Oct
21
Date: 21-October-2026
Time: 10:30 AM ET (New York Time)
Presenter: Dr. Pin-Yu Chen
About this topic:
Large language models (LLMs) and Generative AI (GenAI) are at the forefront of frontier AI research and technology. With their rapidly increasing popularity and availability, challenges and concerns about their misuse and safety risks are becoming more prominent than ever.
In this talk, the presenter will introduce a unified computational framework for evaluating and improving a wide range of safety challenges in generative AI through the lens of signal processing. Specifically, he will show new tools and insights to explore and mitigate the safety and robustness risks associated with state-of-the-art LLMs and GenAI models, including (i) safety risks in fine-tuning LLMs, (ii) LLM red-teaming and jailbreak mitigation, (iii) prompt engineering for safety debugging, and (iv) robust detection of AI-generated content.
About the presenter:
Dr. Pin-Yu Chen
Pin-Yu Chen (F’25) received the Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016.
He is currently a principal research scientist and manager at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. His recent research focuses on AI safety and robustness. His long-term research vision is to build trustworthy machine learning systems.
Dr. Chen received the IJCAI Computers and Thought Award in 2023. He is a co-author of the book “Introduction to Foundation Models” and the book “Adversarial Robustness for Machine Learning”. At IBM Research, he received several research accomplishment awards, including IBM Master Inventor, IBM Corporate Technical Award, and IBM Pat Goldberg Memorial Best Paper. His research contributes to IBM open-source libraries including Adversarial Robustness Toolbox (ART 360), AI Explainability 360 (AIX 360), and In-Context Explainability 360 (ICX-360). He has published more than 50 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at NeurIPS’22, AAAI(’22,’23,’24,’26), IJCAI’21, CVPR(’20,’21,’23), ECCV’20, ICASSP(’20,’22,’23,’24), KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. He is currently on the editorial board of IEEE Transactions on Machine Learning Research, IEEE Transactions on Signal Processing, IEEE Transactions on Pattern Recognition and Machine Intelligence, and IEEE Access. He is also an Area Chair or Senior Program Committee member for NeurIPS, ICLR, ICML, AAAI, IJCAI, and PAKDD, and a Distinguished Lecturer of ACM. He received the IEEE GLOBECOM 2010 GOLD Best Paper Award and UAI 2022 Best Paper Runner-Up Award. In 2025, he received the IEEE SPS Industry Young Professional Leadership Award.
