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Prof. Dr. Ziawasch Abedjan


Leibnitz Universität Hannover
Institut für Praktische Informatik FG Datenbanken und Informationssysteme

Am Welfengarten 1, D-30167 Hannover

Prof. Dr. Ziawasch Abedjan


Fellow | BIFOLD

Full Professor | Datenbanken und Informationssysteme at Leibniz Universität Hannover

Ziawasch Abedjan is Full Professor and chairs the „Datenbanken und Informationssysteme“ group at Leibniz Universität Hannover.
Ziawasch received his PhD from the Hasso Plattner Institute in Potsdam, Germany and spent two years as a postdoctoral associate at MIT. Prior to Hannover, he was Junior Professor at the TU Berlin and senior researcher at DFKI.
His research is supported by additional funding from the DFG, the Federal Ministry for Research and Education, and the Federal Ministry of Transport, Building and Urban Development.

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

Data Management| Sep 21, 2021

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.

Data Management| May 06, 2020

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.