Gao Cong 

Professor, College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore

Gao Cong is currently a Professor in the College of Computing and Data Science at Nanyang Technological University. He previously worked at Aalborg University, Denmark, Microsoft Research Asia, and the University of Edinburgh. His current research interests include Data and AI systems (DB4AI &AI4DB), spatial data management, spatial-temporal data mining, and recommendation systems. He is recognized as an ACM Distinguished Member for his work in spatial data management and spatial-temporal data mining. His citation in Google Scholar was over 24,000 with H-index 79. He received ACM SIGIR’25 test of time award honourable mention award, and the best paper runner-up awards at the ACM WSDM’20 and WSDM’22 conferences for two of his research papers. He serves as an associate editor for ACM Transactions on Database Systems (TODS) and IEEE TKDE. He served as a PC co-chair for ICDE’2022, the associate general chair of KDD’21, a PC co-chair for E&A track of VLDB 2014, and a PC vice-Chair for ICDE’18.

Title: From AI4DB to Databases for AI: Rethinking Data Systems in the LLM Era

Abstract: This talk explores the bidirectional frontier between AI for databases (AI4DB) and databases for AI. On the AI4DB side, I highlight query plan representation as a foundational abstraction for learning-driven optimization, pointing toward foundation models for databases. I will also discuss how large language models (LLMs) are beginning to rewrite queries and even construct indexes, challenging decades of handcrafted heuristics.

In the second part, I turn to databases for AI and ask a more provocative question: Is RAG already reaching its limits? As AI systems evolve, we are moving beyond static retrieval toward agentic analytics, where AI agents interact with data systems in iterative, decision-driven loops. This shift raises a fundamental question: how should such systems be benchmarked?

Overall, this talk presents a unified view of the evolving interplay between databases and AI.