UDM-KDD’24 - Keynotes

Invited Keynote Speakers

	Eyke Hüllermeier, Ludwig-Maximilians-Universität München, Germany Short bio: Dr. Eyke Hüllermeier is a full Professor at the Institute of Informatics at LMU Munich, Germany, where he holds the Chair of Artificial Intelligence and Machine Learning. He studied mathematics and business computing, received his PhD in Computer Science from Paderborn University in 1997, and a Habilitation degree in 2002. Before joining LMU, he held professorships at several other German universities (Dortmund, Magdeburg, Marburg, Paderborn) and spent two years as a Marie Curie fellow at the IRIT in Toulouse (France). His research interests are centered around methods and theoretical foundations of artificial intelligence, with a particular focus on machine learning, preference modeling, and reasoning under uncertainty. He has published more than 400 articles on related topics in top-tier journals and major international conferences, and several of his contributions have been recognized with scientific awards. Professor Hüllermeier is Editor-in-Chief of Data Mining and Knowledge Discovery, a leading journal in the field of AI, and serves on the editorial boards of several other AI and machine learning journals. He is currently President of EuADS, the European Association for Data Science, a member of the Strategy Board of the Munich Center for Machine Learning (MCML), and a member of the Steering Committee of the Konrad Zuse School of Excellence in Reliable AI (relAI).
	Yufei Han, National Institute for Research in Computer Science and Automation, France Short bio: Dr. Yufei Han is currently a senior research scientist at the PIRAT team, INRIA France. Yufei’s research interests include AI-driven cyber security analysis, e.g., malware detection and network intrusion detection. He also focuses on analyzing the adversarial vulnerability of machine learning approaches in security-critical applications. The goal of his work is to provide a trusted machine learning service for cybersecurity data analysis and encourage a synergy between machine learning techniques and cyber security. He has authored over 50 research publications on top-tiered machine learning and cyber security conferences, e.g., KDD, ICML, ICLR IJCAI, AAAI, ICDM, CCS, NDSS, Usenix Security, and S&P Oakland. He has also filed 27 US patents, 12 of which have been already granted.
	Meelis Kull, University of Tartu, Estonia Short bio: Dr. Meelis Kull is an Associate Professor of machine learning, leading the machine learning research group at the Institute of Computer Science, University of Tartu, Estonia. He is also an honorary senior research associate at the University of Bristol, UK. His research interests are machine learning, artificial intelligence, and data science, with a focus on uncertainty quantification and trustworthiness. He enjoys theoretical research with a clear path to applications, and his work is primarily focused on the statistical side of machine learning.