Prof. K. Selçuk Candan
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, including energy systems. 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 also served in the editorial boards of the IEEE Trans. on Knowledge and Data Engineering, ACM Transactions on Database Systems, ACM Transactions on Cloud Computing, and as founding managing editor for the Proceedings of the ACM on Management of Data. He has served in the organization and program committees of various conferences: in 2021, he served as the General Chair of the IEEE Smart Data Services Conference, in 2022 as the General Chair for ACM WSDM conference, and in 2023 as a PC co-Chair for the ACM SIGMOD conference. He has successfully served as the PI or co-PI of numerous grants, including from the National Science Foundation, DoD, DoE, Mellon Foundation, and several industrial partners. He is a member of the Executive Committee of ACM Special Interest Group on Management of Data (SIGMOD) and an ACM Distinguished Scientist.
Smart Data Services for Sensemaking in Human-Centered Dynamic Systems.
Abstract: Many socio-economical critical human-centered domains (such as sustainability, public health) are characterized by highly complex and dynamic systems, requiring data and model driven situational awareness and decision making. Successfully tackling many urgent challenges in these domains requires obtaining a deeper understanding of complex relationships and interactions among a diverse spectrum of entities in different evolving contexts. Models have to be constructed in the presence of sensed data, along with applicable physical models, from multiple sources, often characterized by varying levels of coverage and accuracy. Moreover, both data and models required for the said situational awareness and predictions are defined over high-dimensional and time-varying parameter spaces and require causally informed analysis within the appropriate context. Thus, operations in these domains necessitate addressing several major challenges, including latent contexts of impact, heterogeneous networks of entities, dynamicity of impact in varying contexts, and high computational and I/O costs of context-sensitive impact discovery. These algorithms and the novel data platforms they are deployed in need to be efficient and scalable in terms of off-line and on-line running times and their space requirements. In this talk, we will provide several examples from National Science Foundation and Department of Energy funded projects on resilient building energy systems and discuss outlines of possible computational approaches to these challenges.
