Distributed Data Management and Mining (D2M2) Lab
Lab People
Xiao Li is an assistant professor in the Department of Computer Science and Engineering at Santa Clara University. He obtained his a Ph.D. in Computer Science at The University of Texas at Dallas. His research focuses on distributed machine learning in edge computing, blockchain technology and its applications in distributed systems. He is also interested in applied data science for problem-solving with the application of machine learning, deep learning, and reinforcement Learning. Xiao has published papers in related fields at refereed conferences and journals such as IEEE ICDCS, IEEE TCSS, Theoretical Computer Science, and Journal of Combinatorial Optimization. Xiao was awarded the prestigious Jan Van der Ziel Fellowship at UT Dallas in 2023. Xiao has served as a peer reviewer in various reputable conferences and journals including KDD, ICDCS, IJCAI and Information Sciences. He is also an journal editor of Discrete Mathematics, Algorithms and Applications.
Zhisheng is a master’s student majoring in CS at Santa Clara University. He obtained my bachelor’s degree in Software Engineering at South China Normal University. His research interests lie in federated learning and reinforcement learning.
Kieran Pazmino is a fourth-year student at Santa Clara University, pursuing a degree in Computer Science and Engineering. Their interests center on democratizing the production and use of Artificial Intelligence, with an emphasis on making advanced technologies more accessible and impactful for diverse communities. In this vein, they are also conducting work in federated learning, a decentralized approach to training AI models that preserves privacy and expands access across distributed data sources. As part of their senior design project, Kieran is developing a quadrupedal robot designed to improve accessibility and independence for the elderly. They are passionate about exploring how emerging technologies can be harnessed to expand access, reduce barriers, and improve outcomes in real-world problem-solving.
Shuze Liu is an M.S. student in Computer Science and Engineering at Santa Clara University. His research focuses on federated learning and distributed systems, particularly on developing asynchronous federated learning algorithms for non-independent and identically distributed (non-IID) data. His goal is to build scalable federated learning frameworks that can handle heterogeneous data and device conditions, improving both training efficiency and model performance.