جست‌وجوی استاد و پژوهشگر

• Signal Processing
• Sensor Engineering
• Graph Neural Networks
• +۴ مورد دیگر

G.J.T. Leus is a Professor at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science, where he leads research in the Signal Processing Systems department. His work spans foundational algorithms and applied systems across wireless communications, medical imaging, and network analysis. With over 498 research outputs including journal articles, conference papers, and patents, he maintains an active research program in cutting-edge signal processing methodologies. Research interests focus on: Signal Processing Systems : Advanced algorithms for spectral estimation, array design, and sensor optimization Computational Imaging : Ultrasound techniques for medical diagnostics including carotid artery visualization Graph-Based Methods : Graph neural networks for localization and topology identification Wireless Technologies : OTFS modulation, MIMO systems, and phased array design Recent publications (2023-2025) demonstrate strong emphasis on: Integration of deep learning with tensor decompositions for channel estimation Fundamental limits in estimation theory using Cramér-Rao bound frameworks Topological signal processing extensions to simplicial complexes Computational imaging innovations for medical ultrasound Academic leadership includes editorial roles for the Eurasip Journal on Advances in Signal Processing , keynote presentations at major conferences (e.g., Asilomar 2023), and supervision of graduate researchers. Current projects involve multi-sensor systems for network localization, ultrasound imaging with limited transceivers, and robust graph neural architectures.

• Cognitive Psychology
• Clinical Psychology
• Decision-Making
• +۵ مورد دیگر

Dr. Leslie van der Leer serves as an Assistant Professor in the Social, Health and Organisational Psychology department within Utrecht University's Faculty of Social and Behavioural Sciences. Her academic career spans cognitive neuroscience, psychology, and methods/statistics, with a PhD obtained in 2014. Her research interests focus on cognitive biases, self-perception, interpersonal relationships, and motivation, with specific expertise in delusion research, political belief revision, and virtual reality stress response. She examines how individuals form beliefs through selective sampling and how desirability biases influence political cognition. Her work bridges experimental economics and psychology, particularly in studying 'jumping to conclusions' tendencies among delusion-prone individuals. Analysis of her publication record reveals two primary research trajectories: cognitive mechanisms of belief formation (particularly regarding delusions and political beliefs) and psychophysiological responses to stress using virtual reality. Her work demonstrates methodological diversity, combining experimental economics approaches with neuroscience techniques including EEG and cortisol measurement. As an educator, she contributes to curriculum design, teaching, and assessment across multiple programs including Psychology (Bachelor), Social, Health and Organisational Psychology (Master), and University College Utrecht. She teaches courses such as Academic Professional, Professional Conversation, Social Psychology, The Psychology of Human Motivation, and Questionnaire Construction Training. Her secondary activities include serving as a Member and internal trainer for Junior Chamber International (JCI). Her research contributes to UN Sustainable Development Goals through work on mental health, education quality, and reduced inequalities.

Tom Heskesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Tom Heskes is a Full Professor of Artificial Intelligence at the Institute for Computing and Information Sciences (iCIS) within Radboud University's Faculty of Science, where he also serves as Vice Dean. His research group, part of the Nijmegen ELLIS unit, focuses on foundational machine learning and cross-disciplinary applications in healthcare, energy, and physical sciences. His educational background includes a PhD from Radboud University (1993) and postdoctoral work at the University of Illinois' Beckman Institute. PhD, Radboud University Nijmegen (1989-1993) Postdoc, Beckman Institute, University of Illinois (1993-1994) Heskes specializes in probabilistic machine learning, with core interests in causal discovery, uncertainty quantification, and deep learning interpretability. His group develops statistical frameworks for learning from complex data, emphasizing robustness in biological, medical, and industrial contexts. Recent work integrates graph neural networks with power systems engineering to enhance grid reliability. His publication trends reveal a dual focus: advancing unsupervised learning methodologies (e.g., anomaly detection robustness) while deploying AI in critical infrastructure like energy grids and healthcare monitoring. This reflects his commitment to both theoretical rigor and societal impact. Notable scientific recognition includes: Vici grant from NWO (2006) TOP grant from NWO (2014) As an advisor, Heskes mentors PhD candidates including Luc Evers (Parkinson's disease monitoring). His extensive grant portfolio spans NWO programs (NWA OBSerVeD, CORTEX), EU initiatives (MATRICS), and industry collaborations like Alliander's AI for Energy Grids Lab. He also initiated Radboud AI and co-founded Machine2Learn (acquired by AMD in 2024). Heskes co-directs the AI for Parkinson lab and contributes to iCIS's leadership as former Research Director (2009-2014). His group actively participates in ELLIS networks and interdisciplinary institutes like Radboudumc, driving AI innovation across scientific domains.

