Elisabeth Lex
Scholar

Elisabeth Lex

Google Scholar ID: kEzfvdYAAAAJ
Full Prof. at Graz University of Technology
Human-Centered AIRecommender SystemsNatural Language Understanding
Citations & Impact
All-time
Citations
2,112
 
H-index
24
 
i10-index
51
 
Publications
20
 
Co-authors
86
list available
Contact
No contact links provided.
Resume (English only)
Academic Achievements
  • Published over 150 scientific publications in venues such as WebConf, HT, RecSys, UMAP, ECIR, ISMIR, as well as in journals like Foundations and Trends in Information Retrieval, UMUAI, EPJ Data Science, Frontiers in AI, TISMIR, or the International Journal of Human-Computer Interaction; Regularly gives invited talks and acts as Senior PC member, PC member, co-organizer, track chair, and co-track chair at venues such as WebConf, IUI, RecSys, UMAP, Web Science, or HT.
Research Experience
  • Postdoctoral research fellow at Universidad National de San Luis, Argentina, and RWTH Aachen, Germany after completing the Ph.D. program; Work package leader in the FP7 IP Learning Layers project, researching cognition-inspired recommender systems; Task leader in the H2020 Analytics for Everyday Learning (AFEL) project, researching psychology-informed recommender systems.
Education
  • Ph.D. in Computer Science from Graz University of Technology in 2011; Habilitation (venia docendi) in Applied Computer Science with a postdoctoral thesis on 'Modeling and Predicting User Behavior in Web-based Systems'.
Background
  • Research Interests: Recommender Systems, User Modeling, Information Retrieval, Machine Learning, Data Science; Bio: She is a tenured full professor at Graz University of Technology and the dean of study for the master's program Computational Social Systems. Her areas of expertise include recommender systems, user modeling, behavioral analytics, information retrieval, machine learning, data science, and web mining.
Miscellany
  • She is a passionate teacher at TU Graz, where she teaches Web Technology, Recommender Systems, Advanced Information Retrieval, and Computational Methods for Statistics.