[Eecs_mscs] EECS Research Seminar Talk-Towards Trustworthy AI: Evolving from Secure Systems to Responsible Integration

[Hunter, Tiffany]

Title:
Towards Trustworthy AI: Evolving from Secure Systems to Responsible Integration

Abstract:
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.
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.
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.

Biography:
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.
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.
She has published papers at conferences such as ICCV, AAAI, and ICASSP, and in journals like TKDE.
You can find out more about Xi Li at https://lixi1994.github.io/<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>

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