Markus Reiter-Haas is a researcher at Graz University of Technology. He is part of the Recommender Systems and Social Computing Lab at the Institute of Interactive Systems and Data Science. His thesis focuses on polarization and framing in online media using computational models and theories from the social sciences. His research interests include contextualized embeddings and semantic representations in natural language processing. He has a background in information retrieval in the industry, where he was responsible for the deep-learning recommender system of a job platform. He published in international renowned venues such as RecSys, ICWSM, and SSCR. Likewise, he provided reviewing services to the top conferences in the field of computer science, such as WWW and SIGIR.

Research Interests

• Natural Language Processing
• Social Network Analysis
• Network Science
• Machine Learning
• Deep Learning
• Item Embeddings
• Recommender Systems
• Information Retrieval

Phd Project

His research deals with polarization in public opinion (Refer to the project page for more details).

CV

Short summary of his background is provided below (for a detailed overview refer to his CV).

Teaching

• Advanced Information Retrieval
  Hans-on Lecture on Transformers4IR
  Highlighted Projects of the course

Education

• Doctorate in Computer Science
  Technische Universität Graz (2020 – ongoing)
  Polarization in Public Opinion

• Master in Computer Science
  Technische Universität Graz (2017 – 2020)
  Main Specialization: Knowledge Technologies
  Secondary Specialization: Multimedia Information Systems
  Passed with distinction

• Bachelor in Computer Science
  Technische Universität Graz (2012 – 2017)
  Passed with distinction

• Technical College in Computer Science
  HTBLA Kaindorf an der Sulm (2006 – 2011)
  Passed with distinction

Experience

• University Assistant (2020 – Present)
  Technische Universität Graz
  Research Focus: NLP in SocialSystems

• Data Scientist (2017-2020)
  Moshbit GmbH (Talto - Talents of Tomorrow)
  Research on deeplearning for job recommenders