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 (abbrev. TRL; I'm curating an 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). I also co-chair the Data Management for E2E ML workshop and the SemTab challenge. I review for various tracks/workshops at e.g. VLDB, SIGMOD, EDBT, NeurIPS, ICML, ICLR, WWW. Besides academia, I've been member of the supervisory board of a student consulting firm and was a data scientist for 2+ years, working on tools for automated data analysis and ML pipelines.

Interested in TRL, and looking for PhD/postdoc opportunities? Please leave your information here.

šŸ‘‰ 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.

Observatory [To Appear in PVLDB, 2023]
1) Framework & tool for anazlying table embeddings based on the relational model and data distributions.
2) Library for extracting table embeddings on row- column-, cell-level (TRL workshop @ NeurIPS).
1) analysis paper | 2) library paper | code

GitTables [SIGMOD, 2023]
Corpus of 1.7M relational tables extracted from GitHub CSVs. Columns annotated w/ semantic types.
paper | website | dataset | code | video presentation | slides | podcast

GitSchemas [DBML@ICDE, 2022]
A dataset of approximately 50K real-world database schemas extracted from SQL files from GitHub.
paper | code/dataset

AdaTyper [CIDR, 2022]
Adaptive semantic column type detection system focusing on productization in industry contexts.
paper | video presentation

Sherlock [KDD, 2019]
DL method for semantic data type detection of table columns (top-5 MIT Media Lab repos, 2 Aug 23).
paper | website | code

VizNet [CHI, 2019]
Corpus of over 31 million datasets from open data repositories, for benchmarking visualization studies.
paper | website

Recent news

  • Postdoctoral Fellowship at Berkeley

    Nov 27, 2023

    Pleased to have started a new position as postdoctoral scholar at the University of California, Berkeley. Iā€™m looking forward to starting some interesting new projects, and collaborating with the great EPIC Lab!

  • Best Reviewer Award PhD Workshop VLDB 2023

    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.

  • Upcoming talks

    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!

  • Disseminate podcast on GitTables

    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!

  • The TRL workshop accepted at NeurIPS 2023!

    Jul 13, 2023

    Looking forward to catching up w/ the latest in Table Representation Learning and performance/applications of LLMs over tables, at the Table Representation Learning workshop 2023!