Tom A.E. Oomen is a Full Professor in the Department of Control Systems Technology at the Eindhoven University of Technology (TU/e). He holds affiliations with the Mechanical Engineering School and the EAISI High Tech Systems institute. His research focuses on data-driven control, motion control of mechatronic systems, and system identification, with applications in industries like automotive, medical, and energy systems. Academically, he earned his MSc and PhD from TU/e, and held visiting positions at KTH (Sweden) and the University of Newcastle (Australia). He has received notable grants (NWO Veni/Vidi) and awards, including IEEE and Mechatronics Paper Prize recognition. Oomen is a Senior Member of the IEEE and serves as an Associate Editor for IFAC Mechatronics and IEEE Control Systems Letters. His work bridges fundamental research and industry collaboration, emphasizing advanced motion control and learning algorithms. Projects include PROACTHIS (projection-based control) and ML4CONTROL (AI-driven motion control). He has supervised 99+ works and contributed to over 500 research outputs.
Ron H.J. Peerlings is Associate Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e) , where he leads the Mechanics of Materials research group. Promoted to Associate Professor in 2007 after joining as Assistant Professor in 2000, he has built an extensive portfolio in theoretical and computational mechanics of materials. Education: PhD (1999) – Eindhoven University of Technology, thesis: Enhanced damage modelling for fracture and fatigue Post-doctoral research (1999–2000) – University of Cambridge, Engineering Department Research interests revolve around micromechanics , micro-plasticity , multiscale modelling , homogenisation , damage and fracture , and enriched continuum theories . His work spans advanced high-strength steels, composites, paper and fibrous networks, with strong emphasis on coupling rigorous theoretical developments to industrially motivated problems. His recent publications (2023-2025) demonstrate a clear trajectory towards integrating advanced experimental techniques (e.g., digital image correlation, micro-mechanical testing) with high-fidelity computational frameworks such as crystal-plasticity finite-element modelling, FFT-based solvers and micromorphic homogenisation. Dominant themes include: Deformation and fracture in lath martensite and dual-phase steels Hygro-mechanics of paper and fibrous networks Pattern-transforming mechanical metamaterials Discrete-to-continuum scale bridging methods Scientific awards are not explicitly listed in the provided material; however, his prolific output (294 research items, >6500 citations) attests to significant peer recognition. Teaching & supervision: He delivers courses on Computational Mechanics – Numerical Methods for Fluids and Solids and Fracture Mechanics – Theory and Application , and has supervised >80 student works and numerous PhD candidates whose names appear on joint publications. Laboratory & teams: He heads the Group Peerlings within the Mechanics of Materials cluster, maintaining close collaboration with the Mechanics of Materials Group Geers and extensive national/international experimental and computational networks.
Dr. Jeewanie Jayasinghe Arachchige is a Lecturer in the Department of Computer Science at Vrije Universiteit Amsterdam, Faculty of Science. She teaches undergraduate courses including Bachelor Project Computer Science, Professional Development, and Software Engineering Processes for the academic year 2024–2025. Her research focuses on process mining , healthcare informatics , and data security . She applies process mining to analyze healthcare pathways and subpopulation treatment variations, develops explainable AI frameworks for predictive analytics, and examines data governance in emerging architectures like Data Lakehouses. Her work intersects legal informatics, particularly formalizing Sri Lankan civil court processes using ontology engineering. Recent publications highlight trends in balancing simplicity and complexity in process modeling, Industry 4.0 healthcare applications, and cybersecurity in model-driven web development. She has contributed to over 20 peer-reviewed articles since 2006, spanning topics from service-oriented architectures to value network analysis. Her teaching and research emphasize practical applications of IT in healthcare, legal systems, and enterprise environments. No ancillary activities are currently recorded.
Dr. Peter J.F. Lucas is a Full Professor specializing in Datamanagement & Biometrics with over 35 years of experience in artificial intelligence, probabilistic graphical models, and clinical decision support systems. His research spans intelligent systems, machine learning, and eHealth, with a focus on applying Bayesian networks and probabilistic logic to medical and non-medical domains.
