Pin-Yu Chen: Dr. Pin-Yu Chen is a principal 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 to build trustworthy machine learning systems. 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), IJCAI'21, CVPR('20,'21,'23), ECCV'20, ICASSP('20,'22,'23), KDD'19, and Big Data'18, and organized several workshops for adversarial machine learning. He received the IEEE GLOBECOM 2010 GOLD Best Paper Award and UAI 2022 Best Paper Runner-Up Award.Dr. Cho-Jui Hsieh is an Assistant Professor at the UCLA Computer Science department. His research focuses on developing algorithms and optimization techniques for training large-scale and robust machine learning models. He publishes in top-tier machine learning conferences including ICML, NIPS, KDD, ICLR and has won the best paper awards at KDD 2010, ICDM 2012, ICPP 2018, best paper ?nalist at AISEC 2017 and best student paper ?nalist at SC 2019. He is also the author of several widely used open source machine learning software including LIBLINEAR. His work has been cited by more than 13,000 times on Google scholar.