The University of Washington's database group aims at broadening the focus of database and data management techniques beyond their traditional scope. We do both theoretical and systems work in areas such as probabilistic databases, stream processing, sensor-based monitoring, databases and the web, XML, image/video data management, data management for machine learning, ubiquitous computing, data integration, and data mining.
‘Understanding Programmatic Weak Supervision via Source-aware Influence Function’ has been accepted at NeurIPS 2022. Congrats Jieyu Zhang, Cheng-Yu Hsieh, and Alex Ratner!
‘Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem’ received the VLDB 2022 Best Demo Award. Congrats to the authors: Yuki Asada*, Victor Fu*, Apurva Gandhi*, Advitya Gemawat*, Lihao Zhang*, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, and Matteo Interlandi!
‘Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming’ has been accepted at VLDB 2023. Congrats Cheng-Yu Hsieh, Jieyu Zhang, and Alex Ratner!
‘Query Processing on Tensor Computation Runtimes’ has been accepted at VLDB 2022. Congrats to the authors: Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, and Matteo Interlandi!
‘Creating Training Sets via Weak Indirect Supervision’ has been accepted at ICLR 2022. Congrats Jieyu Zhang and Alex Ratner!