Panelist
We are honored to invite leading experts from both academia and industry on Federated Learning to share their opinions. The planl will focus on the advances, challenges, and new technologies in federated learning.
Bio: Dr. Jundong Li is an Assistant Professor at the University of Virginia with appointments in the Department of Electrical and Computer Engineering, Department of Computer Science, and School of Data Science. Prior to joining UVA, he received his Ph.D. degree in Computer Science at Arizona State University in 2019, M.Sc. degree in Computer Science at University of Alberta in 2014, and B.Eng. degree in Software Engineering at Zhejiang University in 2012. His research interests are generally in data mining and machine learning, with a particular focus on graph machine learning, trustworthy/safe machine learning, causal inference, and, more recently large language models. He has published over 150 papers in high-impact venues and won several prestigious awards, including SIGKDD Best Research Paper Award (2022), PAKDD Best Paper Award (2024), NSF CAREER Award (2022), PAKDD Early Career Research Award (2023), JP Morgan Chase Faculty Research Award (2021 & 2022), and Cisco Faculty Research Award (2021). He has served on the organizing committees of conferences such as KDD, WSDM, SDM, and IEEE BigData, and is currently on the editorial boards for ACM Transactions on Intelligent Systems and Technology (TIST) and ACM Transactions on Knowledge Discovery from Data (TKDD).
Bio: Graham Cormode is a research scientist at Facebook, and a professor in the Department of Computer Science at the University of Warwick in the UK. His research interests are in data privacy, data stream analysis, massive data sets, and general algorithmic problems. His work on statistical analysis of data has been recognized by the 2017 Adams Prize in Mathematics and as a Fellow of the ACM.
Bio: Dr. Sebastian Stich is a faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research interests span machine learning, optimization, and statistics, with a focus on efficient parallel algorithms for training ML models over decentralized datasets. He obtained his PhD from ETH Zurich and worked as a postdoctoral fellow at UCLouvain and EPFL. He is a co-organizer of the International Optimization for Machine Learning workshop at NeurIPS and the Federated Learning One World Seminar, and serves on the editorial boards of the Journal of Optimization Theory and Applications (JOTA) and the Transactions on Machine Learning Research (TMLR). He received a Meta Research Award in 2022 and a Google Scholar Research Award in 2023.
Bio: Dr. Yunfei Xu is the Chief AI Architect at DENSO International America, Inc., where he leads the company’s transformational initiatives in artificial intelligence. Dr. Xu earned his B.S. and M.S. degrees in Automotive Engineering from Tsinghua University, followed by a Ph.D. in Mechanical Engineering from Michigan State University. With over a decade of experience in research and development within the automotive industry, Dr. Xu has been instrumental in advancing innovative solutions. At DENSO, he leads a diverse range of AI activities, focusing on areas such as autonomous driving, mobility services, and smart infrastructure, which are essential to the future of intelligent transportation. Dr. Xu actively collaborates with academic institutions and industrial partners to promote the adoption of AI technologies, ensuring that cutting-edge research is translated into practical applications that enhance vehicle safety, efficiency, and user experience.