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Marc Jochen UetzView profile

Professor

• Operations Research
• Computer Science
• Artificial Intelligence
• +11 more

Marc Jochen Uetz is a Full Professor at the Mathematics of Operations Research department and affiliated with the Digital Society Institute. His research spans operations research and computer science, focusing on scheduling, game theory, and optimization problems. PhD in Mathematics from Technische Universität Berlin Research Interests: Active in algorithmic game theory and stochastic scheduling, he investigates equilibrium models, price of anarchy, and efficient resource allocation in transportation and network systems. His work contributes to UN Sustainable Development Goals related to education and infrastructure. Publication Trends: Recent work combines game theory with two-stage facility location, network routing, and stochastic scheduling of Bernoulli-type jobs. Key keywords include Nash equilibrium, approximation algorithms, and dynamic programming. Scientific Recognition: Excellent Reviewer Award (2017) Teaching Award (2018) Academic Activities: Currently chairs Platform Wiskunde Nederland, contributes to editorial work, and delivers invited talks at international conferences like IJCAI 2024.

Giovanni SilenoView profile

Lecturer

• Artificial Intelligence
• Logic Programming
• Knowledge Representation
• +5 more

Giovanni Sileno is an academic specializing in Artificial Intelligence, Logic Programming, and Knowledge Representation, with teaching appointments at the University of Amsterdam, EPITA Paris, and Université Pierre et Marie Curie. His primary affiliation is with the Informatics Institute at the University of Amsterdam where he serves as a Lecturer. His research interests span multiple domains within computer science, focusing particularly on formal methods for normative systems, agent-based programming, and logic-based knowledge representation. His work bridges theoretical computer science with practical applications in policy modeling, forensic science, and business information systems. Dr. Sileno has developed several notable software projects including AgentScriptCC for single-threaded intentional agents, DCPLschema for normative policy specifications, and libraries for normative primitives in Answer Set Programming. His GitHub activity shows consistent contributions from 2012 through 2025, indicating active engagement in both research and development. His publications reflect a strong focus on formal methods applied to practical problems, with particular emphasis on normative systems, agent architectures, and logic programming applications. The research trajectory shows evolution from foundational topics in logic and numeral systems toward increasingly sophisticated applications in policy modeling and agent-based systems. As an educator, he has taught across multiple institutions and disciplines, covering topics from basic programming paradigms to advanced knowledge representation and formal modeling techniques. His teaching spans undergraduate to master's level courses in information studies, cognitive science, data science, and forensic science.

Ioana PopescuView profile

Research Professor

• Hydrology
• Flood Modelling
• River Systems Modelling
• +7 more

Ioana Popescu is a prominent hydroinformatics researcher affiliated with the IHE Delft Institute for Water Education , where she has worked since 2001. Previously, she served as an Associate Professor at the Faculty of Hydrotechnics, Timisoara, Romania (1990-1999), and a postdoc researcher at the National Research Council of Canada (2000).

Angela MeyerView profile

Assistant Professor

• Energy Meteorology
• Artificial Intelligence
• Atmospheric Science
• +5 more

Dr. Angela Meyer is an Assistant Professor of Energy Meteorology and Artificial Intelligence at TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing since October 2023. She concurrently leads the Energy Weather & AI Lab at the Bern University of Applied Sciences (BFH), School of Engineering and Computer Science. She earned her PhD in atmospheric physics from ETH Zurich (2015) and a master’s degree in mathematics from the University of Cambridge (2009). Research Focus: Intersection of data science, atmospheric science, and renewable energy applications. Machine learning for solar and wind energy forecasting. Federated learning for privacy-preserving wind turbine condition monitoring. Satellite-based solar radiation retrieval and bias correction. Probabilistic intraday and sub-seasonal forecasting. Her research is supported by major grants from the Swiss National Science Foundation (SNSF) and Innosuisse , and she is a project partner in the Horizon Europe UrbanAIR initiative. Scientific Contributions: Over 40 peer-reviewed publications since 2015 in journals such as Applied Energy , Solar Energy , Energy and AI , and Journal of Climate . Key publications include advances in deep generative models for solar forecasting, federated learning in renewable energy, and AI-based satellite retrieval of solar radiation. Active reviewer for Applied Energy , Energies , and program committee member for ECML PKDD and LOD conferences. Research Team & Supervision: Dr. Meyer currently supervises six PhD candidates and six postdoctoral researchers across her labs at TU Delft and BFH. Her group focuses on AI-driven solutions for renewable energy reliability and resilience. Laboratories & Collaborations: Energy Weather & AI Lab – Bern University of Applied Sciences. GRS Lab – TU Delft, Department of Geoscience and Remote Sensing. Active collaborations with ETH Zurich, Siemens Smart Infrastructure, Hexagon AB, and NVIDIA. For more information, visit her personal website or ResearchGate profile .

