Abstract

Statistical facts are powerful as they help our society derive insights from data and form decisions. How can we design algorithmic tools to discover facts from massive data? When we are inundated with too many facts, how do we make our algorithms discern trivial from important facts? When statistics become outright lies, how can we build computing tools to mitigate falsehood? For more than a decade, the Innovative Data Intelligence Research Laboratory (IDIR) has been building data-driven fact-checking and fact-finding systems in searching for answers to these questions. The projects are part of the Lab’s inter-disciplinary research program in computational journalism. This talk will present a high-level, intuitive overview of these projects.

Speaker Bio

Dr. Chengkai Li is a Professor and Associate Chair in the Department of Computer Science and Engineering at The University of Texas at Arlington. His research interests encompass various domains of big data intelligence and data science, including natural language processing, knowledge graphs and the semantic web, databases, data mining, and machine learning. His work extends to applications in computational journalism, data-driven fact-checking, smart agriculture, public health, and transportation. At UTA, he directs the Innovative Data Intelligence Research (IDIR) Lab and serves as co-director of the Center for Artificial Intelligence and Big Data (CARIDA). Dr. Li earned his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, and holds an M.Eng. and a B.S. in Computer Science from Nanjing University.