Arijit Khan

Arijit Khan is an IEEE Senior Member, an ACM Distinguished Speaker, and a tenured Associate Professor in the Department of Computer Science at Bowling Green State University, Ohio, USA. He received his PhD in Computer Science from the University of California, Santa Barbara, followed by a postdoctoral fellowship in the Systems Group at ETH Zürich, Switzerland. Prior to joining BGSU, he served as an Associate Professor at Aalborg University, Denmark, and as an Assistant Professor in the School of Computer Science and Engineering at Nanyang Technological University, Singapore.

Dr. Khan’s research spans graph data management, graph query processing, graph machine learning, and AI‑native data systems integrating LLMs, vector databases, and knowledge graphs. He has published more than 100 papers in premier venues across data management, AI/ML, and systems, including ACM SIGMOD, VLDB, ACM SIGKDD, IEEE ICDE, IEEE ICDM, IEEE TKDE, SIAM SDM, USENIX ATC, EMNLP, ICLR, EDBT, The Web Conference (WWW), ACM WSDM, ACM CIKM, ACM TKDD, and the VLDB Journal. His work has been recognized with the IBM PhD Fellowship (2012–13), a VLDB Distinguished Reviewer Award (2022), and two ACM SIGMOD Distinguished PC Member Awards (2024, 2025).

He has co-presented widely attended tutorials on emerging graph queries, big graph systems, graph ML explainability, and LLM+KG at leading conferences such as VLDB (2014, 2015, 2017), ACM WSDM (2026), EDBT (2025), IEEE DSAA (2023), ACM CIKM (2022), and IEEE ICDE (2012). Dr. Khan has served on program committees of major conferences including ACM SIGMOD, VLDB, ACM SIGKDD, IEEE ICDE, IEEE ICDM, EDBT, and ACM CIKM, and on senior PCs for WWW and ACM SIGKDD.

He has co-organized several influential workshops, including the LLM+Vector Data Workshop at ICDE (2025, 2026), the LLM+Graph Workshops at VLDB (2024-2026), and the Knowledge Graphs for Responsible AI Workshop at ESWC 2025 and CIKM 2024. He is also a co-organizer of the 2026 Dagstuhl Seminar on “Managing Vector Data for Retrieval-Augmented Generation: Systems and Algorithms.”

Dr. Khan is a co-author of a book on uncertain graphs in Morgan & Claypool’s Synthesis Lectures on Data Management. He has contributed invited chapters and articles on graph querying, mining, and LLM‑integrated data systems to the ACM SIGMOD Blog, the Springer Handbook of Big Data Technologies, and the Springer Encyclopedia of Big Data Technologies. He has delivered invited talks and tutorials in more than 15 countries, including at the NII Shonan Meeting on “Graph Database Systems: Bridging Theory, Practice, and Engineering” (Japan, 2018), APWeb-WAIM (2017), COMAD (2016), and the Dagstuhl Seminar on Graph Algorithms and Systems (Germany, 2014).

His editorial service includes Associate Editor roles for IEEE TKDE (2019–2024) and ACM TKDD (2023–present). He has held leadership roles such as Proceedings Chair of EDBT 2020, Poster Track Co-Chair for IEEE ICDE–TKDE 2023, Short Paper Track Co-Chair for ACM CIKM 2024, Demonstration Track Co-Chair for IEEE ICDE 2025, ACM SIGKDD PhD Consortium Co-Chair 2025, and PC Co-Chair for the Australasian Database Conference (ADC 2025).

• Data Management for Emerging Problems in Large Networks
  Graphs are widely used in many application domains, including social networks, knowledge graphs, biological networks, software collaboration, geo-spatial road networks, interactive gaming, among...
• Explainable and Responsible AI with GNNs and GraphRAG
  Graph data--whether social or biological networks, financial transactions, or knowledge graphs--models entities as nodes and their relationships as edges. Deep learning and generative AI...
• Knowledge-Augmented LLMs for Trustworthy Data Systems
  Knowledge‑augmented LLMs offer a promising path toward trustworthy data systems by grounding generative reasoning in structured knowledge. This talk highlights how knowledge graph‑enhanced RAG...