Prof. Dr. Gjergji Kasneci
Responsible Data Science
Business card at TUMonline
My research focuses on transparency, robustness, bias, and fairness in machine learning algorithms and involves ethical, legal, and societal considerations with the goal of using artificial intelligence responsibly for the benefit of individuals and society.
After completing my studies in Computer Science and Mathematics at the University of Marburg in 2005, I joined the Max Planck Institute for Informatics at the University of Saarland. There, I specialized in graph-based mining, information retrieval, and semantic search, earning my PhD in Computer Science in 2009. Following my PhD, I spent two years as a Postdoctoral researcher at Microsoft Research Cambridge, UK, working on probabilistic inference over knowledge bases. From 2011 to 2014, I led the Web Mining Research group at the Hasso Plattner Institute as a Senior Researcher with a research focus on scalable representation, disambiguation, and retrieval of entities occurring in the web. In 2014, I joined SCHUFA Holding AG as an area manager for Innovation and Strategic Analysis. I later served as the company's Chief Technology Officer from 2017 to 2022. Additionally, as an Honorary Professor at the University of Tübingen, I led the Data Science and Analytics Group from 2018 until 2023, focusing on research in explainable, robust, and scalable AI applications. In 2023, I was appointed Professor of Responsible Data Science at TU Munich and a core member of the Munich Data Science Institute.
Selected Awards & Funding
- Honorary Professorship from the University of Tübingen (2019)
- Seoul Test of Time Award by the International World Wide Web Conference Committee, international recognition for one of the most influential articles on Knowledge Extraction and Organization (2018)
- Funding for an Industry-on-Campus Professorship for Data Science & Analytics (2018 - 2023) at the University Tübingen (1,3 Mio. EUR)
- DFG FOR 1306, PI of "A Library for Scalable Analytics and Mining in Stratosphere"
- Core Member of Cyber Valley
Recent Invited Talks (Selection)
- Invited Talk: "Model Explanations in Dynamic Data Environments", Workshop on Explainable Artificial Intelligence: From Static to Dynamic at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD, September 2023)
- Panel Discussion: "Finding the Right Balance in Data Science and AI: Regulation vs. Innovation", Opening Ceremony of the Munich Data Science Institute (September 2023)
- "Generative AI Approaches to Counterfactual Explanations" at the Symposium on Generative AI of the European Central Bank (June 2023)
- Panel Discussion: "Eye Tracking and Machine Learning" at the ACM Symposium on Eye Tracking Research and Applications (June 2023)
- "Towards Realistic Counterfactual Explanations for Tabular Data" at the Workshop on Artificial Intelligence, Causality and Personalized Medicine (AICPM 2022)
Leemann, T., Pawelczyk, M., Kasneci, G.: Gaussian Membership Inference Privacy. Conference on Neural Information Processing Systems (2023).
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G.: ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences (2023), 103, 102274.
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., & Kasneci, G.: Deep neural networks and tabular data: A survey. IEEE Transactions on Neural Networks and Learning Systems (2022).
Rong, Y., Leemann, T., Borisov, V., Kasneci, G., & Kasneci, E.: A consistent and efficient evaluation strategy for attribution methods. In Proceedings of the International Conference on Machine Learning 2022 (18770-18795).
Pawelczyk, M., Broelemann, K., & Kasneci, G.: Learning model-agnostic counterfactual explanations for tabular data. In Proceedings of The Web Conference 2020 (pp. 3126-3132).
Jenders, M., Kasneci, G., & Naumann, F.: Analyzing and predicting viral tweets. In Proceedings of the 22nd International Conference on World Wide Web (pp. 657-664)
Kasneci, G., Ramanath, M., Sozio, M., Suchanek, F. M., & Weikum, G.: Star: Steiner-tree approximation in relationship graphs. In 2009 IEEE 25th International Conference on Data Engineering (pp. 868-879).
Kasneci, G., Suchanek, F. M., Ifrim, G., Ramanath, M., & Weikum, G.: Naga: Searching and ranking knowledge. In 2008 IEEE 24th International Conference on Data Engineering (pp. 953-962).
Suchanek, F. M., Kasneci, G., & Weikum, G.: Yago: a core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web 2007 (pp. 697-706).
More publications can be found here.