15 p.m.<\/p>\n

Title: <\/strong>Any-Dimensional Data Science<\/span><\/p>\n

Abstract<\/strong>: Many applications throughout data science require methods that are well-defined and performant for problems or data of any size.\u00a0 In machine learning, we are given training data from which we wish to learn algorithms capable of solving problems of any size.\u00a0 In particular, the learned algorithm must generalize to inputs of sizes that are not present in the training set.\u00a0 For example, algorithms for processing graphs or point clouds must generalize to inputs with any number of nodes or points.\u00a0 A second challenge pertaining to any-dimensionality arises in applications such as game theory or network statistics in which we wish to characterize solutions to problems of growing size.\u00a0 Examples include computing values of games with any number of players, or proving moment inequalities for random vectors and graphs of any size.\u00a0 From an optimization perspective, this amounts to deriving bounds that hold for entire sequences of problems of growing dimensionality.\u00a0 Finally, in applications involving graph-valued data, we wish to produce constant-sized summaries of arbitrarily-large networks that preserve their essential structural properties. \u00a0These summaries can then be used for efficiently testing properties of the underlying large network, e.g., testing for the presence of hubs is of interest in massive biological and traffic networks.\u00a0 We develop a unified framework to tackle such any-dimensional problems by using random sampling maps to compare and summarize objects of different sizes.\u00a0 Our methodology leverages new de Finetti-type theorems and the recently-identified phenomenon of representation stability.\u00a0 We illustrate the resulting framework for any-dimensional problems in several applications.\u00a0<\/span><\/span><\/p>\n

<\/span>Bio:<\/strong> Eitan Levin is a graduate student in Applied and Computational Mathematics at Caltech. \u00a0His research interests lie in the mathematics of data science, with a focus on developing practically-relevant methodology by leveraging ideas from optimization, probability, algebra, and combinatorics. \u00a0He has received the INFORMS Optimization Society Student Paper Prize (2024) and the Thomas A. Tisch prize for graduate teaching in Computing and Mathematical Sciences at Caltech (2022).\u00a0 Previously, he received an undergraduate degree in Mathematics from Princeton University.<\/span><\/p>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/92215066845<\/p>\n","protected":false},"excerpt":{"rendered":"

Location: Wyman N425 When: March 4th at 12:15 p.m. Title: Any-Dimensional Data Science Abstract: Many applications throughout data science require methods that are well-defined and performant for problems or 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