Please note: This seminar will take place in DC 1304.
Arijit Khan, Associate Professor
Aalborg University, Denmark
Graph data, e.g., social and biological networks, financial transactions, and knowledge graphs, are pervasive in the natural world, where nodes are entities with features, and edges denote relations among them. Machine learning and recently, graph neural networks (GNNs) become ubiquitous, e.g., in cheminformatics, bioinformatics, fraud detection, question answering, and recommendation. However, GNNs are “black-box” — it remains a desirable yet nontrivial task to explain the results of high-quality GNNs for domain experts.
In this talk, I shall introduce our ongoing works about how data management techniques can assist in generating user-friendly, configurable, queryable, and robust explanations for graph neural networks.
Bio: Arijit Khan is an Associate Professor at Aalborg University, Denmark. His PhD is from University of California, Santa Barbara, USA, and he did a post-doc in the Systems group at ETH Zurich, Switzerland. He has been an assistant professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research is on data management and machine learning for the emerging problems in large graphs.
He is an IEEE senior member and an ACM distinguished speaker. Arijit is the recipient of the IBM Ph.D. Fellowship (2012-13), a VLDB Distinguished Reviewer award (2022), and a SIGMOD Distinguished PC award (2024). He is the author of a book on uncertain graphs and over 80 publications in top venues including ACM SIGMOD, VLDB, IEEE TKDE, IEEE ICDE, SIAM SDM, USENIX ATC, EDBT, The Web Conference (WWW), ACM WSDM, ACM CIKM, ACM TKDD, and ACM SIGMOD Record. Dr Khan is serving as an associate editor of IEEE TKDE 2019-2024 and ACM TKDD 2023-now, proceedings chair of EDBT 2020, IEEE ICDE TKDE poster track co-chair 2023, ACM CIKM short paper track co-chair 2024, and IEEE ICDE demonstration paper track program co-chair 2025.