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

[Shaojie Bai]

Hi everyone, this talk is happening in 5 minutes :-)

On Mon, Apr 26, 2021 at 3:46 PM Shaojie Bai <shaojieb at andrew.cmu.edu> wrote:

> Dear all,
>
> Just a reminder that the CMU AI Seminar
> <http://www.cs.cmu.edu/~aiseminar/> is tomorrow *1**2pm-1pm*:
> https://cmu.zoom.us/j/92752459790?pwd=aWN0NXhxaitPTHlnaEZpYUFuNUk1UT09.
>
> Bo Li (UIUC) (see details below) will be talking about adversarial attacks
> and security problems in modern machine learning.
>
> Thanks,
> Shaojie
>
> On Tue, Apr 20, 2021 at 1:15 PM Shaojie Bai <shaojieb at andrew.cmu.edu>
> wrote:
>
>> 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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