Matthijs Spaanمشاهده پروفایل

استاد

• Safe and Robust Reinforcement Learning
• Planning Under Uncertainty
• Sequential Decision Making
• +۵ مورد دیگر

Matthijs Spaan is a Professor of Reliable AI Algorithms at Delft University of Technology , where he co-directs the Sequential Decision Making group in the Department of Intelligent Systems (EEMCS faculty). Previously, he served as an Associate Professor in the Algorithmics group at TU Delft and as a Senior Research Scientist at the Institute for Systems and Robotics, Instituto Superior Técnico (Lisbon). He holds a PhD in Computer Science (2006) and an MSc in Artificial Intelligence (2002) from the University of Amsterdam. His research focuses on safe and robust reinforcement learning , planning under uncertainty using POMDPs/Dec-POMDPs, and sequential decision-making algorithms . He has contributed to embedding epistemic uncertainty in deep reinforcement learning, safe policy improvement, and constrained decision-making frameworks for smart energy systems, robotics, and traffic optimization. His work includes 15+ key publications in top venues like AAAI, IJCAI, ICAPS, and AAMAS, spanning 2024 to 2011. He has advised four PhD students to completion and co-organized major workshops such as the Lorentz workshop on Rigorous Automated Planning (2022) and AAAI 2022 program. His professional service includes editorial roles at AI Magazine and Journal of Artificial Intelligence Research , area chair positions, and leadership in IJCAI, ICAPS, and IFAAMAS.

• Neuroscience
• Cerebellar Cortex
• Neural Network
• +۵ مورد دیگر

Devika Narain is an Associate Professor in the Department of Neurosciences at Erasmus MC. Her research program focuses on neural computation mechanisms, with particular emphasis on cerebellar function, Bayesian inference, and temporal processing in neural networks. Her primary research interests include: Cerebellar and cerebral cortex dynamics Neural implementations of Bayesian computation Temporal processing and learning mechanisms Neural network modeling Cognitive neuroscience and behavioral analysis Recent publications demonstrate a consistent research trajectory in computational neuroscience, with works exploring cerebellar encoding of temporal stimuli (2023), open resources for neuroscience tasks (2022), neural implementations of Bayesian inference (2021), cortical latent dynamics (2019), and cerebellar learning mechanisms (2018). Her articles frequently appear in high-impact journals including Nature Communications and Neuron.

Xavier G.L.V. Pouwelsمشاهده پروفایل

استادیار

• Cost-Effectiveness Analysis
• Health Care Economics
• Medical Decision Modeling
• +۶ مورد دیگر

