Keynote Speakers

Jundong Li is an Assistant Professor in the Department of Electrical and Computer Engineering, with a joint appointment in the Department of Computer Science, and School of Data Science. 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 mining, causal inference, and algorithmic fairness. As a result of his research work, he has published over 100 papers in high-impact venues (including KDD, WWW, IJCAI, AAAI, WSDM, EMNLP, CIKM, ICDM, SDM, ECML-PKDD, CSUR, TPAMI, TKDE, TKDD, TIST, etc), with over 5,500 citation count. He has won several prestigious awards, including SIGKDD 2022 Best Research Paper Award, NSF CAREER Award, JP Morgan Chase Faculty Research Award, Cisco Faculty Research Award, and being selected for the AAAI New Faculty Highlights roster. His group's research is generously supported by NSF (CAREER, III, SaTC, SAI), JLab, JP Morgan, and Cisco.

Pranesh Srinivasan is a Senior Staff Engineer at Google. He is a Tech Lead for several key aspects of LLMs & Generative Experiences with a particular focus on grounding and RAG. These include projects such as Search Generative Experience (SGE). In addition, he. leads several efforts in core Question-Answering and Search Ranking. Previously, he was a Program Trading Quant at Goldman Sachs working on Blind Principal Risk and Guaranteed flows.

Ajim Uddin is an assistant professor of financial technology at the New Jersey Institute of Technology. His research area is FinTech and the application of machine learning to finance, with a special focus on financial networks. He has applied his research toward the development of a dynamic graph learning framework for asset pricing, incorporating signed networks, i.e., positive and negative connections, in equity pricing models. He is also working on understanding the influences of network connections among decision-makers in the financial decision-making process. These include designing network projection models for the investment network and examining how network influences affect security pricing, as well as institutional investors' herding behavior and portfolio performance.