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Technische Universität München
Technische Universität München
Prof. Dr. Gjergji Kasneci

Professorship
Responsible Data Science
Further Responsibilities
Vice Dean Information Officer (SOT)
Founder and Spokesperson for AI in Finance
Affiliations
TUM School of Social Sciences and Technology
TUM School of Computation, Information and Technology
Munich Data Science Institute
Fellow of Konrad Zuse School of Excellence in Reliable AI


Research Focus

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.

Short CV

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.

Courses

Selected Awards & Funding

Recent Invited Talks (Selection)

  • Panel discussion on the topic "Civic Coding - Use and Application of Artificial Intelligence for the Common Good", Public Outreach Event, organized by the IT Section of the City of Munich, (June 2024)
  • 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)

Selected Publications

Yang, S., Yuan, C., Rong, Y., Steinbauer, F., Kasneci, G.: P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models. In Proceedings of the 2024 Annual Conference of the Association for Computational Linguistics (ACL 2024).

Prenkaj, B., Villaizán-Vallelado, M., Leemann, T., Kasneci, G.: Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), August 25–29, 2024, Barcelona, Spain.

Yang, S. and Kasneci, G.: Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

Leemann, T., Pawelczyk, M., Kasneci, G.: I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data. AAAI Conference on Artificial Intelligence (AAAI 2024).

Leemann, T., Pawelczyk, M., Kasneci, G.: Gaussian Membership Inference Privacy. Conference on Neural Information Processing Systems (NeurIPS 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 (ICML 2022).

Pawelczyk, M., Broelemann, K., & Kasneci, G.: Learning model-agnostic counterfactual explanations for tabular data. In Proceedings of The Web Conference (ACM Web Conference / WWW 2020).

Jenders, M., Kasneci, G., & Naumann, F.: Analyzing and predicting viral tweets. In Proceedings of the 22nd International Conference on World Wide Web (WWW 2013)

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 (ICDE 2009).

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 (ICDE 2008).

Suchanek, F. M., Kasneci, G., & Weikum, G.: Yago: a core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web (WWW 2007).

More publications can be found here.

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