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.

• Jun 10, 2025 ‐ Sebastian has been invited to give a keynote at the AI for Data Editing workshop at KDD’25 in Toronto, Canada.
  
  
• May 2, 2025 ‐ Olga and Sebastian took part in the seminar on the Challenges and Opportunities of Table Representation Learning in Dagstuhl, which aims to connect the communities of data management, machine learning, and natural language processing to discuss the future of learning on tabular data.
  
  
• 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.
  
  

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

• (Dr.) Stefan Grafberger, PhD student, Software engineer at Snowflake
• (Dr.) 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
• (Dr.) 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

For 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.

Theses

If you are interested in writing a bachelor and master thesis with us, please check out our list of available topics at theses.tu-berlin.de.

PhD Position in Responsible Data Engineering (salary level E13)

We are looking for a PhD student to conduct research in responsible data engineering. The research will be conducted in close collaboration with Prof. Julia Stoyanovich from New York University. Responsible data engineering is emerging as a new discipline at the intersection of data engineering and AI that treats ethics, legal compliance, and inclusivity as central design considerations. The holistic nature of this approach is based on the observation that the decisions we make during data collection and preparation profoundly impact AI systems we build and deploy.

The goal of this position is to create a new system which helps data engineers to design data preparation pipelines that optimize model performance along a rich set of responsibility objectives, including accuracy, robustness, fairness, and legal compliance. For that, the system will proactively guide data engineers through the selection and evaluation of a large set of data preprocessing, data augmentation and feature selection operations. A reliable, efficient and easy-to-use open source implementation of this system will be created as part of the research project.

This endeavor is technically challenging in multiple ways. First, data preparation and model selection need to be optimized for multiple objectives, in contrast to existing approaches, which focus on a single objective only such as overall prediction accuracy. Second, the system will have to create, rewrite and concurrently execute large numbers of different pipeline variants, which requires an efficient runtime and novel query optimization techniques. Third, the research needs to account for current dramatic changes in the development practices of AI applications, e.g., AI assisted programming, tabular foundation models and AI-based data science agents.

Requirements

• Successfully completed university degree (Master, Diplom or equivalent) in Computer Science or Artificial Intelligence
• Very good programming skills in Python and at least one additional language (Java/Rust/C++)
• Knowledge in data processing with dataflow systems, relational databases and/or dataframe libraries (e.g., Apache Spark, DuckDB, pandas, etc.)
• Experience with increasing the efficiency, scalability and correctness of data-centric programs
• Basic knowledge of machine learning and common libraries (e.g., pandas, sklearn, pytorch, SparkML, etc.)

Not required, but nice to have

• First-hand experience with real world data processing systems and/or ML deployments (e.g., from internships, jobs or entrepreneurial experience)
• Contributions to open source projects

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-177/25, until 20th of June 2025.

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