Prof. Dr. Joeran Beel is head of the Intelligent Systems Group at the University of Siegen; Visiting Research Fellow at Trinity College Dublin, Ireland, where he previously was an Assistant Professor; and Visiting Professor at the National Institute of Informatics (NII) in Tokyo, where he previously was a postdoctoral researcher. Joeran Beel’s research focuses on automated machine learning & meta-learning, information retrieval, and recommender systems. He published more than 80 peer-reviewed publications and acts as a reviewer for venues such as SIGIR, ECIR, RecSys, UMAP, ACM TiiS, and JASIST. Joeran Beel acquired more than €2.5 million in funding for his research and business start-ups.

Carola Doerr, formerly Winzen, is a permanent CNRS researcher at the LIP6 Computer Science department at Sorbonne University in Paris, France. Her main research activities are centered around black-box optimization, a topic that fascinates her since her time when she worked as a business consultant for McKinsey & Company. Mathematician by training, Carola studies power and limits black-box optimization algorithms by mathematical and by empirical means. She also designs and analyzes optimization techniques for a variety of academic and industrial problems, ranging from biomedical applications to material design and sensor network configuration.

Carola has organized several events centered around black-box optimization algorithms, among them Dagstuhl seminars, Lorentz Center workshops, and special issues in IEEE Transactions on Evolutionary Computation (TEVC) and Algorithmica. She was program chair for PPSN 2020, FOGA 2019 and the theory tracks of GECCO 2015 and 2017. Carola is an associate editor of TEVC, of ACM Transactions on Evolutionary Learning and Optimization (TELO), and board member of the Evolutionary Computation journal. Her works have received several awards, among them a CNRS bronze medal, the Otto Hahn Medal of the Max Planck Society, and best paper awards at GECCO, CEC, and EvoApplications.

Aleksandra Faust is a Senior Staff Research Scientist and Reinforcement Learning research team co-founder at Google Brain Research. Previously, Aleksandra was with Robotics at Google, Waymo, and Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico (with distinction), and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Her research interests include learning-based autonomous systems and agents, safe and scalable reinforcement learning, learning to learn (AutoRL), social and population-based automation. Aleksandra won IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, VentureBeat, and was awarded Best Paper in Service Robotics at ICRA 2018, Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML 2019, and Best Paper of IEEE Computer Architecture Letters in 2020.

Luigi Nardi is an assistant professor of machine learning at Lund University and a research staff at Stanford University. His current research focuses on black-box optimization theory and its use in various practical applications, including AutoML, hardware design, database management, computer vision, and robotics. Prior to joining the faculty at LU, Luigi was a post-doc in the department of computing at Imperial College London and worked as a permanent research engineer at the financial firm Murex S.A.S. after completing his Ph.D. at Université Pierre et Marie Curie (UPMC) in Paris. Luigi is the founder and CEO of DBtune (www.dbtune.ai).

Luc De Raedt is full professor at the Department of Computer Science, KU Leuven, and director of Leuven.AI, the newly founded KU Leuven Institute for AI. He is a guest professor at Örebro University in the Wallenberg AI, Autonomous Systems and Software Program. He received his PhD in Computer Science from KU Leuven (1991), and was full professor (C4) and Chair of Machine Learning and Natural Language Processing Lab at the Albert-Ludwigs-University Freiburg, Germany (1999-2006). His research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of learning and reasoning, in particular, for his work on probabilistic and inductive programming. He co-chaired important conferences such as ECMLPKDD 2001 and ICML 2005 (the European and International Conferences on Machine Learning), ECAI 2012 and will chair IJCAI in 2022 (the European and international AI conferences). He is on the editorial board of Artificial Intelligence, Machine Learning and the Journal of Machine Learning Research. He is a EurAI and AAAI fellow, an IJCAI Trustee and received an ERC Advanced Grant in 2015.

Colin White is Head of Research at Abacus.AI. Previously, he obtained his PhD from Carnegie Mellon University, where he was supported by the NDSEG Fellowship. Colin White’s research focuses on neural architecture search and other areas of automated machine learning, explainability and fairness in machine learning, and recommender systems. A theme of his research is in creating tools for better benchmarking, and conducting large-scale, fair comparisons of existing methods. Colin regularly publishes at conferences such as NeurIPS, ICLR, and AAAI.