Prof. Dr. Elena Demidova


Prof. Dr. Elena Demidova


Elena Demidova is Professor of Computer Science and head of the  Data Science and Intelligent Systems Group (DSIS) at the  University of Bonn

Research Focus
  • Data Analytics
  • Spatio-Temporal data
  • Artificial Intelligence
  • Machine Learning
  • the Web
  • Semantic Web

For an overview of Elena Demidova’s publications, please  follow this link.

Curriculum Vitae

Professional Experience

from 10.2020: Professor of Computer Science at the  University of Bonn, Germany. 

08.2018 – 09.2020 Research Group Leader,  L3S Research Center, Hanover, Germany.

01.2017 – 07.2018 Senior Researcher, Project Coordinator,  L3S Research Center, Hanover, Germany.

01.2016 – 12.2016 Senior Research Fellow,  WAIS Group, ECS, University of Southampton, UK.

06.2006 – 12.2015  Post-Doc researcher (02.2013 – 12.2015), Project leader (01.2014 – 12.2014), Research fellow, PhD student (06.2006-01.2013).  L3S Research Center and  Institute of Distributed Systems, Leibniz Universität Hannover, Germany.

12.2003 – 12.2005 Research / student assistant.  University of Osnabrück, Germany.  CASHMERE-int project.

09.2002 – 08.2003 Intern in software development.  MPDV Mikrolab GmbH, Mosbach, Germany. Development of data mining software.

12.2000 – 08.2002 Student assistant.  Osnabrück University of Applied Science, Germany. Development of software for mathematical analysis.


04.2017 Fellow of  The Higher Education Academy, UK.

01.2013 PhD in Computer Science (Dr. rer. nat.)  Leibniz Universität Hannover, Germany. Grade “very good”. PhD Thesis: “Usability and Expressiveness in Database Keyword Search: Bridging the Gap”. [ pdf] [ TIB catalogue]

03.2006 M.Sc. in Information Engineering,  University of Osnabrück, Germany and  University of Twente, The Netherlands. Grade “very good”.

06.2003 Computer Science Engineer (Media Informatics, Dipl.-Inf. (FH)) German grade: 1.58 (in the top 30% of the year).  Osnabrück University of Applied Science, Germany.

Current projects at L3S



d-E-mand aims to create a foundation for small businesses to create novel services in context of increased spatio-temporal charging demand of electric vehicles.