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UID:0-1704@hmc.edu
DTSTART;TZID=America/Los_Angeles:20251121T000000
DTEND;TZID=America/Los_Angeles:20251121T000000
DTSTAMP:20251118T234411Z
URL:https://www.hmc.edu/calendar/events/mathematics-or-candidate-research-
 talk-nathan-justin-19-2/
SUMMARY:Mathematics OR Candidate Research Talk\, Nathan Justin ’19
DESCRIPTION:Finalists for a tenure-track faculty position in Operations Res
 earch will present a research talk.\n\nNathan Justin ’19 will discuss 
 “Responsible Machine Learning via Mixed-Integer Optimization.”\n\nAbst
 ract: In the last few decades\, machine learning (ML) has achieved signifi
 cant success across domains ranging from healthcare and sustainability to 
 the social sciences\, criminal justice and finance. But its deployment in 
 increasingly sophisticated and sensitive areas affecting individuals\, the
  groups they belong to and society as a whole raises critical concerns aro
 und fairness\, transparency and robustness\, among others. As the complexi
 ty and scale of ML systems and of the settings in which they are deployed 
 grow\, so does the need for responsible ML methods that address these chal
 lenges while providing reliable performance in practice. Mixed-integer opt
 imization (MIO) offers a powerful framework for embedding responsible ML c
 onsiderations directly into the learning process while maintaining perform
 ance. This talk outlines some of the core principles of responsible ML\, t
 heir importance in applications and the practical utility of MIO for build
 ing ML models that align with these principles. To illustrate these ideas\
 , I will present recent research on how mixed-integer optimization can be 
 used to construct decision trees designed for responsible deployment in hi
 gh-stakes settings\, motivating a variety of future research directions.
CATEGORIES:Alumni,Faculty,Staff,Students
LOCATION:Shanahan Center\, 320 E. Foothill Blvd.\, Claremont\, CA\, 91711\,
  United States
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