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Booz Allen Hamilton Colloquium: "Can Machine Learning Ever Be Trustworthy?" David Evans, UVA
Friday, December 7, 2018
3:30 p.m.-4:30 p.m.
1110 Jeong H. Kim Engineering Building
For More Information:
Kara Stamets
301 405 4471
stametsk@umd.edu

David Evans
Professor of Computer Science, University of Virginia

Can Machine Learning Ever Be Trustworthy?

Abstract: Machine learning has produced extraordinary results over the past few years, and machine learning systems are rapidly being deployed for critical tasks, even in adversarial environments.  This talk will survey some of the reasons building trustworthy machine learning systems is inherently impossible, and dive into some recent research on adversarial examples. Adversarial examples are inputs crafted deliberately to fool a machine learning system, often by making small, but targeted perturbations, starting from a natural seed example. Over the past few years, there has been an explosion of research in adversarial examples but we are only beginning to understand their mysteries and just taking the first steps towards principled and effective defenses. The general problem of adversarial examples, however, has been at the core of information security for thousands of years. In this talk, I'll look at some of the long-forgotten lessons from that quest, unravel the huge gulf between theory and practice in adversarial machine learning, and speculate on paths toward trustworthy machine learning systems.

Bio: David Evans (https://www.cs.virginia.edu/evans/) is a Professor of Computer Science at the University of Virginia and leader of the Security Research Group (https://www.jeffersonswheel.org) and a member of the NSF Center for Trustworthy Machine Learning (https://ctml.psu.edu/). He is the author of an open computer science textbook (https://computingbook.org) and a children's book on combinatorics and computability (https://dori-mic.org). He won the Outstanding Faculty Award from the State Council of Higher Education for Virginia, and was Program Co-Chair for the 24th ACM Conference on Computer and Communications Security (CCS 2017) and the 30th (2009) and 31st (2010) IEEE Symposia on Security and Privacy, where he initiated the SoK papers. He has SB, SM and PhD degrees in Computer Science from MIT, a green stripe belt in Tae Kwon Do, and has been a faculty member at the University of Virginia since 1999.

This Event is For: Clark School • Graduate • Undergraduate • Faculty • Post-Docs • Alumni

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