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UID:823@fds.yale.edu
DTSTART;TZID=America/New_York:20241218T130000
DTEND;TZID=America/New_York:20241218T140000
DTSTAMP:20250916T142146Z
URL:https://fds.yale.edu/events/fds-seminar-aram-alexandre-pooladian-nyu/
SUMMARY:FDS Seminar: Aram-Alexandre Pooladian (NYU)
DESCRIPTION:\n"Large-scale probabilistic inference via optimal transport"\n
 \n\n\nAbstract: Many contemporary challenges in probabilistic inference b
 oil down to understanding how measures evolve. For generative models or in
  trajectory inference\, we ask how samples\, representing distributions\, 
 transform over time. When optimizing a functional over the space of measur
 es\, the optimization iterations themselves concern the transformations of
  measures. In this talk\, I will use optimal transport (OT) as a unifying 
 framework to address these challenges in data science. Part I is about opt
 imization guarantees via OT\, and Part II surrounds statistical guarantees
  for generative modeling. This is joint work with Roger Jiang (NYU)\, Sinh
 o Chewi (IAS --> Yale)\, and Jonathan Niles-Weed (NYU).\n\n\n\nBio:&nbsp\;
 Aram-Alexandre Pooladian is a PhD Candidate at New York University where h
 e is supervised by Jonathan Niles-Weed. His research incorporates optimal 
 transport as a tool for understanding the mathematics of data science and 
 for large-scale applications in machine learning. His research is funded b
 y the National Science Foundation\, the National Science and Engineering C
 ouncil of Canada\, Google\, and Meta AI. His research has received a Best 
 Paper Award from NeurIPS -- Optimal Transport and Machine Learning.\n
CATEGORIES:FDS Events,Postdoctoral Applicants
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