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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Title:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Towards Trustworthy AI: Evolving from Secure Systems to Responsible Integration<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Abstract:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Trustworthy Artificial Intelligence (AI) has become a significant concern as machine learning (ML) models become integral to safety-critical systems like vehicle autopilots
and medical diagnostics. Consequently, the focus extends beyond simple effectiveness and efficiency to also encompass the resilience of these systems against errors and adversarial attacks. Within the spectrum of such attacks, poisoning attacks represent a
significant threat, compromising the integrity of models by inserting malicious instances into their training sets.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">In this presentation, I will share my research contributions to machine learning security, specifically focusing on poisoning attacks and defenses: (1) improving ML model
robustness against untargeted poisoning attacks through an unsupervised anomaly detection method grounded in the Bayesian Information Criterion; and (2) investigating the vulnerabilities of ML models in novel applications, such as video action recognition
systems, to carefully designed targeted poisoning attacks.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Furthermore, I will outline the direction of my future research on Trustworthy AI. This includes aligning my current research in ML security with the emerging trend of
foundation models and broadening my research to incorporate ethical and privacy considerations within AI. I aim to support technological advancement and address societal challenges through reliable AI.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Biography:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Xi Li is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Penn State University, supervised by Dr. George Kesidis and Dr. David Miller.
She received her B.S. degree in Electrical Engineering from Southeast University, Nanjing, China, in 2016, and her M.S. degree in Computer Science from Penn State University in 2018.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">Her research interests include trustworthy AI and adversarial machine learning, with her Ph.D. thesis specifically focusing on poisoning attacks and defenses against
deep neural networks. Her research vision is centered on developing trustworthy and reliable AI systems, aiming to support the advancement of technology and solve social challenges.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">She has published papers at conferences such as ICCV, AAAI, and ICASSP, and in journals like TKDE.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif">You can find out more about Xi Li at
<a href="https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Flixi1994.github.io%2F&data=05%7C02%7Ceecs_mscs%40listserv.ohio.edu%7C70d40dad9f1743162bd808dc32348eb4%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C638440448534980154%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=lsNXJt4gjkLOF%2BuWNDuiHIzprWZPVQqlFjHGs1WYnXw%3D&reserved=0" originalsrc="https://lixi1994.github.io/" shash="EqP1IPUmHLaQcxo50AIs5tsG4tqXInSuvwIuSmEG/FUZeqmadmjKxyx/u/TJngNqFQCyBKR1lnqE5yAz5GMzWXxdHQX5SIZ6CPMt5PAnfBTrdh94szU+Sw1fTpG/o4aUCrBECsZfjgSIGi66H2XR47061Ws24HdOvUjlCfgCCu8=">
https://lixi1994.github.io/</a><o:p></o:p></span></p>
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