Xavier G.L.V. Pouwels serves as Assistant Professor at the Health Technology & Services Research department within the TechMed Centre at the University of Twente. His academic work bridges health economics, medical technology assessment, and open science initiatives, with particular focus on cardiovascular disease modeling and cost-effectiveness analysis frameworks. His research spans Cost-Effectiveness Analysis , Health Care Economics , and Medical Decision Modeling , with significant contributions to cardiovascular interventions, albuminuria screening programs, and open-source health economic modeling tools. His work demonstrates strong integration of probabilistic analysis methods with clinical applications, particularly in cardiology and stroke prevention. Dr. Pouwels has developed influential open-science resources including the Probabilistic Analysis Check dashBOARD (PACBOARD) and educational materials for R programming in Health Technology Assessment. His publication trends show increasing focus on model validation frameworks, open-source economic modeling, and practical implementation of health technology assessment tools in clinical decision-making. h-index 10 (Scopus) 266 Citations (Scopus) His collaborative work includes significant contributions to ISPOR Special Interest Group reports and multi-institutional studies on disease modeling frameworks. Current activities focus on advancing open science practices within health care technology education and developing reusable disease modeling blueprints through expert consensus building. Dr. Pouwels leads the PACBOARD initiative for systematic validation of health economic models and contributes to open science education through Zenodo-hosted resources on R Markdown and open science principles. His work emphasizes transparency, reproducibility, and practical implementation of economic modeling in healthcare decision-making.

Onno Kleenمشاهده پروفایل

استادیار

• Time Series Analysis
• Financial Econometrics
• Macro-Finance Forecasting
• +۲ مورد دیگر

Onno Kleen is an Assistant Professor of Econometrics at Erasmus School of Economics, specializing in time series analysis applied to financial economics and macro-finance. His research focuses on distribution forecasting, volatility modeling, and high-dimensional data analysis using machine learning techniques. His publications demonstrate consistent focus on developing practical econometric tools, including open-source R packages for financial data analysis ( highfrequency ) and mixed-frequency GARCH modeling ( mfGARCH ). Article analysis shows strong emphasis on volatility forecasting techniques and distributional scoring methods. Software Development: Creator of R packages alfred (FRED/ALFRED data access) and mfGARCH (mixed-frequency models) Education: PhD in Economics (Heidelberg University, 2020)

Kailai Liمشاهده پروفایل

استادیار

• Robotic Perception
• Sensor Fusion
• State Estimation
• +۲ مورد دیگر

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +۳ مورد دیگر

Mauro R.U. Salazar Villalon is an Assistant Professor at Eindhoven University of Technology (TU/e) in the Department of Mechanical Engineering and co-affiliated with the Eindhoven AI Systems Institute (EAISI). He obtained his B.Sc. and M.Sc. from ETH Zurich, followed by a Ph.D. from ETH Zurich in collaboration with Ferrari Formula 1. After postdoctoral work at Stanford's Autonomous Systems Lab, he joined TU/e in 2020. Educational Background: B.Sc. & M.Sc. in Mechanical Engineering, ETH Zurich Ph.D. in Mechanical Engineering, ETH Zurich (with Ferrari F1) His research focuses on optimization models for cyber-socio-technical systems, addressing challenges in mobility, pandemics, and material design. He develops methods for sustainable transportation systems, including incentive schemes for user behavior alignment. Recent trends in his publications span hydrogen-based hybrid aero engines, ride-pooling network flows, battery-electric truck infrastructure, and electric vehicle powertrain optimization. His work integrates advanced mathematical modeling with real-world applications. Scientific Awards: ETH Medal (Master & PhD theses) Best Student Paper Awards (2018, 2022) Best Teacher Award (2021) Nominated for TU/e Young Researcher Award (2022) IEEE VPPC 2024 Best Paper Award He contributes to projects like Model Predictive Pandemic Control and National Growth Fund initiatives, while teaching courses on powertrain design, engineering optimization, and optimal control. Collaborations extend to Stanford University and the Ferrari Formula 1 team.

