The DEEM Lab is a cross-organisational research group uniting the chair for the management of data science processes at Technische Universität Berlin with external members from multiple universities and industry. The lab is led by Prof. Dr.-Ing. Sebastian Schelter and is part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD).

Our lab conducts fundamental research at the intersection of data management and machine learning, which addresses data-related problems in ML applications that cause negative economic, societal or scientific impact. Our goal is to foster the responsible management of data and to lower the technical bar for working with data science technologies.

Our research 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. The focus areas of the lab are:

• Data-centric debugging and testing of machine learning pipelines
• Data processing in compliance with legal regulations, such as the “right-to-be-forgotten”
• Automated validation of data at scale

Our 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. We have ongoing collaborations with the University of Amsterdam and CWI in the Netherlands, as well as with the Center for Responsible AI at New York University.

• Mar 25, 2025 ‐ Zeyu gave an invited talk about the efficient utilization of language models for table data preparation at the industry event on Next-Generation Data Management Systems at EDBT 2025 in Barcelona, and subsequently presented our paper on A Deep Dive Into Cross-Dataset Entity Matching with Large and Small Language Models.
  
  
• Jan 11, 2025 ‐ Stefan will be co-organising the workshop on Data Management for End-to-End Machine Learning (DEEM) at SIGMOD 2025 in Berlin.
  
  
• Dec 3, 2024 ‐ We have been co-organising a workshop on ‘The EU AI Act – Developing a technical perspective’ together with our colleagues from machine learning and law as well as industry practitioners.
  
  
• Oct 10, 2024 ‐ Our research group has been covered in an interview on the #ai_berlin website.
  
  
• Jun 1, 2024 ‐ We will be present at the upcoming VLDB conference in China with several contributions:

  • How Data Management Research Helps to Improve Real World ML Applications (Keynote at the workshop on Quality in Databases)
  • A Flexible Forecasting Stack (Industry paper with AWS)
  • Snapcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning (Demonstration)
  • Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans (Paper at the PhD workshop)
  • Assisted design of data science pipelines (Poster of a VLDBJ publication)

  
  
  

Faculty & Staff
Prof. Dr. Sebastian Schelter	Celia Bohnhardt-Schneider
PhD Students & Guests
Stefan Grafberger	Hao Chen	Olga Ovcharenko
Pierre Lubitzsch	Zeyu Zhang (University of Amsterdam)	Shubha Guha (University of Amsterdam)
Till Doehmen (Motherduck)	Yichun Wang (University of Amsterdam)	David Campos (Aalborg University)
Master Students
Aynaz Abdollahzadeh (University of Amsterdam)	Leonardo Dominici (University of Amsterdam)

• Barrie Kersbergen, PhD student, Staff scientist at bol.com
• Dr. Olivier Sprangers, PhD student, Applied scientist at Nixtla
• Dr. Mozhdeh Ariannezhad, PhD student, ML scientist at booking.com
• Arezoo Sarvi, PhD student, Data scientist search at Albert Heijn
• Radu Geacu, Master student, Data engineer at the Royal Schiphol Group
• David Vos, Master student, PhD student at the University of Amsterdam
• Benjamin Wang, Master student, ML scientist at booking.com
• Dr. Ji Zhang, Guest researcher, Research scientist at Huawei

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.

• Email: schelter[at]tu-berlin[dot]de
• Profiles: Google Scholar, Bluesky Twitter, Linkedin, DBLP, ORCID
  
  

Summer semester 2025

We offer the following courses during the summer semester 2025:

• EDML - Engineering Data for Machine Learning
• RDSEM - Seminar on Responsible Data Engineering
• PPDS - Programmierpraktikum Datensysteme

If you are interested in taking one of our courses, please sign up on the corresponding course page on ISIS and attend the first lecture, where we will discuss the details for the formal registration.

Postdoc Position in Responsible Data Engineering (salary level E14)

We are looking for a postdoc to conduct independent research in responsible data engineering. The research direction should be compatible with the themes of our lab, such as data-centric debugging and testing of machine learning applications, data processing in compliance with legal regulations, or the automation of data validation and preparation for ML. Software and data artifacts resulting from the research should be made available under open source licenses or contributed to existing open source projects.

Further tasks of the position include the collaboration with PhD students, the coordination with other research groups in BIFOLD and external partners, the supervision of master/bachelor theses and teaching activities.

Requirements

• Successfully completed university degree (Master, Diplom or equivalent) and PhD in computer science or artificial intelligence
• Strong research background in data management or data-centric machine learning
• Publication record in international refereed conferences such as SIGMOD, VLDB, SIGIR, ICLR, KDD, NeurIPS or ICML

Desirable

• Experience in supervising Master/PhD students
• Teaching experience

How to apply
Please send your application with the usual documents by e-mail to Prof. Dr. Sebastian Schelter at schelter [at] tu-berlin [dot] de , quoting the reference number IV-576/24.

Email: schelter [at] tu-berlin [dot] de

Technische Universität Berlin
FG Management of Data Science Processes
Sekr. TEL 9-2
Ernst-Reuter Platz 7
10587 Berlin
Germany

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