Bart van ErpView profile

Researcher

• Bayesian Inference
• Machine Learning
• Signal Processing
• +3 more

Bart van Erp serves as a University Researcher within the Department of Electrical Engineering at Eindhoven University of Technology, actively contributing to the Signal Processing Systems (SPS) group and Bayesian Autonomous Intelligent Systems lab (BIASlab). He completed his PhD in June 2025 under Prof. Bert de Vries with dissertation work on automated Bayesian hearing aid design. His academic foundation includes: Bachelor of Science in Electrical Engineering, cum laude (2015-2018) Master of Science in Electrical Engineering, cum laude with honors (2018-2020), supported by ALSP scholarship Van Erp's research centers on Bayesian inference frameworks for real-world signal processing challenges, particularly in hearing aid personalization and autonomous systems. He pioneers message-passing algorithms on factor graphs to implement variational free energy minimization, enabling adaptive user preference learning and energy-efficient control systems. His work bridges theoretical probabilistic modeling with clinical applications in hearing impairment solutions. Analysis of his 2024-2025 publications reveals consistent focus on Bayesian neural networks and active inference methodologies, with increasing emphasis on practical implementations for assistive devices. Key trends include Dirichlet process applications for context-aware systems and depth estimation improvements in uncertainty-aware computer vision. Recognition includes: ALSP scholarship for Master's excellence As project member in the CoHear initiative (2017-2025), he contributed to collaborative hearing device design while securing open-source development through ReactiveMP.jl. Though newly minted as a PhD, he has not yet supervised graduate students but actively supports university education (0.3 FTE during studies). His dual role as Product Lead/Co-Founder at Lazy Dynamics BV since May 2023 demonstrates translational research impact. Within BIASlab, van Erp drives the integration of probabilistic programming with medical device innovation, maintaining strong industry partnerships in hearing technology through GN Hearing collaborations and open-source tooling.

Max HinneView profile

Assistant Professor

• Bayesian Statistics
• Machine Learning
• Neuroscience
• +6 more

Max Hinne is an assistant professor at the Department of Artificial Intelligence, Radboud University, Nijmegen, The Netherlands, where he leads the Uncertainty in Complex Systems research group. His work bridges artificial intelligence, neuroscience, and statistics through advanced Bayesian methodologies. Dr. Hinne's research focuses on Bayesian modeling of brain networks using neuroimaging data. His primary interests include: Bayesian nonparametric models, particularly Gaussian processes Structural and functional brain connectivity analysis Predictive modeling of neural systems Causal inference frameworks Uncertainty quantification in complex systems Development of computational tools for neuroscience His approach emphasizes how probabilistic methods can address uncertainty in complex biological systems while providing interpretable models of brain function. Analysis of Dr. Hinne's publication trajectory reveals a consistent focus on Bayesian methods applied to increasingly diverse domains. Starting with foundational work in brain connectomics, his research has expanded to include applications in developmental psychology, medical genetics, and educational technology. His most recent work demonstrates sophisticated integration of nonparametric Bayesian methods with domain-specific challenges, particularly in handling uncertainty in complex, high-dimensional data across multiple scientific fields. Dr. Hinne actively mentors students and invites master's thesis projects focused on Bayesian nonparametric methods for neuroimaging data. He has developed several software tools including the Bayesian Connectomics Toolbox (BaCon), latent space modeling code, and GP CaKe for causal inference. His research group maintains strong connections with the Donders Institute for Brain, Cognition and Behaviour, facilitating interdisciplinary collaborations between statisticians, neuroscientists, and domain experts.

Anouk Waeijen-van DiepenView profile

Researcher

• Machine Learning
• Biomedical Engineering
• Mechanical Ventilation

Anouk Waeijen-van Diepen is a PhD candidate at the Biomedical Diagnostics Lab within the Signal Processing Systems group at Eindhoven University of Technology (TU/e), focusing on integrating machine learning with mechanical ventilation systems. Her work aims to address patient-ventilator asynchrony through advanced algorithms and simulated clinical data . Education : MSc in Electrical Engineering (2017, TU/e) BSc in Bioinformatics/Biostatistics (2014, TU/e) Pre-Master in Electrical Engineering (2015, TU/e) Her research spans biomedical signal processing , machine learning applications in healthcare, and ventilator system optimization . She specializes in asynchrony detection using clinical and simulated data , with a focus on intensive care unit (ICU) environments. Key trends in her publications include adversarial learning for waveform generation, model-based approaches for patient effort estimation, and automated detection algorithms validated through peer-reviewed journals like Computer Methods and Programs in Biomedicine and Heliyon . She has contributed to IEEE symposia and European Signal Processing Conferences . Projects include: STW Zero 15-06 P2 (2017–2024): Autonomous Acoustic Systems Smart Monitoring (2015–2020): Real-time healthcare data analysis

