Aad van der Vaart is a Professor of Stochastics at Leiden University's Mathematical Institute. He was awarded the prestigious NWO Spinoza Prize in 2015 for groundbreaking work in mathematical statistics, particularly Bayesian methods applied to medical imaging, genetic data, and complex models. His research bridges pure mathematical theory with applied domains like neuroscience and astronomy. Research Interests : Van der Vaart focuses on infinite-dimensional Bayesian statistics, nonparametric models, and statistical genetics. His work emphasizes rigorous mathematical analysis of prior distributions and their impact on data-driven conclusions. Applications include gene network modeling and PET scan image reconstruction. Key Contributions : Authored influential books on estimation theory; pioneered modern Bayesian approaches to high-dimensional data. His Spinoza Prize funds will support interdisciplinary research and hiring new talent in statistical methods. Awards : NWO Spinoza Prize (2015), recognized as a global leader in statistical theory. Future Directions : Expanding into astronomical data analysis and medical applications, leveraging Bayesian frameworks for big datasets.
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.
Anna Grigolon is an Assistant Professor at the University of Twente , Netherlands, affiliated with the Transport Engineering and Management Research Group . Her research focuses on sustainable urban mobility , user-centric transport solutions , and travel behavior analysis using tools like discrete choice modeling , spatial analysis , and social psychology theories . Research Interests : Sustainable Urban Mobility Accessibility Modeling Travel Behavior Discrete Choice and Latent Class Modeling Spatial Analysis and GIS Shared Micromobility and Mobility Hubs Equity in Transport Planning Article Trends : Anna’s recent work (2025–2024) emphasizes mobility justice , 15-minute city transitions, and equity in transport access , particularly for marginalized communities like São Paulo favelas. She integrates digital tools (e.g., serious games, kiosks) and space-time metrics to evaluate mobility solutions. Projects : She currently leads the SmartHubs project and contributes to DREAMS and R-map , focusing on smart, equitable mobility systems in Europe and Saudi Arabia.
Jan Dijkstra is an Associate Professor at Wageningen University specializing in Animal Nutrition . His research focuses on dairy cattle nutrition, methane emissions, and nutrient efficiency. Academic Rank: Associate Professor Department: Animal Nutrition His work emphasizes: Mathematical modelling of methane production Phosphorus and nitrogen balance optimization Feed supplementation strategies for reduced emissions Genotype-environment interactions in dairy systems Recent publications highlight advancements in methane mitigation , nutrient cycling , and rumen microbiome dynamics . He serves as a promotor for multiple PhD projects and contributes to datasets on equine microbiomes and rumen metabolites. Scientific Awards American Feed Industry Association Award (2015) Publicatieprijs ASG (2013) Supervision PhD candidates: Kozorezov, Henry, Koning, Vivares Martinez EngD candidate: Holshof
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.
Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.
Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)
Hans Bouwmeester is a Full Professor in Toxicology at Wageningen University & Research, specializing in chemical risk assessment, nanomaterial toxicity, and in vitro modeling. His work focuses on integrating artificial intelligence and physiologically based kinetic (PBK) models to predict toxic effects of contaminants like organophosphate pesticides, microplastics, and mycotoxins. He leads projects exploring the health impacts of foodborne contaminants in inflammatory bowel disease and developing animal-free testing methods. Key collaborations include EU initiatives like the ONTOX project and the GUTTEST program, which utilize advanced in vitro models such as gut-on-a-chip systems. Research interests span toxicokinetics, nanoplastics exposure, and the application of Bayesian networks for nanomaterial hazard ranking. His team addresses translational challenges in linking in vitro data to in vivo outcomes, with a focus on bile acid metabolism and cardiotoxicity prediction. Bouwmeester has supervised over 10 PhD candidates, including studies on microplastic hazard assessment, nanomaterial gastrointestinal fate, and AI-driven data extraction for risk assessment. Notable contributions include datasets on nanoplastics' protein corona effects and transcriptomic analyses of intestinal cell responses. His work is published in journals like Ecotoxicology and Environmental Safety , Toxicology , and Environmental Science & Technology , emphasizing open-access research and interdisciplinary approaches to chemical safety.
Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).
Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.
Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.
Joris M. Mooij is a Professor of Mathematical Statistics at the Korteweg-De Vries Institute of the University of Amsterdam, Netherlands. His research focuses on causality, spanning causal modeling, discovery, and inference with applications in biology, medicine, fairness, and business analytics. He combines mathematical modeling with statistical and algorithmic approaches in his work. Dr. Mooij received his PhD with honors from Radboud University Nijmegen in 2007, focusing on approximate inference in graphical models. After postdoctoral work at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, he obtained an NWO VENI grant in 2011 for further postdoctoral research at Radboud University. He became Assistant Professor at the University of Amsterdam's Informatics Institute in 2013, was promoted to Associate Professor in 2017, and became a full Professor of Mathematical Statistics in 2020. Dr. Mooij's research centers on causal inference, with particular expertise in structural causal models, cyclic causal systems, and causal discovery algorithms. His work addresses fundamental questions about when causal relationships can be identified from observational data and how to develop robust causal discovery methods that work in complex real-world settings with latent variables, cycles, and selection bias. He has made significant contributions to understanding the limitations of existing causal discovery approaches and developing new methods that overcome these limitations. His research group organizes the Amsterdam Causality Meeting series and develops theoretical frameworks for causal modeling that encompass both acyclic and cyclic systems. Dr. Mooij has collaborated extensively on applications of causal methods in biological systems, including protein signaling networks and gene expression data. The group's recent work explores performative predictions, causal domain adaptation, and robust causal discovery methods that account for selection bias and missing data. Dr. Mooij has received numerous awards for his research, including: Best paper award at UAI for "Establishing Markov equivalence in cyclic directed graphs" IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award ICML Test of Time Honorable Mention Best student paper award at UAI 2010 He has secured competitive research funding through an NWO VENI grant, NWO VIDI grant, and an ERC Starting Grant, which supported the establishment of his research group consisting of 3 PhD students and 3 postdocs focused entirely on causality. Dr. Mooij has supervised several PhD students, including Tineke Blom, whose work on "Causality and Independence in Perfectly Adapted Dynamical Systems" significantly influenced his thinking about causality in complex systems. He has co-taught the MasterMath course on Causality and published lecture notes titled "A Mathematical Introduction to Causality." His research continues to push the boundaries of causal inference methodology and its applications across diverse scientific domains.
Ine van der Fels-Klerx is an Associate Professor in Business Economics at Wageningen University & Research, with a focus on food safety and risk management. She is also a Programme and Account Management lead, overseeing interdisciplinary projects. Her work integrates economic analysis with food safety challenges, particularly addressing mycotoxin contamination, climate change impacts, and circular economy solutions. Key collaborations include projects on early warning systems for mycotoxins and cost-effective monitoring strategies for contaminants in food supply chains. Her research spans topics such as alternative protein sources, insect-based feed production, and resilient supply chain designs. Notable projects include developing decision support tools for risk-based monitoring and assessing the economic feasibility of food safety interventions. She has advised multiple PhD candidates, including those studying mycotoxin management in maize and climate change effects on aflatoxins. Media contributions highlight her expertise in food safety, including interviews on predicting grain contamination and smart monitoring techniques. She actively participates in workshops and conferences, discussing digitalization in food systems and the role of artificial intelligence in food safety prevention.
Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.
Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)
