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In this talk, Chen shares his research journey toward building an AI model inspector for evaluating, improving, and exploiting adversarial robustness for deep learning, starting with an overview of research topics concerning adversarial robustness and machine learning, including attacks, defenses, verification, and novel applications. For each, Chen summarizes key research findings, including, 1) practical optimization-based attacks and their applications to explainability and scientific discovery; 2) plug-and-play defenses for model repairing and patching; 3) attack-agnostic robustness assessment; and 4) data-efficient transfer learning via model reprogramming. The talk concludes with his vision of preparing deep learning for the real world and the research methodology of learning with an adversary.
Pin-Yu Chen is a principal research scientist of the Trusted AI Group and PI of the MIT-IBM Watson AI Lab at the IBM Thomas J. Watson Research Center. He is also Chief Scientist of the RPI-IBM AI Research Collaboration program. His recent research focus has been on adversarial machine learning and robustness of neural networks, and more broadly, making machine learning trustworthy. His research contributes to IBM Adversarial Robustness Toolbox, AI Explainability 360, AI Factsheets 360, and Watson Studio. Chen received his Ph.D. in electrical engineering and computer science and his M.A. in Statistics from the University of Michigan at Ann Arbor.