Frank van HarmelenView profile

Professor

• Artificial Intelligence
• Semantic Web
• Knowledge Representation
• +3 more

Prof. Frank van Harmelen is a Full Professor at Vrije Universiteit Amsterdam, holding positions in the Faculty of Science (Artificial Intelligence), Network Institute, and Knowledge Representation and Reasoning department. He also serves as an Adjunct Professor at Wuhan University of Science and Technology and Wuhan University Faculty of Information Sciences since 2014 and 2015, respectively. He earned his PhD in Artificial Intelligence from the University of Edinburgh (1985–1989), focusing on meta-level inference efficiency. His research spans Semantic Web, Knowledge Representation, Ontology Engineering, and neurosymbolic AI, with contributions to UN Sustainable Development Goals related to technological innovation. His recent work emphasizes knowledge graphs (e.g., KG4NH for nutrition/health, OfficeGraph for IoT), neurosymbolic integration, and applications in healthcare, sustainability, and education. He has led projects like the HumanE AI Network and contributed to datasets such as MetaLink and equivalence class analyses in LOD. Awards: Fellow of European AI Society (2007), Member of Royal Dutch Academy of Sciences (2017), Ten Year Impact Award (2012) Activities: Steering roles in international conferences, member of prestigious academies, and involvement in EU/US Agent Markup initiatives. Teaching: Courses on AI in Health and Introduction to AI. Grants/Projects: Principal Investigator for HumanE AI Network (2020–2024), contributed to IoT and smart grid solutions. His 292+ publications include foundational texts like the Semantic Web Primer and recent advancements in knowledge graph completion and guideline-informed reinforcement learning.

Wouter KouwView profile

Assistant Professor

• Probabilistic Machine Learning
• Variational Bayesian Inference
• Active Inference
• +6 more

Wouter Kouw is an Assistant Professor at the Electrical Engineering department of Eindhoven University of Technology (TU/e) , leading the Bayesian Intelligent Autonomous Systems lab. With a dual PhD in Computer Science (2018, TU Delft) and MSc in Neuroscience (2013, Maastricht University) , he bridges neurobiology and artificial intelligence through variational Bayesian inference and active inference frameworks. Research Focus : Probabilistic machine learning systems using message passing algorithms on factor graphs , applied to mobile robotics and adaptive control Key Projects : CONTACT-AI (contact-rich robot navigation), FEP-walker (active inference-based locomotion), and BayesBrain (hybrid neuro-in-silico computing) His work spans nonlinear system identification , uncertainty quantification , and sensor modeling , with recent publications in IEEE Transactions , Entropy , and Communications in Computer and Information Science . Awards include the Niels Stensen Fellowship (2017) and TU/e Team Science Award nomination (2023) . Collaborations extend to institutions in Germany, USA, and Denmark, with teaching responsibilities in Bayesian Machine Learning and Neuro Computation .

Jelle ZuidemaView profile

Associate Professor

• Natural Language Processing
• Explainable AI
• Cognitive Modelling
• +6 more

Dr. Jelle Zuidema is Associate Professor in Natural Language Processing, Explainable AI and Cognitive Modelling at the Institute for Logic, Language and Computation (ILLC) of the University of Amsterdam, with primary affiliation in the Natural Language Processing research group and secondary affiliation in Language & Music Cognition. He directs the Cognition, Language and Computation lab (CLC-lab) within the Faculty of Science. His research bridges artificial intelligence, cognitive science and linguistics with focus on interpretable deep learning models for text and sound. He pioneered techniques including diagnostic classification (probing), attention rollout, TreeLSTM, and masked language modeling. His work addresses fundamental questions about hierarchical compositionality in language and how neural networks can represent linguistic structure. Analysis of his publication record reveals consistent innovation in neural network interpretability methods, with recent work focusing on bias detection in language models and attention flow quantification. His research trajectory shows evolution from foundational neural parsing work to current emphasis on explainable AI and cognitive plausibility of deep learning models. Dr. Zuidema actively supervises PhD and MSc students while teaching courses including Evolution of Language and Music, Foundations of Neural and Cognitive Modelling, and Interpretability & Explainability in AI. He coordinates the Cognitive Science track in the Brain & Cognitive Sciences master's program. He directs the CLC-lab which pursues both hypothesis-driven methods (diagnostic classifiers, representational similarity analysis) and data-driven methods (layer-wise relevance propagation, contextual decomposition) for neural network interpretation. The lab's InDeep project focuses on interpreting deep learning models for text and sound processing.

