Received his Licenciado (5-years) degree in Electrical and Computer Engineering from the University of Porto (UP) in 2006, followed by an M.Sc. in Data Analysis and Decision Support Systems from UP in 2008. In 2013, he completed his Ph.D. in the Doctoral Program in Sustainable Energy Systems at UP. He is the Coordinator of the Center for Power and Energy Systems at INESC TEC. Worked on international and national projects on wind power forecasting and integration into power system operations. Key contributions include control room tools for EU ANEMOS.plus, novel forecasting methods in the U.S. DOE ARGUS project, and statistical models for the EPREV project, which led to co-founding Prewind. Participated in EU projects like SuSTAINABLE, evolvDSO, HYPERBOLE, and InterConnect, and coordinated forecasting systems development under Smart4RES. Coordinates the European project AI4REALNET, which consists of the application of AI to the operation of critical infrastructures. Held editorial roles in IEEE journals. Active in committees such as IEEE Working Group on Energy Forecasting, vice-chair of the IEEE Task Force on Data Sharing, and CIGRE WG2.42 Secretary.

Member of the organization committee of SEST (International Conference on Smart Energy Systems and Technologies) 2019 and PSCC (Power Systems Computation Conference) 2020 and 2022. Member of the technical program committee of PSCC 2018 and 2020, and SEST 2019-2024. Organization of a Special Session “Big data and machine learning for power systems” at IEEE PowerTech 2021, Madrid, Spain. Organization of the Workshop “Data Sharing in Smart Grids” at IEEE SmartGridComm 2022, Singapore. Member of the program committee of the workshop “AI for Critical Infrastructure” at IJCAI 2024, Jeju Island, South Korea. Organization of tutorial session on ECML-PKDD 2015, “Eureka! - How to build accurate predictors for real-valued outputs from simple methods”.

Herke van Hoof is currently an associate professor at the University of Amsterdam in the Netherlands, where he is part of the Amlab. Before joining the University of Amsterdam, Herke van Hoof was a postdoc at McGill University in Montreal, Canada, where he worked with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. He obtained his PhD at TU Darmstadt, Germany, under the supervision of Professor Jan Peters, where he graduated in November 2016. Herke got his bachelor's and master's degrees in Artificial Intelligence at the University of Groningen in the Netherlands.

His work focuses on algorithmic improvements in the field of reinforcement learning. Previous and ongoing projects and collaborations include a public-private project in the "LIFT" national program on learning train shunting heuristics from interaction data where Herke was principle investigator, the “Efficient deep learning” public-private national project, where Herke supervised one PhD student on the topic of improving active learning strategies from data; the Delta lab collaboration between the University of Amsterdam and Bosch aimed at fundamental research in deep learning with an eye on autonomous vehicles where Herke was lab manager. Herke was also involved in collaboration with industry researchers from companies such as Ortec (on deep learning for logistics problems), Qualcomm (on learning efficient execution for edge devices), and TenneT (on learning hierarchical strategies for power grid control).

Herke was previously workflow co-chair of the 2017 International Conference on Machine Learning. Furthermore, he co-organized several workshops (the 2024 Benelux workshop on reinforcement learning, the 2016 workshop on robot-environment interaction for perception and manipulation co-located with Robotics: Science and Systems; the 2014 workshop on Active Learning in Robotics co-located with the Humanoids conference). He was also one of the coordinators of a graduate course in the Netherlands on ”Reinforcement learning for adaptive hybrid intelligence”.

Manuel Schneider received his M.Sc. in theoretical Physics from ETH Zürich in 2012. After his studies, he worked in the development of data science and machine learning applications in the private sector before returning to ETH Zürich in 2017 and receiving his Ph.D. in digital Bioethics, focused on the study of ethical questions using machine learning and through the modeling of large-scale social systems. Since 2020, he has been involved in the Flatland open-source framework for railway network simulations and re-enforcement learning. Currently, he is the Head of Research at the Flatland Association, a non-profit organization focused on open research in resource allocation problems around railway network operations using machine learning.

Manuel was co-organizer of multiple conference workshops on machine learning (Applied Machine Learning Days 2020, Lausanne Switzerland; The Web Conference 2021, Ljubljana, Slovenia; Applied Machine Learning Days 2021, Lausanne Switzerland), co-chaired two conference tracks (Applied Machine Learning Days 2024, Lausanne Switzerland; Applied Machine Learning Days 2025, Lausanne Switzerland), helped in the organization and facilitation of multiple machine learning competitions on railway dispatching (Flatland challenges, among them at NeurIPS 2020), and co-organized three international standalone multi-day workshops (Flatland Workshops 2022, 2023, 2024 in Switzerland) on simulations and re-enforcement learning for railway network operations.

Marco Mussi received his B.Sc. in Engineering of Information Systems in 2017 and a M.Sc. in Computer Science and Engineering in 2019, followed by a Ph.D. in Information Technology (with honors) from Politecnico di Milano in 2024. He is currently a Postdoctoral Researcher with the Dipartimento di Elettronica, Informazione e Bioingegneria at Politecnico di Milano, specializing in artificial intelligence and machine learning, with a particular focus on foundational aspects of reinforcement learning.

He is a contributor to the Horizon Europe project AI4REALNET, which addresses foundational aspects of both AI and human-AI collaboration in critical infrastructures such as energy, railway, and air traffic management. He has authored numerous publications in top-tier journals and conferences, including ICML, NeurIPS, KDD, and AISTATS. He organized scientific events, in particular the 15th European Workshop on Reinforcement Learning (EWRL 2022), one of the largest independent machine learning workshops worldwide, attracting over 250 participants. Additionally, he has served as a Program Committee member and Reviewer for major conferences such as NeurIPS, ICML, AAAI, ICLR, AISTATS and for top-tier journals published by IEEE, Springer, and Elsevier.