Madelon Hulsebos
I’m a researcher at CWI (tenured in April ‘26) where I lead the Table Representation Learning (TRL) Lab. I’m also an ELLIS Scholar and faculty MT member of the ELLIS unit Amsterdam, and Member of the Scientific Advisory Board of Prior Labs. Previously, I did a postdoc at UC Berkeley and obtained my PhD from the University of Amsterdam for which I spent time at MIT and Sigma Computing. Before academia, I spent 2+ years in industry working on ML-driven automated data science tools. Find my full CV here.
My research is focused on table representation learning (TRL), basically anything “AI for tabular data”, since tables prevail in the data landscape, contain valuable insights, and fuel important decisions in everywhere from governments and industry to healthcare. My vision is to democratize insights from tabular data ✨, and my research is centered around that (see below). My research has been supported by an NWO AiNed grant, BIDS-Accenture Fellowship, CWI internal funding, and industry sponsors.
In 2021, I set the goal to establish tabular data as a key modality for AI akin to images and text. To this end, and ever since, I’ve happily been driving Tabular AI ♥ initiatives besides my own research: I founded the Table Representation Learning (TRL) workshop series at NeurIPS and ACL, organized various related efforts at Dagstuhl, EurIPS, etc, and established the TRL research theme at ELLIS Amsterdam. I’m generally reviewing for various research tracks/workshops at e.g. NeurIPS, ICLR, ACL, and SIGMOD.
Some research interests and contributions in Tabular AI in line with my vision for democratizing insights from structured data:
- Tabular reasoning: question ambiguity in tabular data analysis, detecting tabular semantics (e.g. column types Sherlock) and contextual sensitivity, why you shouldn't use LLMs for tabular QA.
- Predictive data science: on the silent failures in agentic data science (new!).
- Table retrieval & embeddings: towards open tabular insight extraction (new!), benchmarking table retrieval (TARGET), DCTR retriever for complex queries, Observatory on relational properties in table embeddings.
- Dataset search: vision and insights on dataset search systems, proactive task-based dataset search DataScout.
- Corpora for tabular AI: SQaLe: 500K+ text2SQL samples (new!), GitTables: 1M+ real-world tables, SchemaPile: 22K+ DB schemas.