Transferring AI into actions - Accelerate the EU AI industrial valorization

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 guestprofessor 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 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.

Sebastian Trimpe is a Full Professor at RWTH Aachen University, where he heads the Institute for Data Science in Mechanical Engineering (DSME) since May 2020. Additionally, he is a founding director of the RWTH Center for Artificial Intelligence and, as of 2023, serves as one of its two Executive Directors. Sebastian's research focuses on fundamental questions at the crossroads of machine learning, control, networked systems, and robotics, with innovative applications thereof. Prior to his role at RWTH, he was a Max Planck Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen/Stuttgart. Sebastian earned his Ph.D. degree in 2013 from ETH Zurich, working with Raffaello D'Andrea at the Institute for Dynamic Systems and Control.

Wim Nuijten is the scientific director of the Eindhoven Artificial Intelligence Systems Institute (EAISI) at Eindhoven University of Technology and an AI and OR scientist and entrepreneur. He has two main scientific and industrial interests i) modeling real-life planning and scheduling problems and using AI, OR, and their combination to solve them and ii) using AI, OR, and their combination to do sports analysis like human pose estimation, cognitive performance training, and player and match analysis in football.

Peter Koerte has been Chief Strategy Officer at Siemens since February 2020, and in October 2020 he additionally took on the role of Chief Technology Officer. In these functions he is responsible for the development of corporate strategy as well as the digitization and establishment of the Xcelerator (X). He reports directly to the CEO of Siemens AG.

Peter began his career at Siemens in 2007 in Corporate Strategy. In 2011 he moved to Siemens’ healthcare business, where he held several management positions in high-margin units in diagnostics. Most recently, he headed the Digital Health unit for the development of AI-based diagnostic procedures in medicine. Before joining Siemens AG, he worked for the Boston Consulting Group (BCG).Peter holds a joint master’s degree in mechanical engineering and business administration from the Karlsruhe Institute of Technology, a PhD in strategy and international management from WHU Otto Beisheim School of Management and completed the General Management Program at the Harvard Business School.

Sabine Demey is the director of the Flanders AI Research Program. She brings together researchers from 10 research partners in Flanders (universities and research centres with imec as coordinating partner). Together they tackle challenging AI Research Challenges and apply the new AI methods in healthcare, in industry 5.0, for the energy transition, in society. She believes it is important for technological developments such as AI to have a meaningful impact on people, industry and society. Sabine is a computer scientist with a PhD in robotics. She has 20+ years industrial experience in research, product and business development in 3D printing, software for the manufacturing industry and for healthcare.

Vice President Innovation at Simulation and Test Solutions of Siemens Digital Industries Software

Industrial Mathematician and Innovator | Siemens Technical Fellow | Siemens Top Innovator and Inventor of the Year

Geographer, Vice-rector Science, Engineering and Technology and Vice-rector Sustainability at KU Leuven

Claus Bahlmann, works with Siemens Mobility as Head of AI and Principal for AI and CV. In these roles, he and his team are incubating strategic R&D programs for Siemens Mobility, such as the Assisted and Driverless Driving for Rail program or the Deep Learning Factory, Siemens Mobility's ecosystem for Machine & Deep learning development. He is serving the BMBF (Germany's Ministry of Education & Research) initiated platform Lernende Systeme - AG Mobilität to collaborate across German industry and research organizations on strategic directions to advance AI for society. Before joining Siemens Mobility, Claus worked since 2004 as Research Scientist for Siemens Corporate Technology in Princeton, NJ, USA, developing Machine Learning and Computer Vision technology for ADAS, healthcare, and security applications. He graduated from University of Freiburg in 2003 with a PhD degree on Machine Learning with "summa cum laude". He has co-authored more than 20 publications in Computer Vision, Machine Learning & ADAS with 2700+ citations. He was co-awarded best-paper awards in IV 2008 and IWFHR 2002 and the Wolfgang-Gentner-Nachwuchsförderpreis in 2003.

