Classes & TalksMonday, November 30
CyLab Seminar: Andrew Ilyas
Monday, November 30 · 12-1 p.m. · Studio Theater, Jared L. Cohon University Center, Carnegie Mellon University
- When
- Monday, November 30 · 12-1 p.m.
- Where
- Studio Theater, Jared L. Cohon University Center, Carnegie Mellon University5000 Forbes Ave · Pittsburgh, PA · 15213
- Good to know
About this event
Speaker:Andrew Ilyas Assistant Professor, Electrical and Computer Engineering and Software and Societal Systems Carnegie Mellon UniversityTalk Title:Talk title coming soonAbstract:Abstract coming soonBio: Andrew Ilyas is an assistant professor at CMU. Previously, he was a Stein Fellow at Stanford Statistics and a Ph.D. student MIT, where he was fortunate to be advised by Costis DaskalakisOpens in new window and Aleksander MadryOpens in new window and supported by an Open Philanthropy AI FellowshipOpens in new window. Ilyas attended MIT for undergrad, majoring in CS and in Math. Outside of research, I enjoy playing soccer and table tennis. Ilyas's goal is to uncover general principles that describe and predict the behavior of ML systems-ideally enabling predictably reliable future systems. This goal entails combining statistical tools with large-scale experiments to precisely understand the ML "pipeline," from training data (and the way we collect it), to learning algorithms, to deployment. He also likes thinking broadly about (human) trust in AI systems.strong>Talk Title:
Talk title coming soon
Abstract:
Abstract coming soon
Bio:
Andrew Ilyas is an assistant professor at CMU. Previously, he was a Stein Fellow at Stanford Statistics and a Ph.D. student MIT, where he was fortunate to be advised by Costis DaskalakisOpens in new window and Aleksander MadryOpens in new window and supported by an Open Philanthropy AI FellowshipOpens in new window. Ilyas attended MIT for undergrad, majoring in CS and in Math. Outside of research, I enjoy playing soccer and table tennis.
Ilyas's goal is to uncover general principles that describe and predict the behavior of ML systems-ideally enabling predictably reliable future systems. This goal entails combining statistical tools with large-scale experiments to precisely understand the ML "pipeline," from training data (and the way we collect it), to learning algorithms, to deployment. He also likes thinking broadly about (human) trust in AI systems.
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