Prof. Jack van der Vorst is the Personal Professor of AgriFood Supply Chain Logistics at Wageningen University's Operations Research and Logistics Group. Previously serving as a member of the Board of Directors of Wageningen University & Research (until 2024) and General Director of the Social Sciences Group, he leads over 1000 personnel across three institutes. His advisory roles include the Topteam AgriFood (Science Captain), Top consortium for Knowledge and Innovation (TKI), The Sustainability Consortium (TSC), and Florensis BV's Supervisory Board. With 20+ PhD supervisions and 200+ publications, his research focuses on innovative logistics concepts in AgriFood systems, including supply chain resilience, sustainability, and system innovation. His work integrates modeling frameworks with practical industry applications, emphasizing perishable products, horizontal collaboration, and environmental efficiency. Research interests span AgriFood System Design, Supply Chain Strategy, and Performance Management. His recent studies address challenges like postharvest loss reduction in developing countries, CO2 emission minimization in cold chains, and circular economy implementation in mushroom supply chains. Methodologically, he employs multi-criteria decision models, optimization techniques, and simulation to address complex logistics problems. Publications highlight themes such as horizontal collaboration success factors, vulnerability assessment frameworks, and green supply chain design. His work bridges academic rigor with industry needs, often collaborating with policymakers and international organizations to promote sustainable agri-food systems.
Anna Salvati is an Associate Professor specializing in nanomedicine and drug delivery systems. Her research focuses on nanoparticle-cell interactions, biomolecular corona formation, and the physicochemical properties governing nanomaterial behavior in biological environments. Key research themes include cellular uptake mechanisms, toxicity of nanomaterials, and nanoparticle targeting strategies. Recent publications highlight her work on nanoparticle stability, membrane interactions, and environmental impacts of microplastics. Her studies integrate multiomics approaches, advanced imaging, and interlaboratory validation to address challenges in nanomedicine design and safety testing. Current projects explore the role of high-density lipoproteins in nanoparticle functionality and the modulation of drug release kinetics through material engineering.
Dr. Julia Kamenz is an Assistant Professor (Rosalind Franklin fellow) at the University of Groningen's Faculty of Science and Engineering, where she leads research in the Molecular Systems Biology group within the Groningen Biomolecular Sciences and Biotechnology Institute (GBB). Her work focuses on understanding the molecular mechanisms that regulate cell cycle progression and cell division. Dr. Kamenz received her undergraduate training in Biochemistry at the University of Tuebingen, completed her PhD at the Friedrich Miescher Laboratory of the Max Planck Society under Dr. Silke Hauf (defended February 2015 with highest honors), and conducted postdoctoral research at Stanford University with Prof. James E. Ferrell. Her PhD work was supported by a Boehringer Ingelheim Fonds fellowship, and her postdoc was funded by a German Research Foundation (DFG) Postdoctoral Fellowship. Her research expertise spans cell cycle regulation and dynamics, post-translational modifications, Xenopus laevis model systems, and live cell microscopy. Dr. Kamenz investigates how kinases and phosphatases intricately regulate cell proliferation and division, with particular interest in the molecular mechanisms that ensure faithful chromosome segregation during mitosis. Her recent work has revealed novel insights into mitotic checkpoint signaling, particularly in early embryonic development where these checkpoints appear to function differently than in somatic cells. Dr. Kamenz's publication record demonstrates a strong focus on the dynamics of cell cycle transitions, with recent papers appearing in high-impact journals including Nature, The Journal of Biological Chemistry, and The Journal of Cell Biology. Her research integrates experimental biochemistry, live-cell imaging, and computational modeling approaches to understand complex regulatory networks. ERC Starting Grant (November 2022) NWO Vidi Grant (July 2021) Mansour Postdoctoral Travel Award (2019) Dr. Kamenz has secured significant research funding including an ERC Starting Grant (€1.5 million) and an NWO XS grant (€50,000) for her project "What limits mitotic checkpoint signaling in the early embryo?" Her research contributes to understanding fundamental biological processes with implications for developmental biology and cancer research. She collaborates extensively within the University of Groningen and with international partners, particularly in the areas of cell cycle research and biophysical approaches to biological problems. Dr. Kamenz leads a research group focused on cell cycle regulation within the Molecular Systems Biology division of the Groningen Biomolecular Sciences and Biotechnology Institute. Her lab combines biochemical approaches using Xenopus egg extracts with live-cell imaging and computational modeling to dissect the molecular mechanisms controlling cell division.
