[Colloquium] Bo Li talk, 4/29/19

[Sandra Quarles]

UNIVERSITY OF CHICAGO
COMPUTER SCIENCE DEPARTMENT
PRESENTS

Bo Li
University of Illinois at Urbana-Champaign





Monday, April 29, 2019 at 11:00am
John Crerar Library, Room 390

Title:  Secure Learning in Adversarial Environments

Abstract:  Advances in machine learning have led to rapid and widespread deployment of software-based inference and decision making, resulting in various applications such as data analytics, autonomous systems, and security diagnostics. Current machine learning systems, however, assume that training and test data follow the same, or similar, distributions, and do not consider active adversaries manipulating either distribution. Recent work has demonstrated that motivated adversaries can circumvent anomaly detection or classification models at test time through evasion attacks, or can inject well-crafted malicious instances into training data to induce errors in classification through poisoning attacks. In this talk, I will describe my recent research about evasion attacks, poisoning attacks, and privacy problems in machine learning systems. In particular, I will introduce an example of physical attacks in autonomous driving recognition system, and discuss several potential defensive approaches as well as robust learning models.

Bio:  Dr. Bo Li is an assistant professor in the department of Computer Science at University of Illinois at Urbana–Champaign, and is a recipient of the Symantec Research Labs Fellowship. Prior to this she was a postdoctoral researcher in UC Berkeley. Her research focuses on both theoretical and practical aspects of security, machine learning, privacy, game theory, and adversarial machine learning. She has designed several robust learning algorithms, a scalable framework for achieving robustness for a range of learning methods, and a privacy preserving data publishing system. Her recent research focuses on adversarial deep learning and generative models, as well as designing scalable robust machine learning models against adversarial attacks.

Host:  Ben Y. Zhao

PDF:  




Sandy Quarles
Project Assistant
Computer Science Department
5730 S. Ellis Ave.
Chicago, IL 60637
773.702.3508
773.702.8487 Fax









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