Head of Research Group Semantic & Reasoning

Major research areas are

• enterprise data strategies
• natural language processing
• data modeling and semantic technologies
• graph-based data management / knowledge graphs
• logic-based reasoning
• diagnostics and root-cause analysis
• data mining and machine learning
• edge analytics / AI on embedded devices
• software architectures for data analytics and artificial intelligence

Strengthening Siemens' position as the innovation leader in the domain of simulation & digital twin.

Driving consistency and coherency in the research and technology roadmap of Siemens Technology's Simulation & Digital Twin group, making the lifecycle digital twin real in end-user applications, while continuously incubating new ideas and concepts for the future, like the industrial metaverse.

Chair Digital Twin for Smart and Sustainable Products

Research topics:

• Flexible multibody simulation
• Model reduction
• State-input-parameter estimation

Assistant Professor at KU Leuven, Declaratieve Talen en Artificiële Intelligentie (DTAI)

Technology Manager - Predevelopment Mechatronics & Customer Applications

• Digital manufacturing & closed loop systems
• Data analysis on edge and cloud systems
• Robots based automation

Specialties: Computer Vision, Machine Learning, Deep Learning
Dynamic 3D Scene Understanding (object recognition, segmentation, tracking, motion prediction)

Currently, I am heading the Mobile Perception Systems lab of Eindhoven University of Technology. The Mobile Perception Systems Lab researches methods in Artificial Intelligence that allow mobile autonomous systems to perceive their environment. Our long-term goal is to realize AI that can anticipate on future events in highly dynamic and complex environments. We always validate our AI methods 'in the loop', meaning in the context of challenging real-world applications, mainly from the industry domains of automotive, transportation, and logistics.

Antonello Monti received his M.Sc degree (summa cum laude) and his PhD in Electrical Engineering from Politecnico di Milano, Italy in 1989 and 1994 respectively. He started his career in Ansaldo Industria and then moved in 1995 to Politecnico di Milano as Assistant Professor. In 2000 he joined the Department of Electrical Engineering of the University of South Carolina (USA) as Associate and then Full Professor. Since 2008 he is the director of the Institute for Automation of Complex Power System within the E.ON Energy Research Center at RWTH Aachen University. Since 2019 he holds a double appointment with Fraunhofer FIT where he is developing the new Center for Digital Energy in Aachen.

Dr. Monti is author or co-author of more than 400 peer-reviewed papers published in international Journals and in the proceedings of International conferences. He is a Senior Member of IEEE, Associate Editor of the IEEE System Journal, Associate Editor of IEEE Electrification Magazine, Member of the Editorial Board of the Elsevier Journal SEGAN and member of the founding board of the Springer Journal “Energy Informatics”. Dr. Monti was the recipient of the 2017 IEEE Innovation in Societal Infrastructure Award.

Professor Konstantinos Gryllias is a mechanical engineering professor at KU Leuven, specialising in AI-based condition monitoring of rotating machinery. His research focuses on fault detection, diagnostics, and digital twins, combining signal processing, machine learning, and hybrid modelling. He leads projects in sectors such as manufacturing, energy, and transportation, and is affiliated with Leuven.AI and Flanders Make.

Skills and Expertise: Classification, Unsupervised Learning, Pattern Recognition, Machine Learning, Feature Extraction, Signal Processing, Structural Dynamics, Finite Element Analysis, Stress Analysis, Finite Element Modeling

Phuong Nguyen is an associate professor with the research group Electrical Energy Systems at the TU/e department of Electrical Engineering. His research interests include applications of ICT in smart energy systems, distributed state estimation, control and operation of the power system, distributed and computational intelligence, and applications in the future power delivery system. He is a founder of the digital Power & Energy Systems lab (digi-PES) which aims to enable an energy transition from micro energy grids towards the future integrated energy system. This laboratory environment is a cyber-physical ecosystem for students and researchers to explore innovations in various energy-related aspects of (but not limited to) nano/micro-grids, local energy communities, local flexibility/energy markets, optimal power/energy flow, and congestion management. Hand-in-hand with emerging (big) data and Internet-of-Things (IoT) domains, such research provides a foundation for comprehensive data-driven and inter-dependency models of energy system integration.

