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Behind the scenes with the #INFORMS2024 Plenaries: AI Fireside Chat
Manage episode 446190818 series 2422598
We’re continuing our countdown to the 2024 INFORMS Annual Meeting, in Seattle, Washington, October 20-23, when more than 6,000 INFORMS members, students, prospective employers and employees, and academic and industry experts will share the ways O.R. and analytics are fueling Smarter Decisions for a Better World.
In this episode, I’m joined by Cynthia Rudin, with Duke University, Charles Isbell, with the University of Wisconsin, and Michel Littman, with the National Science Foundation, for a preview of their plenary session, “Making the Most of this AI Moment: A Fireside Chat.”
Charles serves as the provost and vice chancellor for academic affairs with the UW-Madison, and Michael is the division director for information and intelligent systems at NSF.
Cynthia, the session moderator, is a longtime INFORMS member and the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, as well as heads the Interpretable Machine Learning Lab.
105 episodes
Manage episode 446190818 series 2422598
We’re continuing our countdown to the 2024 INFORMS Annual Meeting, in Seattle, Washington, October 20-23, when more than 6,000 INFORMS members, students, prospective employers and employees, and academic and industry experts will share the ways O.R. and analytics are fueling Smarter Decisions for a Better World.
In this episode, I’m joined by Cynthia Rudin, with Duke University, Charles Isbell, with the University of Wisconsin, and Michel Littman, with the National Science Foundation, for a preview of their plenary session, “Making the Most of this AI Moment: A Fireside Chat.”
Charles serves as the provost and vice chancellor for academic affairs with the UW-Madison, and Michael is the division director for information and intelligent systems at NSF.
Cynthia, the session moderator, is a longtime INFORMS member and the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, as well as heads the Interpretable Machine Learning Lab.
105 episodes
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