10/29(五) Making AI Trustworthy 主講人：Pin-Yu Chen 博士
國立清華大學資訊工程學系
Department of Computer Science
National Tsing Hua University
專題演講
SEMINAR
主講人： Dr. Pin-Yu Chen
SPEAKER IBM Thomas J. Watson Research Center
題 目：Making AI Trustworthy
TOPIC
時 間：110年10月29日(五)上午10點至12點
DATE
地 點：線上會議室
PLACE
meeting link:
Abstract:
Despite achieving high standard accuracy in a variety of machine learning tasks, deep learning models built upon neural networks have recently been identified as having critical issues related to trustworthiness, including fairness, explainability, and adversarial robustness. This talk will be divided into two parts. In the first part, I will give an overview of the main challenges and ongoing research progress in trustworthy AI, covering fairness, explainability, adversarial robustness, and transparency. In the second part, I will do a deep dive into holistic adversarial robustness, covering three fundamental pillars in adversarial robustness: attack, defense and verification. More details can be found at www.pinyuchen.com
Bio:
Dr. Pin-Yu Chen is a research staff member 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. degree 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 and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. At IBM Research, he received the honor of IBM Master Inventor and several research accomplishment awards, including an IBM Master Inventor and IBM Corporate Technical Award in 2021. His research works contribute to IBM open-source libraries including Adversarial Robustness Toolbox (ART 360) and AI Explainability 360 (AIX 360). He has published more than 40 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at AAAI’22, IJCAI’21, CVPR(’20,’21), ECCV’20, ICASSP’20, KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award.
聯絡人：郭昱廷 教授