Toon Goedemé studied electrical engineering at KU Leuven. He received the Ph.D. degree in vision-based topological navigation from KU Leuven, in December 2006, under the guidance of Prof. L. Van Gool and T. Tuytelaars. Afterwards, he started teaching at the Technical University De Nayer, Sint-Katelijne-Waver, where he founded his research group Embedded and Artificially Intelligent Vision Engineering (EAVISE), in 2008. Nowadays, his group is integrated in the KU Leuven and consists of three professors (Joost Vennekens, Patrick Vandewalle, and himself), four postdocs and about 20 researchers, playing a vital role in the transfer of computer vision and AI know-how from academic research towards the industry. Since 2014, he has been an Associate Professor with KU Leuven. He is the (co)author of more than 190 international publications and was a project leader of more than 75 industrially co-founded research projects. Together with his team, he won several awards, such as the Best Paper Award at Embedded Vision Workshop CVPR 2015, the Best Demo Award at BNAIC 2015, the Best Paper Award at CGVCVIP 2016, the Willy Asselman Award for research achievements in 2016, and the Best Paper Award at Embedded Vision Workshop ECCV 2020. He is also an Associate Editor of the IET Computer Vision journal and the MDPI Journal of Imaging.

I am a Full Professor, holding the Manufacturing networks chair, and chairing the section Operations, Planning, Accounting and Control of the department of Industrial Engineering & Innovation Science at Eindhoven University of Technology. My expertise and tuition areas include probability theory, statistics, operations research, manufacturing networks, stochastic operations research and queueing models. My current research interests focusses on the modeling, design and control of manufacturing systems,

I am leading a strong research team that is known for its close ties with the high-tech industry and its ground breaking work on data-driven modeling, analysis, design and control of manufacturing systems. My group is active in various manufacturing domains, including the semi-conductor industry, low-volume high-mix high-complexity manufacturing, food processing industry, bio-manufacturing and warehousing. As a leading scientist in manufacturing networks, my aim for the upcoming years is to contribute to bringing Industry 4.0 and 5.0 into reality.

Jun Wu is an Associate professor of computational design at the Delft University of Technology, the Netherlands. Before joining the design engineering department at TU Delft, he was an HC Ørsted postdoc fellow in the department of mechanical engineering at DTU Denmark. He received his Ph.D. (2015) in computer science from TU Munich, Germany, and a Ph.D. (2012) in mechanical engineering from Beihang University, China..

His research is focused on computational design and digital fabrication, with an emphasis on topology optimization (which is sometimes referred to as generative design). His work received best paper awards at international conferences including the Symposium on Solid and Physical Modeling 2019, and the World Congress of Structural and Multidisciplinary Optimization 2019. He received the SMA Young Investigator Award from the Solid Modelling Association in 2021. He serves on the editorial board of Computer-Aided Design and Structural and Multidisciplinary Optimization.

Spearheading the deployment of artificial intelligence in energy management systems (EMS) and advanced distribution management systems (ADMS+DERMS) to heighten capabilities and enhance operational efficiency in grid management:

• Pioneering the integration of sophisticated AI analytics across descriptive, predictive, and prescriptive models to advance the operational intelligence of grid systems.
• Leading the implementation of cutting-edge deep learning technologies, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and reinforcement learning models including Soft Actor-Critic (SAC) and Deep Deterministic Policy Gradient (DDPG), to refine the performance of grid management software.
• Driving the incorporation of Machine Learning Operations (MLOps) into Continuous Integration/Continuous Deployment (CI/CD) practices into o ensure continuous training and efficient management of machine learning models.

Prof. Dr. Bart De Moor is gewoon hoogleraar aan het departement Elektrotechniek van de Katholieke Universiteit Leuven, België. Zijn onderzoeksinteresses omvatten numerieke lineaire algebra, systeemidentificatie, geavanceerde procescontrole, datamining en bio-informatica. Hij behaalde een doctoraat in toegepaste wetenschappen in 1988 aan dezelfde universiteit.

• Voorzitter VAIA (Vlaamse AI Academie)
• Co-auteur van het Vlaamse Beleidplan Artificiële Intelligentie
• Gewoon hoogleraar, KU Leuven