Table Representation Learning Workshop
Qian Liu @ Sea AI Lab (Updated on Nov. 2023)
DEEM: Workshop on Data Management for End-to-End Machine Learning @ ACM SIGMOD 2024
I'm a postdoctoral scholar at UC Berkeley with the EPIC Lab and BIDS. Prior to joining UC Berkeley, I did my PhD research at the INDE Lab at the University of Amsterdam . I believe that tables are a promising modality for representation learning with too much application potential to ignore. Therefore, I research Table Representation Learning and applications in data management and analysis. Broadly, my objective is to make insight retrieval from data a walk in the park for everyone through intelligent data
This interest started at the MIT Media Lab, where I developed Sherlock , a deep learning method for detecting table semantics at scale, enabling applications like data validation. In 2020, the interest from industry in Sherlock and the observed dominance of relational tables across the data landscape, inspired me to dedicate my PhD to learned table representations and their applications in practice. A piece of this puzzle is GitTables : a dataset of 1.7M tables extracted from CSV files on GitHub and enriche
To stimulate research on TRL, I founded the Table Representation Learning workshop (NeurIPS). As part of the wider research community, I support JSys as Assistant Editor, co-organize Data Management for E2E ML (SIGMOD) and the SemTab challenge. I review for various tracks/workshops at e.g. VLDB, EDBT, NeurIPS, ICML, WWW. Besides academia, I am member of the supervisory board of a student consulting firm and was a data scientist for 2+ years, working on automating ML-driven analyses. 👉 Read more in my CV
Table Representation Learning Workshop
Qian Liu @ Sea AI Lab (Updated on Nov. 2023)
DEEM: Workshop on Data Management for End-to-End Machine Learning @ ACM SIGMOD 2024