Prof. Dr. Frommholz, Ingo
- Full Professor
- Academic Office
Short BIO
Prof. Dr. Dipl.-Inform. Ingo Frommholz (FBCS, FHEA) is Full Professor and Head of the School of Applied Data Science at Modul University Vienna, Austria. Dr. Frommholz received his PhD from the University of Duisburg-Essen in Germany. He later spent 16 years in the UK at the University of Glasgow, the University of Bedfordshire, and the University of Wolverhampton. He is the PI of the EU Horizon Europe MSCA Staff Exchange project OMINO. He currently chairs the BCS Information Retrieval Specialist Group (BCS-IRSG) in the UK. He further supports his research community as for example senior managing editor of the International Journal on Digital Libraries, General Chair of ACM CIKM 2023, and as programme committee member and reviewer of several prestigious conferences and journals.
Research
Prof. Dr. Ingo Frommholz' main research aim is to tackle information overload by supporting users in finding relevant information within large data collections more effectively. His expertise spans Data Science, Artificial Intelligence (AI), and interactive Information Retrieval (IR). Key focus areas include: Generative AI, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs; Utilizing mathematical modelling, such as quantum mechanical models, for formal approaches to Information Access and Retrieval, often described as quantum-inspired IR; Interactive IR models using the Principle of Polyrepresentation or Information Foraging Theory; Bibliometric-enhanced Information Retrieval (e.g., in academic search). His research, which ranges from probabilistic logics for annotation-based retrieval to modern deep learning for digital humanities, seeks to establish a Machine-in-the-Loop (MITL) paradigm where the user controls the AI-based information seeking process.
Functions/Roles/Memberships at MU
- School of Applied Data Science 2025/03/01-
Courses
- Spring 2026 Algorithms and Data Structures
- Spring 2026 Fundamentals of Computer Science and Programming
- Spring 2026 Information Search and Retrieval
- Spring 2026 Machine Learning I
- Fall 2025/26 Fundamentals of Computer Science and Programming
- Fall 2025/26 Advances in New Media and Information Systems