René van de Molengraft is a Full Professor of Robotics at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e). He holds dual professorships in EAISI High Tech Systems and EAISI Foundational. His research focuses on autonomous robotic systems operating in human environments, emphasizing adaptability and task execution in varying conditions. Academic Background: MSc in Mechanical Engineering (TU/e, 1986) PhD in Identification of Mechanical Systems for Control (TU/e, 1990) Career progression: Assistant Professor (1990–2008) → Associate Professor (2008–2024) → Full Professor (2024) Research Interests: Autonomous robotics, human-robot interaction, agricultural automation, and control systems. His work emphasizes practical applications, such as the Tech United RoboCup team (world champion in multiple years) and large-scale robotics competitions. Projects: Key initiatives include the Tech United RoboCup team, the AI-matters manufacturing network, and the Frontiers in Autonomous Systems Technology (FAST) project. He has organized major robotics events like the 2013 and 2024 RoboCup World Championships in Eindhoven. Labs/Teams: Founder and leader of Tech United, a robotics team renowned for its achievements in robotic soccer and service robotics. Active in TU/e's Robotics research group and collaborations with industry partners.
Charles E.H. Berger serves as Professor by Special Appointment in Criminalistics at Leiden University's Institute for Criminal Law and Criminology since November 2011, a position funded by the Stichting Leerstoel Criminalistiek. He concurrently holds a principal scientist position at the Netherlands Forensic Institute (NFI), where he contributes to education, R&D strategy, and research on forensic evidence interpretation. His research program centers on logically sound interpretation of forensic evidence through probability theory and computational methods. Berger specializes in applying Bayesian statistics to forensic anthropology, personal identification, and evidential evaluation. His work emphasizes moving forensic science toward activity-level interpretations while managing contextual information to prevent bias. Berger plays a pivotal international role as member of ISO technical committee TC272, serving as lead editor for Part 4 (Interpretation) of the ISO-21043 Forensic Sciences standard. His scholarly contributions focus on improving forensic reasoning frameworks and establishing objective evaluation methodologies. His publications demonstrate consistent engagement with foundational forensic science challenges, particularly in developing statistically rigorous approaches to evidence interpretation that maintain scientific integrity within legal contexts. Berger actively promotes scientifically sound practices across the criminal justice system, emphasizing the importance of clear communication between forensic scientists, legal professionals, and other stakeholders to ensure proper understanding and application of forensic evidence.
Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.
Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.
Prof. Dr. Bob van de Water is a Professor of Drug Safety Science at Leiden University, where he serves as head of the Cancer Therapeutics and Drug Safety group within the Division of Drug Discovery & Safety at the Leiden Academic Centre for Drug Research (LACDR). His research focuses on understanding the molecular mechanisms underlying drug toxicity and cancer drug resistance. His primary research interests include: Adaptive cellular stress responses Cancer drug resistance and metastasis Cell signaling pathways Drug safety evaluation Functional genomics High throughput microscopy Molecular mechanisms of drug toxicity Toxicology Professor van de Water leads several significant research projects including studies on oncogenic protein tyrosine kinases in breast cancer, live cell imaging-based modeling of cellular toxicity pathways, and identification of novel druggable targets for different breast cancer types. He teaches in the Bio-Pharmaceutical Sciences programs at both BSc and MSc levels, offering courses on drug safety evaluation, signal transduction, and cellular toxicology. His research group includes current PhD candidates: Serkan Aslan Sibel Bahtiri Linda van den Berk Imke Bruns Gerhard Burger Professor van de Water has previously supervised numerous PhD students to completion, including: Olivier Béquignon Muriel Heldring Marije Niemeijer Lukas Wijaya Luc Bischoff
