Synthesis Lectures on Data Mining and Knowledge Discovery: Mining Structures of Factual Knowledge from Text (PDF)

Xiang Ren is an Assistant Professor in the Department of Computer Science at USC, affiliated faculty at USC ISI, and a part-time data science advisor at Snap Inc. At USC, Xiang is part of the Machine Learning Center, NLP community, and Center on Knowledge Graphs. Prior to that, he was a visiting researcher at Stanford University, and received his Ph.D. in CS@UIUC. His research develops computational methods and systems that extract machineactionable knowledge from massive unstructured data (e.g., text data), and particular focuses on problems in the space of modeling sequence and graph data under weak supervision (learning with partial/noisy labels, and semi-supervised learning) and indirect supervision (multi-task learning, transfer learning, and reinforcement learning). Xiang’s research has been recognized with several prestigious awards including a Yahoo!-DAIS Research Excellence Award, a Yelp Dataset Challenge award, a C. W. Gear Outstanding Graduate Student Award and a David J. Kuck Outstanding M.S. Thesis Award. Technologies he developed have been transferred to U.S. Army Research Lab, National Institute of Health, Microsoft, Yelp, and TripAdvisor.

Jiawei Han is the Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab (2009–2016), and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing since 2014. He is a Fellow of ACM, a Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005

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