Dr. Bo Li is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, security, machine learning, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Dr. Xing Xie is currently a Senior Principal Research Manager at Microsoft Research Asia, and a guest Ph.D. advisor at the University of Science and Technology of China. He received his B.S. and Ph.D. degrees in Computer Science from the University of Science and Technology of China in 1996 and 2001, respectively. He joined Microsoft Research Asia in July 2001, working on data mining, social computing and ubiquitous computing. During the past years, he has published over 300 papers, won the ACM SIGKDD China 2021 test of time award, the 10-year impact award honorable mention in ACM SIGSPATIAL 2020, the 10-year impact award in ACM SIGSPATIAL 2019, the best student paper award in KDD 2016, and the best paper awards in ICDM 2013 and UIC 2010. In Oct. 2009, he founded the SIGSPATIAL China chapter which was the first regional chapter of ACM SIGSPATIAL. He is a Fellow of China Computer Federation (CCF), and a Distinguished Member of ACM.

Dr. Peter Kairouz is a Research Scientist at Google, where he leads research efforts focused on federated learning and privacy-preserving technologies. Before joining Google, he was a Postdoctoral Research Fellow at Stanford University. He received his Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign (UIUC). He is the recipient of the 2012 Roberto Padovani Scholarship from Qualcomm's Research Center, the 2015 ACM SIGMETRICS Best Paper Award, the 2015 Qualcomm Innovation Fellowship Finalist Award, and the 2016 Harold L. Olesen Award for Excellence in Undergraduate Teaching from UIUC.

Dr. Nicolas Papernot is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Toronto. He is also a faculty member at the Vector Institute where he holds a Canada CIFAR AI Chair, and a faculty affiliate at the Schwartz Reisman Institute. His research interests span the security and privacy of machine learning. Nicolas is a Connaught Researcher and was previously a Google PhD Fellow. His work on differentially private machine learning received a best paper award at ICLR 2017. He is an associate chair of IEEE S&P (Oakland) and an area chair of NeurIPS. He earned his Ph.D. at the Pennsylvania State University, working with Prof. Patrick McDaniel. Upon graduating, he spent a year as a research scientist at Google Brain where he still spends some of his time.

Dr. Carl Yang is an Assistant Professor in Emory University. He received his Ph.D. in Computer Science at University of Illinois, Urbana-Champaign in 2020, and B.Eng. in Computer Science and Engineering at Zhejiang University in 2014. His research interests span graph data mining, applied machine learning, knowledge graphs and federated learning, with applications in recommender systems, social networks, neuroscience and healthcare. Carl's research results have been published in 70+ peer-reviewed papers in top venues including TKDE, KDD, WWW, NeurIPS, ICML, ICLR, ICDE, SIGIR and ICDM. He is also a recipient of the Dissertation Completion Fellowship of UIUC in 2020, the Best Paper Award of ICDM in 2020, the Best Paper Award of KDD Health Day in 2022, the Outstanding Paper Award of ML4H in 2022, the Amazon Research Award and multiple Emory internal research awards.

Dr. Salman Avestimehr is the Dean’s Professor and the inaugural director of the USC-Amazon Center on Trustworthy AI at the ECE and CS Department of University of Southern California, and the CEO and co-founder of FedML (https://fedml.ai). He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science, both from the University of California, Berkeley. His research interests include decentralized and federated machine learning, information theory, security, and privacy. Dr. Avestimehr has received many awards for his research, including the Presidential PECASE award from the White House (President Obama), the James L. Massey Research & Teaching Award from IEEE Information Theory Society, an Information Theory Society and Communication Society Joint Paper Award, and several Best Paper Awards at Conferences. He has been an Amazon Scholar in Alexa-AI, and is a fellow of the IEEE.