Prof. Dr. Ziawasch Abedjan
Prof. Dr. Ziawasch Abedjan
Research Group Lead
Ziawasch Abedjan is Full Professor and Chair of the Data Integration and Data Preparation group at BIFOLD and TU Berlin.
Ziawasch Abedjan is Chair of the Data Integration and Data Preparation group at BIFOLD and TU Berlin. His research is focused on developing machine learning- based and scalable techniques for various data integration, such as data cleaning, data science pipelines, and data discovery.
Ziawasch received his PhD from the Hasso Plattner Institute in Potsdam, Germany and spent two years as a postdoctoral associate at MIT. From 2016 - 2020 he was Junior Professor at the TU Berlin and senior researcher at DFKI. 2020 he was appointed Full Professor and Chair of the „Datenbanken und Informationssysteme“ group at Leibniz Universität Hannover. He spent two years as visiting academic at Amazon Search. His research is supported by the DFG and the Federal Ministry of Science and Education.
| 2019 | SIGMOD Most Reproducible Paper Award |
| 2019 | GI Junior Fellow |
| 2019 | BTW 2019 Winner of Data Science Challenge |
| 2015 | SIGMOD Best Demonstration Award |
| 2014 | CIKM Best Student Paper Award |
| 2014 | ESWC Best Workshop Paper Award |
| 2013 | Best Dissertation Award of the University of Potsdam |
| 2010-2014 | Scholarship of the HPI Research School |
- Data Science
- Databases
- Data Integration
- Data Mining
- Machine Learning
- ACM
- GI
Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
How Green is AutoML for Tabular Data?
Fatemeh Ahmadi, Marc Speckmann, Malte Fabian Kuhlmann, Ziawasch Abedjan
MaTElDa: Multi-Table Error Detection
Dakai Men, Binger Chen, Ziawasch Abedjan
Identifying Semantic Components for PBE-based Transformation Discovery
Fatemeh Ahmadi, Yusuf Mandirali, Ziawasch Abedjan
Accelerating the Data Cleaning Systems Raha and Baran through Task and Data Parallelism
Sebastian Eggers, Ziawasch Abedjan
Automating Data Lineage and Pipeline Extraction
ICDE 2025 Conference Contributions
BIFOLD researchers will participate in ICDE 2025, contributing three research papers. One of these papers will receive the Best Paper Award. The conference will be held May 19–23, 2025, in Hong Kong.
EDBT/ICDT 2025 Conference Contributions
Four BIFOLD research groups will take part in the EDBT/ICDT 2025 data management conference. They will present seven publications, including one that received the EDBT 2025 Best Paper Award. The conference is scheduled for 25–28 March 2025 in Barcelona, Spain.
Multiple Awards for BIFOLD Researchers
The BTW 2025 proved as very successful for BIFOLD researchers: Not only won one of our alumni the Best Dissertation Award: Two additional BIFOLD groups were also honored.
Paper Forecast BTW 2025
Several BIFOLD research groups participate in the 21st Conference on Database Systems for Business, Technology and Web in Bamberg, Germany, from March 3 to 7, 2025.
BIFOLD Ph.D. Student Receives Software Campus Funding
Muaid Mughrabi is a Ph.D. student specializing in Data Integration and Data Preparation at BIFOLD, Technische Universität Berlin, under the supervision of Prof. Dr. Ziawasch Abedjan. In the coming year, he will embark on a research project in collaboration with Celonis.
Researcher Spotlight: Dr. Binger Chen
Get to know BIFOLD researcher Dr. Binger Chen. She recently successfully completed her PhD with Summa cum Laude. Glimpse into her research utilizing Big Data and Machine Learning technology to create representations of programming language features.
Reviewing VLDB 2024
Four BIFOLD research groups participated in the 50th International Conference on Very Large Databases in Guangzhou, China, taking place from August 26 to 30, 2024.
BIFOLD at the 2024 ACM SIGMOD/PODS Conference
BIFOLD researchers presented four research papers, two demos, one workshop paper and were of a panel at the 2024 ACM SIGMOD/ PODS Conference in Santiago, Chile.
Researcher Spotlight: Felix Neutatz
"Machine Learning and Data Quality" is the first concluded BIFOLD Agility Project. Get to know BIFOLD researcher Felix Neutatz and gain valuable insights into the project, his work and the Agility Program.
“KI ist was es isst”: Prof. Dr. Ziawasch Abedjan on Data Quality at TU Berlin
At the “Tag der Lehre” (Teaching Day) at the TU Berlin on May 27, 2024, BIFOLD Research Group Lead Prof. Dr. Ziawasch Abedjan welcomed the attendees with a 20-minute talk on the quality of training data.
Welcome Prof. Dr. Ziawasch Abedjan
As of March 1, 2024, BIFOLD welcomes a new Research Group Lead in Berlin: Prof. Dr. Ziawasch Abedjan heads the new chair for Data Integration and Data Preparation. In particular, he develops new algorithms for the automatic preparation, extraction, and cleaning of datasets for data science workflows.
In search for algorithmic fairness
Artificial intelligence (AI) has found its way into many work routines – be it the development of hiring procedures, the granting of loans, or even law enforcement. However, the machine learning (ML) systems behind these procedures repeatedly attract attention by distorting results or even discriminating against people on the basis of gender or race. “Accuracy is one essential factor of machine learning models, but fairness and robustness are at least as important,” knows Felix Neutatz, a BIFOLD doctoral student in the group of Prof. Dr. Ziawasch Abedjan, BIFOLD researcher and former professor at TU Berlin who recently moved to Leibniz Universität Hannover. Together with Ricardo Salazar Diaz they published “Automated Feature Engineering for Algorithmic Fairness“, a paper on fairness of machine learning models in Proceedings of the VLDB Endowment.
An overview of the current state of research in BIFOLD
Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.
Researchers in Prof. Abedjan’s group win SIGMOD reproducibility award
The paper “Raha: A Configuration-Free Error Detection System” by Mohammad Mahdavi, Ziawasch Abedjan, Raul Castro Fernandez, Samuel Madden, Mourad Ouzzani, Michael Stonebraker, and Nan Tang won the ACM SIGMOD Most Reproducible Paper Award.
Four papers authored by TU Berlin and DFKI researchers have been accepted at SIGMOD 2020
Data management systems researchers in the Database Systems and Information Management (DIMA) Group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) Group at DFKI (the German Research Institute for Artificial Intelligence) were informed that their papers have been accepted at the 2020 ACM SIGMOD/PODS International Conference on the Management of Data.