[CMU AI Seminar] Apr 27 at 12pm (Zoom) -- Bo Li (UIUC) -- Secure Learning in Adversarial Environments -- AI Seminar sponsored by Fortive

[Shaojie Bai]

Dear all,

We look forward to seeing you *next Tuesday (4/27)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of our *CMU AI seminar*, sponsored
by Fortive <https://careers.fortive.com/>.

To learn more about the seminar series or see the future schedule, please
visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
<http://www.cs.cmu.edu/~aiseminar/>

On 4/27, *Bo Li* (UIUC) will be giving a talk on "*Secure Learning in
Adversarial Environments*".

*Title*: Secure Learning in Adversarial Environments

*Talk Abstract*: Advances in machine learning have led to rapid and
widespread deployment of learning based inference and decision making for
safety-critical applications, such as autonomous driving 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 other machine learning models at test time through evasion
attacks, or can inject well-crafted malicious instances into training data
to induce errors in inference time through poisoning attacks. In this talk,
I will describe my recent research about security and privacy problems in
machine learning systems. In particular, I will introduce several
adversarial attacks in different domains, and discuss potential defensive
approaches and principles, including game theoretic based and knowledge
enabled robust learning paradigms, towards developing practical robust
learning systems with robustness guarantees.

*Speaker Bio*: Dr. Bo Li is an assistant professor in the department of
Computer Science at University of Illinois at Urbana-Champaign, and the
recipient of the Symantec Research Labs Fellowship, Rising Stars, MIT
Technology Review TR-35 award, Intel Rising Star award, Amazon Research
Award, and best paper awards in several machine learning and security
conferences. Previously 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, scalable frameworks
for achieving robustness for a range of learning methods, and a privacy
preserving data publishing system. Her work have been featured by major
publications and media outlets such as Nature, Wired, Fortune, and New York
Times.

*Zoom Link*:
https://cmu.zoom.us/j/92752459790?pwd=aWN0NXhxaitPTHlnaEZpYUFuNUk1UT09

Thanks,
Shaojie Bai (MLD)
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