Maurice Heemels is a Full Professor at Eindhoven University of Technology (TU/e), leading the Control Systems Technology group. He holds additional professorships in EAISI Mobility, EAISI Foundational, EAISI Health, and EAISI High Tech Systems. His research focuses on hybrid and networked systems, emphasizing resource-aware control, event-triggered strategies, and cyber-physical systems integration. He is an IEEE Fellow and chairs the IFAC Technical Committee on Networked Systems. Academic Background: MSc and PhD in Mathematics (TU/e, 1995 and 1999, both summa cum laude ) Visiting Professorships: ETH Zurich (2001), UC Santa Barbara (2008) Industry Experience: Research & Development at Océ NV Research Interests: Hybrid Systems, Networked Control, Event-Triggered Control Model Predictive Control (MPC) in healthcare and high-tech systems Cyber-Physical Systems for applications like lithography and precision agriculture Key Contributions: Developed Hybrid Integrator-Gain (HIGS) systems and Projection-Based Control methodologies Recipient of a VICI Grant for wireless control systems research Oversaw over €7M in research funding from NWO, EU, and industry Awards & Recognition: Automatica Outstanding Service Award (2014) Best Paper Awards (EBCCSP 2017, etc.) Invited Keynote Speaker at ECC, CDC, and others Grants & Projects: Current Projects: COMEDI (Cost-effective Mechatronics), PROACTHIS (Projection-based Control) Past Projects: Fault Detection in Wafer Scanners, Drone-based Farming Labs & Teams: Active in TU/e’s Cyber-Physical Systems and Systems Engineering research groups, collaborating globally on nonsmooth dynamics and hybrid systems.
Laura Maruster is an Assistant Professor at the Faculty of Economics and Business, University of Groningen, Netherlands. She holds an MSc from the West University of Timisoara, Romania, and a PhD in Technology Management from Eindhoven University of Technology, Netherlands. Her research focuses on process mining, process modelling, and healthcare networks, with applications in operations research and data science. She collaborates extensively with industry and public sector partners on projects like the Casimir initiative (RuG and Gasunie). Teaching areas include business processes, data mining, and research methodology. Recent work includes studies on healthcare logistics optimization and process mining beyond traditional workflows. Her publications appear in leading journals such as BMJ Open , IEEE Transactions on Engineering Management , and Computers in Industry . Research interests span healthcare networks, emergency medical services analytics, and enterprise process optimization. Notable recent contributions include analyzing interhospital patient transfers and redesigning engineering design processes using data-driven methods.
David Mann serves as an Associate Professor at Vrije Universiteit Amsterdam in the Faculty of Behavioural and Movement Sciences, with additional appointments at the Institute for Brain and Behavior Amsterdam (IBBA) and Amsterdam Movement Sciences - Sports (AMS). His research focuses on the intersection of vision science, sports performance, and cognitive processes, particularly examining how visual impairments affect athletic performance and everyday functioning. Dr. Mann's research interests center on visual impairment in athletes, gaze behavior during sports performance, visual search patterns, and talent identification. His work spans multiple disciplines including sports psychology, cognitive neuroscience, and adaptive sports, with particular emphasis on how visual field and acuity limitations impact performance in basketball, football, and ball sports. His fingerprint analysis reveals strong expertise in Visual Impairment (100%), Athletes (91%), Visual Acuity (67%), Visual Field (53%), Visual Search (37%), and Gaze Behavior (33%). His recent publications demonstrate a consistent focus on understanding the relationship between visual perception and sports performance. Key research trends include examining quiet eye duration in basketball shooting, the effects of vision loss on naturalistic search, the role of cognitive skills in youth football performance, dynamic anticipation in sports, and how people with vision impairment use gaze to hit balls. These studies employ methodologies including eye tracking, cognitive testing, and performance analysis across various sports contexts. Dr. Mann currently serves as Director of the International Paralympic Committee (IPC) Classification Research and Development Centre for Athletes with Vision Impairment, demonstrating his leadership in this specialized field. He is also active in teaching as Course Coordinator for Talent and Talent Identification. His research portfolio includes an active project titled "Developing sensory-cognitive predictors of everyday functioning with visual impairment" running from January 2023 to December 2025, which he conducts with colleague C. Olivers. Dr. Mann has supervised 6 PhD theses and teaches courses including Master Research Project, Talent and Talent Identification, and Talent Identification and Development.
