News

Recent Publications

All Publications

(2024). Snapcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning. VLDB (demo).

(2024). Towards Interactively Improving ML Data Preparation Code via 'Shadow Pipelines'. Data Management for End-to-End Machine Learning workshop at ACM SIGMOD.

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(2024). Directions Towards Efficient and Automated Data Wrangling with Large Language Models. Databases and Machine Learning workshop at ICDE.

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(2024). SchemaPile: A Large Collection of Relational Database Schemas. ACM SIGMOD.

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(2024). Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. IEEE Data Engineering Bulletin (Special Issue on Data-Centric Responsible AI).

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Team

Faculty, PhD Students & Staff
Prof. Dr. Sebastian Schelter
(Group chair)
Stefan Grafberger
(PhD student)
Hao Chen
(to join as PhD student)
External PhD Students & Guests
Barrie Kersbergen
(bol.com)
Zeyu Zhang
(University of Amsterdam)
Shubha Guha
(University of Amsterdam)
Till Doehmen
(Motherduck)
Yichun Wang
(University of Amsterdam)
(External) Master Students
Aynaz Abdollahzadeh
(University of Amsterdam)
Leonardo Dominci
(University of Amsterdam)

Alumni (name, role and first employment)

Prof. Dr.-Ing. Sebastian Schelter

Sebastian Schelter is a Full Professor at the Berlin Institute on the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin. His research is focused on the intersection of data management and machine learning with the goal to foster the responsible management of data and to democratise data science technologies.

The research of his group is accompanied by efficient and scalable open source implementations, many of which are applied in real world use cases, for example in the Amazon Web Services cloud and in large European e-commerce platforms.

In the past, he has been an assistant professor at the University of Amsterdam, a faculty fellow at New York University, a senior applied scientist at Amazon Research and a research intern at Twitter and IBM Almaden in California. His research contributions have been recognized with an ACM SIGMOD Systems Award, an ACM SIGMOD Best Demo Runner Up Award, and a Best Paper Runner Up Award from the Table Representation Learning workshop at NeurIPS.

Scientific Service
  • Editorial duties: Associate Editor for PVLDB Volume 15, Action Editor for the Journal of Data-Centric Machine Learning Research (DMLR), Action Editor for the open source track of the Journal of Machine Learning Research (JMLR), Guest editor for the IEEE Data Engineering Bulletin
  • Organisation: Founder and co-organiser (until 2020) of the workshop series on “Data Management for End-to- End Machine Learning (DEEM)” at SIGMOD, workshop chair EDBT 2026, co-chair industry track of EDBT 2022, web chair of SIGMOD 2025, co-chair BOSS workshop at VLDB in 2016, Co-organiser of the “Dutch Data Systems Design Seminar” series with CWI Amsterdam
  • Program Committee: SIGMOD 2017 & 2019-2025, VLDB 2021, ICDE 2018-2021 & 2023-2024, EDBT 2017 & 2021, CIKM 2020, PhD Symposium at VLDB 2021, DEEM workshop at SIGMOD 2021-2024, aiDM workshop at SIGMOD 2019, LSRS workshop at RecSys 2013-2015, AIDB workshop at VLDB 2020, DBML workshop at ICDE 2021, TRL workshop at NeurIPS 2022-2023, Provenance Week 2020
  • Awards: ACM SIGMOD Systems Award 2023, ACM SIGMOD Best Demo Runner Up Award 2023, Best Paper Runner Up Award from the Table Representation Learning workshop at NeurIPS
  • Keynotes: Workshop on Online Recommender Systems and User Modeling at RecSys'20, Workshop on Data Management for End-to- End Machine Learning at SIGMOD'21, Data Centric AI Workshop from ETH Zuerich/Stanford 2021, Workshop on Quality in Databases at VLDB'24
  • Panelist: Systems for ML at VLDB 2021, PhD symposium at ICDE 2021, Data management challenges for LLM-powered solutions at DEEM@SIGMOD'23
Past employments
Professional Memberships
  • Apache Software Foundation (emeritus)
  • Association for Computing Machinery
  • Electronic Frontier Foundation
  • Deutscher Hochschulverband

Teaching

We will soon post an overview of our offered courses here.

Job Openings

We will soon announce several openings for PhD students, Postdocs and research assistants.

Contact

Phone: +49 30 314 23555
Email: schelter [at] tu-berlin [dot] de

Technische Universität Berlin
Management of Data Science Processes, EN-726
Einsteinufer 17
10587 Berlin
Germany

Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr.-Ing. Sebastian Schelter