About Me

Dr. Emanuel Lacić is working on AI in Infobip with a current focus on Generative AI and analyzing the impact of Large Language Models (LLMs). He joined Infobip after working as the Operations Area Manager and Key Researcher of the Fair-AI division at the Know-Center, Austria's leading research center for data-driven business and Big Data analytics. He received his PhD (with hons) in Computer Science from Graz University of Technology and his M.Sc. as well as B.Sc. in Software Engineering and Information Systems from the Faculty of Electrical Engineering and Computing at the University of Zagreb. He is a former Marshall Plan fellow and has been working as a visiting researcher at the Computer Science department of the University of California, Los Angeles (UCLA). His research is utilized in several industry and European-funded projects such as Learning Layers or MoreGrasp. He is the special issue editor of Frontiers in Big Data - Recommender Systems section, and his main research interests are in the information retrieval field of recommender systems, with a specific focus on algorithmic accuracy, real-time performance, privacy, fairness and biases. His research on fairness in AI and bias in recommender systems was awarded with the Mind-the-gap gender and diversity award of Graz University of Technology in 2022.

Available topics for a Bachelor/Master Thesis in fairness, explainability and reproducibility in AI

Co-supervised Theses

Reiter-Haas, M. (2020) Evaluation of Job Recommendations for the Studo Jobs Platform. Master Thesis. Graz University of Technology.
Fadljevic, L. (2017) Analysis and Prediction of Movie Preferences. Master Thesis. University of Zagreb, Faculty of Electrical Engineering and Computing (cooperaton with the Know-Center in Graz, Austria).
Duricic, T. (2015) Real-time recommendations based on social trust. Master Thesis. University of Zagreb, Faculty of Electrical Engineering and Computing (cooperation with the Know-Center in Graz, Austria).