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VERSION:2.0
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BEGIN:VEVENT
SUMMARY:EPFL AI Center x SDSC - Research Seminar Series - Alhussein Fawzi
DTSTART:20240415T140000
DTEND:20240415T160000
DTSTAMP:20260929T061239Z
UID:fa61ce96a2634b22908dface8480791421a4d7b7bc2ae2ff653e7290
CATEGORIES:Conferences - Seminars
DESCRIPTION:Alhussein Fawzi\nThe workshop is jointly organized by the EPFL
  AI Center and the Swiss Data Science Center (SDSC).\n\nTitle\nMachine lea
 rning for discovering new algorithms and solving mathematical problems\n\n
 Abstract \nIn the modern era of computing\, algorithms for solving fundam
 ental problems such as computing the shortest path in graphs and solving l
 inear equations\, are used billions of times every day. However\, paradoxi
 cally\, such algorithms which are central to modern computing were often d
 esigned well before the advent of modern computation. In fact\, discoverin
 g new efficient algorithms is notoriously difficult\, and often involves s
 olving prohibitively large combinatorial problems. In this talk\, I will d
 escribe how to use machine learning techniques - in particular reinforceme
 nt learning and large language models - to discover new algorithms and sol
 ve long-standing mathematical problems. We will particularly focus on des
 igning new algorithms for fundamental computational problems\, such as mat
 rix multiplication.\n\nBio\nAlhussein Fawzi is a research scientist at Goo
 gle DeepMind. He was awarded the MIT Innovators Under 35 award in 2023 for
  his work on discovering new algorithms with machine learning. He works on
  AI for Science\, and is particularly interested in using Machine Learning
  to unlock new results in Computer Science\, Algorithms\, and Mathematics.
  He recently published two papers in Nature magazine\, where he used Machi
 ne Learning and Large Language Models to discover new algorithms for funda
 mental computational tasks\, and new mathematical results. Prior to workin
 g at Google DeepMind\, he obtained his PhD from EPFL from the signal proce
 ssing laboratory in 2016\, working on the robustness and reliability of ma
 chine learning models.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420 https://epfl.zoom.us/
 j/69274141076
STATUS:CONFIRMED
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