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Keynote Speakers

Exciting Keynotes at KI2022

Manuela Veloso

Managing Director, Head, J. P. Morgan Chase AI Research

Title: AI in Robotics and AI in Finance: Challenges, Contributions, and Discussion

Abstract: My talk will follow up on my many years of research in AI and robotics and my few recent years of research in AI in finance. I will present challenges and solutions on the two areas, in data processing, reasoning, including planning and learning, and execution. I will conclude with a discussion of the future towards a lasting human-AI seamless interaction.

Bio: Manuela Veloso is Head of J.P. Morgan Chase AI Research and Herbert A. Simon University Professor Emerita at Carnegie Mellon University, where she was previously Faculty in the Computer Science Department and Head of the Machine Learning Department. Veloso researches in Artificial Intelligence (AI) with focus on autonomous robots and recently in AI in Finance. She is past president of the Association for the Advancement of Artificial Intelligence (AAAI), and the co-founder and a Past President of the RoboCup Federation. Veloso is a Fellow of AAAI, AAAS, ACM, and IEEE. She is a member of the National Academy of Engineering.


Eyke Hüllermeier

Institut für Informatik, Ludwig-Maximilians-Universität München

Title: Representation and Quantification of Uncertainty in Machine Learning

Abstract: Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the recent past. This talk will address questions regarding the representation and adequate handling of (predictive) uncertainty in (supervised) machine learning. A specific focus will be put on the distinction between two important types of uncertainty, often referred to as aleatoric and epistemic, and how to quantify these uncertainties in terms of suitable numerical measures. Roughly speaking, while aleatoric uncertainty is due to randomness inherent in the data generating process, epistemic uncertainty is caused by the learner’s ignorance about the true underlying model. Going beyond purely conceptual considerations, the use of ensemble learning methods will be discussed as a concrete approach to uncertainty quantification in machine learning.

Bio: Eyke Hüllermeier is a full professor at the Institute of Informatics at LMU Munich, Germany, where he heads 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. Prior to joining LMU, he held professorships at several other German universities and spent two years as a Marie Curie fellow at the IRIT in Toulouse (France). Currently, he is also a Chief Scientist at the Fraunhofer Institute for Mechatronic Systems Design. His research interests are centered around methods and theoretical foundations of artificial intelligence, with a specific focus on machine learning and reasoning under uncertainty. Besides, he is interested in the application of AI methods in other disciplines, ranging from the natural sciences and engineering to the humanities and social sciences.  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.