About me
I'm a postdoctoral fellow at UC Berkeley with the EPIC Lab at EECS / BIDS. Prior to joining UCB, I did my PhD research at the University of Amsterdam and partly at Sigma Computing and the MIT Media Lab.In my opinion, tables are a promising modality for representation and generative learning with too much application potential to ignore. Therefore, I research Table Representation Learning (check out this index of recent research on TRL) and applications in data management and analysis. Broadly, my objective is to make insight retrieval from structured data a walk in the park for everyone. Some of my research in this area can be found below.
To stimulate research on TRL, I founded the Table Representation Learning workshop (@NeurIPS). Within the wider research community, I co-organize the Data Management for E2E ML workshop at SIGMOD and the Table-to-KG matching (SemTab) challenge. I review for various tracks/workshops at e.g. VLDB, SIGMOD, NeurIPS, ICLR, WWW. Before starting in academia, I was a data scientist for 2+ years, working on automating ML-driven analyses.
Interested in a PhD/Postdoc on neural models for structured data starting fall 2024? Leave your info here!
👉 Read more in my CV.
Selected projects
The projects below reflect my main research interest. But I enjoy working on other topics too. Check my profile on Google Scholar for my full publication record.
1) Survey results surfacing why, what, and how is searched for data, key open challenges, and system desiderata.
2) System (tbc).
1) paper survey
A dataset of approximately 50K real-world database schemas extracted from SQL files from GitHub.
paper | code/dataset
1) Framework for analyzing table embeddings based on the relational model, and desiderata for TRL models.
2) Library for extracting table embeddings on row- column-, cell-level.
1) analysis paper | 2) library paper | code
Corpus of 1.7M relational tables extracted from GitHub CSVs. Columns annotated w/ semantic types.
paper | website | dataset | code | video presentation | slides | podcast
Adaptive semantic column type detection system focusing on productization in industry contexts.
paper | video presentation
DL method for semantic data type detection of table columns (top-5 MIT Media Lab repos, 2 Aug 23).
paper | website | code
Corpus of over 31 million datasets from open data repositories, for benchmarking visualization studies.
paper | website
Recent news
Mar 20, 2024
Thrilled to share that I’m awarded the AiNed Fellowship Grant (worth $1M) to lead the 5-year DataLibra research project at CWI in Amsterdam starting fall 2024. DataLibra is focused on democratizing insight retrieval from structured data through representation and generative learning over tables.
Nov 27, 2023
Pleased to have started a new position as postdoctoral scholar at the University of California, Berkeley. I’m looking forward to starting some interesting new projects, and collaborating with the great EPIC Lab!
Sep 12, 2023
Grateful to have received the Best Reviewer Award at the VLDB 2023 PhD workshop! Hearing that my reviews are considered valuable means a lot to me.
Aug 04, 2023
Excited to be invited to talk about Transformers for Tables at the Transformers at Work (15 Sep 2023), and about GitTables at the TaDA workshop (remote) at VLDB (1 Sep 2023). Very welcome to join!
Jul 20, 2023
Had a nice chat on the Disseminate Podcast w/ Jack about the thoughts and processes behind GitTables, and the potential of learned table representations. Listen to the podcast here, thanks for hosting me Jack!