On the Synergy between Game Theory and Data Science and Engineering
K. Selcuk Candan, Arizona State University
Wednesday, June 7th 2023; 1:30pm-3:30pm

Abstract: Game theory is the study of mathematical models of conflict and cooperation between decision makers and helps develop mathematical techniques for analyzing situations in which individuals make decisions that influence one another. Data science and engineering, on the other hand, are the investigation of the models, algorithms, and systems that collect, integrate, manage, and convert data into usable information through a combination of data management, data processing, artificial intelligence, and machine learning. In this course, we investigate the synergy between these two, apparently distinct, fields, both with the ultimate goal of informing decision making — in the first half of the course, we discuss how game theoretical approaches can be used to help solve a fundamental data science and engineering problem, dimensionality reduction. In the second half, we see that data science and engineering can also come in help to address practical challenges faced by game theoretical decision making, such as limited scalability, especially in the presence of imperfect information.

Bio: K. Selçuk Candan is a Professor of Computer Science and Engineering at the Arizona State University. He is also the Director of ASU’s Assured and Scalable Data Engineering (CASCADE). Prof. Candan’s primary research interest is in the area of management of non-traditional, heterogeneous, and imprecise (such as multimedia, web, and scientific) data. His research focuses on scalable and accurate data integration, management, analysis, and machine learning to enable decision support operation of complex systems (e.g., energy systems, healthcare systems). Among others, he has developed multi-model epidemic simulation data management systems to address computational challenges that arise from the need to acquire, model, analyze, index, visualize, search, and recompose, in a scalable manner, large volumes of data that arise from observations and simulations during a disease outbreaks (PanCommunity). Through various funding sources, he has developed data and model sharing and integration frameworks, including NSF funded “the Digital Archeological Record” and “DataStorm: A Data- and Decision-Flow and Coordination Engine for Coupled Simulation Ensembles”. He has published over 200 journal and peer-reviewed conference articles, one book, and 16 book chapters. He has 9 patents. Prof. Candan served as an associate editor of one of the most respected database journals, the Very Large Databases (VLDB) journal. He is also in the editorial boards of the IEEE Trans. on Knowledge and Data Engineering (2016–present), ACM Transactions on Database Systems, ACM Transactions on Cloud Computing. He has served in the organization and program committees of various conferences. Most recently, he is serving as the Program co-Chair for the ACM SIGMOD conference in 2023. He has successfully served as the PI or co-PI of numerous grants, including from the National Science Foundation, Air Force Office of Research, Army Research Office, Mellon Foundation, and HP Labs. He served as a member of the Executive Committee of ACM Special Interest Group on Management of Data (SIGMOD) and is an ACM Distinguished Scientist. He is a non-EU partner of the EvoGamesPlus ITN. More information is available at http://kscandan.site.