Keynote Speaker


Pin-Yu Chen

Pin-Yu Chen

IBM

About

Dr. Pin-Yu Chen is a principal research scientist 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. Dr. Chen received his Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen’s recent research focuses on adversarial machine learning of neural networks for robustness and safety. His long-term research vision is to build trustworthy machine learning systems.

He received the IJCAI Computers and Thought Award in 2023. He is a co-author of 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) and AI Explainability 360 (AIX 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), 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 Transactions on Machine Learning Research and serves as an Area Chair or Senior Program Committee member for NeurIPS, ICML, AAAI, IJCAI, and PAKDD. He received the IEEE GLOBECOM 2010 GOLD Best Paper Award and UAI 2022 Best Paper Runner-Up Award.

Quantum for AI and AI for Quantum: Exploring the Benefits of Quantum Machine Learning in Representation Learning, Generalization, and Trust

Quantum computing and machine learning (AI) are among the most anticipated areas for bringing revolutionary breakthroughs in the near future. This talk explores the benefits of end-to-end quantum machine learning consisting of quantum circuits for feature extraction and representation learning with classical deep neural networks. Specifically, we will cover the following topics:
(i) Representation learning: an end-to-end quantum machine learning framework called QTN-VQC, which combines a quantum tensor network (QTN) with a variational quantum circuit (VQC)
(ii) Generalization: theoretical characterization of variational quantum circuit based functional regression
(iii) Trust: the advantages of quantum machine learning for privacy and adversarial robustness

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