The University of Washington’s database group is at the forefront of pioneering research that transcends traditional boundaries of data management. Our multidisciplinary team explores cutting-edge areas including multimodal database systems, the intersection of generative AI and data management, dynamic query evaluation and optimization, immersive, scalable data visualization, and user-centered research. We are passionate about transforming how data is stored, accessed, and understood—driving innovation that empowers scientists, industries, and society to harness the full potential of data in the AI era.
‘CENTS: A Flexible and Cost-Effective Framework for LLM-Based Table Understanding’ by Guorui Xiao, Dong He, Jin Wang, and Magdalena Balazinska has been accepted at VLDB 2025.
‘LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences’ by Haozhe Zhang, Christoph Mayer, Mahmoud Abo Khamis, Dan Olteanu, Dan Suciu received the SIGMOD’2025 Best Paper Award.
‘Bootstrapping Compositional Video Query Synthesis with Natural Language and Previous Queries from Users’ by Manasi Ganti, Enhao Zhang, and Magdalena Balazinska has been accepted at the HILDA workshop, SIGMOD 2025.
‘MaskSearch: Querying Image Masks at Scale’ by Dong He, Jieyu Zhang, Maureen Daum, Alexander Ratner, and Magdalena Balazinska has been accepted at ICDE 2025.
Kyle Deeds and Timo Camillo Merkle received the Best Paper award at ICDT 2025 for their paper Worst-Case Optimal Joins Meet Partition Constraints!
‘Mind the Data Gap: Bridging Large Language Models (LLMs) to Enterprise Data Integration’ by Moe Kayali (University of Washington), Fabian Wenz (TUM), Nesime Tatbul (Intel Labs and MIT), Cagatay Demiralp (MIT CSAIL) has been accepted at CIDR 2025.
Four papers from UWDB got accepted into SIGMOD 2025!
Two papers from UWDB got accepted in ICDT 2025!