Matthijs van LeeuwenView profile

Associate Professor

• Exploratory data mining
• Machine learning
• Data science
• +4 more

Matthijs van Leeuwen is an Associate Professor and Director of Education at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. He leads the Explanatory Data Analysis group and is affiliated with the university-wide SAILS AI research program. Academic rank: Associate Professor Institution: Leiden University Research group: Explanatory Data Analysis University affiliation: SAILS AI research program His research focuses on exploratory data mining with emphasis on explainability for domain experts. Key areas include pattern discovery, anomaly detection, and human-in-the-loop systems. He applies information-theoretic concepts like Minimum Description Length (MDL) and develops interactive methods for real-world applications in life sciences, social sciences, manufacturing, and healthcare. Recent publications demonstrate his work in explainable AI through diverse subgroup discovery, graph anomaly detection, and medical data analysis. Notable contributions include synthetic health record generation, wearable sensor data interpretation, and hemoglobin deferral prediction models. Scientific Awards : Senior Teaching Qualification (SKO) certificate (2022) As Director of Education for LIACS' Master's programs, he plays a leadership role in academic training while maintaining active research partnerships across multiple domains including manufacturing, aviation, and medical informatics.

Johannes HölzlView profile

Researcher

• Formal Verification
• Probabilistic Programming
• Markov Chains
• +7 more

Johannes Hölzl is a Post-doctoral Researcher in the Faculty of Science , specifically within the Department of Computer Science at Vrije Universiteit Amsterdam . He works in the Section of Theoretical Computer Science , focusing on formal verification and probabilistic systems. His research integrates higher-order logic with programming languages to model and verify complex computational processes. Position: Post-doctoral Researcher Institution: Vrije Universiteit Amsterdam Department: Computer Science Section: Theoretical Computer Science Hölzl's research spans formal verification, probabilistic programming, and concurrency. He has contributed extensively to the Isabelle/HOL proof assistant, formalizing mathematical structures like measure theory, Markov chains, and timed automata. His work bridges theoretical analysis with practical verification tools, ensuring rigorous proofs in automated reasoning. His publications highlight advancements in probabilistic systems, including formalizing Markov decision processes (2017), verifying compilers for probability density functions (2015), and analyzing expected running times of probabilistic programs (2016). The 2025 article on probabilistic timed automata indicates ongoing exploration of concurrent probabilistic models. Hölzl has no explicitly listed scientific awards in the provided texts. His collaborations with researchers like Tobias Nipkow and Andrei Popescu underscore interdisciplinary efforts in formal methods and security verification. He contributes to the Theoretical Computer Science Section at VU Amsterdam, advancing foundational research in logic-based programming and probabilistic modeling.

• Operations Research
• Discrete Mathematics
• Tropical Geometry
• +3 more

Georg Loho is an Assistant Professor in the department of Mathematics of Operations Research. His research focuses on the intersection of combinatorics, optimization, and geometry, with a particular emphasis on oriented matroids, tropical geometry, and algorithmic game theory. He explores applications in machine learning, neural network architecture, and stochastic games. Key research themes include the study of matroid theory, convex analysis, and the development of algorithms for optimization problems. His work bridges theoretical mathematics with practical applications in computational fields, such as analyzing the complexity of neural networks and solving problems in mean payoff games. Recent contributions include advancements in tropical geometry applied to neural networks and the reduction of stochastic games to semidefinite programming. He actively collaborates on interdisciplinary projects, as evidenced by his talks and publications in top-tier journals such as Selecta Mathematica and TheoretiCS . His research outputs span topics like polymatroids, lattice polytopes, and combinatorial optimization, reflecting a commitment to both foundational theory and computational methods.

Thijs W. van de LaarView profile

Assistant Professor

• Probabilistic Programming
• Active Inference
• Bayesian Signal Processing
• +4 more

Thijs W. van de Laar is an Assistant Professor at the Eindhoven University of Technology in the Department of Electrical Engineering , affiliated with the Bayesian Intelligent Autonomous Systems lab (BIASlab) and the EAISI Foundational and High Tech Systems groups. His research focuses on intelligent systems for decision-making under dynamic conditions, particularly through scalable probabilistic programming implementations of Active Inference. Academic Background : PhD (2019, TU/e) in automated Bayesian signal processing algorithm design; MSc in natural sciences (biophysics, science communication, Radboud University). Van de Laar's research integrates probabilistic programming with active inference to develop autonomous agents capable of real-time adaptation. Key methodologies include variational message passing , factor graph representations , and free energy minimization . His recent publications examine active inference formalisms, factor graph applications in AI, and uncertainty reduction strategies. Collaborative work spans hearing aid technology, autonomous navigation, and synthetic intelligence development.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +3 more

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.