[Colloquium] 12/7: Suvrit Sra (MIT) - Geometric Optimization: Old and New

[Rob Mitchum]

*Part of the Computer Science/Statistics/Data Science Institute
Distinguished Speaker Series
<https://datascience.uchicago.edu/news/autumn-2022-distinguished-speaker-series/>*

*Wednesday, December 7th *
*12:00pm - 1:30pm (12:00 lunch, 12:30 talk)*
*In Person: John Crerar Library 390*
*Zoom: *Register
<https://www.eventbrite.ca/e/distinguished-speaker-series-suvrit-sra-mit-tickets-476609982287>
for details

*Suvrit Sra*
*Associate Professor of EECS*
*Massachusetts Institute of Technology*


*Geometric Optimization: Old and New*

*Abstract*: This talk is about non-Euclidean optimization, primarily (but
not only) about problems whose parameters lie on a Riemannian manifold. I
will lay particular emphasis on geodesically convex optimization, a vast
class of non-convex optimization that remains tractable. I will recall some
motivating background and present canonical examples. Starting with our
2016 paper on g-convex optimization, the iteration complexity theory for
g-convex optimization has grown substantially: I will summarize some of the
recent progress including Riemannian acceleration, and saddle-point
problem, Langevin MCMC etc., while noting some key open problems.

*Bio*: Suvrit Sra <https://optml.mit.edu/> is an Associate Professor of
EECS at MIT, and a core member of the Laboratory for Information and
Decision Systems (LIDS), the Institute for Data, Systems, and Society
(IDSS), as well as a member of MIT-ML and Statistics groups. He obtained
his PhD in Computer Science from the University of Texas at Austin. Before
moving to MIT, he was a Senior Research Scientist at the Max Planck
Institute for Intelligent Systems, Tübingen, Germany. His research bridges
mathematical areas such as differential geometry, matrix analysis, convex
analysis, probability theory, and optimization with machine learning. He
founded the OPT (Optimization for Machine Learning) series of workshops,
held from OPT2008–2017 at the NeurIPS (erstwhile NIPS) conference. He has
co-edited a book with the same name (MIT Press, 2011). He is also a
co-founder and chief scientist of macro-eyes, an OPT+ML driven startup with
a focus on intelligent supply chains.



-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*Department of Computer Science*
*Data Science Institute*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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