Mircea Lazarمشاهده پروفایل

دانشیار

• Constrained Control
• Model Predictive Control
• Hybrid Systems
• +۴ مورد دیگر

Mircea Lazar is an Associate Professor in the Control Systems group at Eindhoven University of Technology. His research spans constrained control, model predictive control (MPC), and stability analysis of hybrid systems, with applications in power systems, smart grids, and precision mechatronics. Research highlights include: Development of data-driven predictive control methods Stability guarantees for nonlinear and hybrid systems Applications in water networks, power converters, and mechatronics He has received the EECI PhD Award and NWO VENI grant. Recent publications focus on real-time MPC implementations, physics-guided neural networks, and optimization for large-scale systems. Collaborations include ASML, DAF, Philips, and Ford. He chairs the IEEE CSS Technical Committee on Hybrid Systems and has supervised 9 PhD students.

• Artificial Intelligence
• Machine Learning
• Probabilistic Circuits
• +۳ مورد دیگر

Devendra Singh Dhami is an Assistant Professor in Mathematics and Computer Science at Eindhoven University of Technology, specializing in uncertainty in artificial intelligence. His research develops advanced methods in probabilistic circuits, neural-symbolic learning, and causal inference for applications in computer vision and bioinformatics. Key research areas include machine learning scalability, graph-based biological network analysis, and satellite image processing for infrastructure risk detection. His work frequently integrates deep learning with symbolic reasoning. Recent publications focus on neuro-symbolic systems, temporal causality, and efficient algorithms for large-scale data. Trends show strong emphasis on interpretable AI and cross-disciplinary applications. No awards, students, or lab details are documented.

Dmitry Bagaevمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Probabilistic Programming
• Reactive Message Passing
• +۲ مورد دیگر

Dmitry Bagaev is a Researcher at Eindhoven University of Technology's Signal Processing Systems department, specializing in Bayesian inference and probabilistic programming. His work develops computational tools like RxInfer.jl and ReactiveMP.jl for scalable probabilistic modeling. Research spans Bayesian inference frameworks, reactive programming architectures, and applications in computational immunology. Recent publications focus on probabilistic programming languages (GraphPPL.jl), multi-agent trajectory planning, and SARS-CoV-2 T-cell receptor databases. Articles demonstrate consistent innovation at the intersection of computational statistics, biological data analysis, and real-time systems engineering. Awards and advising roles are not documented in available records. Research occurs within the BIASLab group, focusing on signal processing systems and probabilistic AI.

Dinand Alkemaمشاهده پروفایل

استادیار

• Flood Risk Assessment
• Disaster Risk Management
• Hydrodynamic Modeling
• +۴ مورد دیگر

Dinand Alkema is an Assistant Professor at the University of Twente's Faculty of Geo-Information Science and Earth Observation, part of the Natural Hazards and Disaster Risk Management research group. His work focuses on flood risk assessment in Southeast Asia, integrating hydrodynamic models and remote sensing. He holds an MSc (1995) and PhD (2007) in Physical Geography from Utrecht University. His research emphasizes probabilistic risk methodologies and multi-hazard approaches in data-scarce regions. Education: MSc in Physical Geography, Utrecht University (1995) PhD in Physical Geography, Utrecht University (2007) Research Interests: Flood modeling, disaster risk management in developing countries, and applications of GIS/remote sensing for hazard assessment. His recent work includes Caribbean multi-hazard dashboards and geothermal mapping in East Africa. Projects: Coordinator of the CASITA University Network in Southeast Asia and leader of the GITHRA project in Vietnam. Teaches disaster management courses in Thailand, Sri Lanka, and India. Grants and Advising: Supervised PhD student Nguyen Thi Hai Van and contributes to training programs with organizations like the Asian Disaster Preparedness Center.

• Algorithms
• Optimization
• Combinatorial Optimization
• +۳ مورد دیگر

Bodo Manthey is an Associate Professor in the Department of Mathematics of Operations Research and the Digital Society Institute. His research focuses on algorithms, combinatorial optimization, and computational complexity. He has contributed to areas such as the Traveling Salesman Problem (TSP), clustering algorithms, and smoothed analysis of heuristics. His work bridges theoretical computer science and operations research, with a strong emphasis on algorithm design, approximation algorithms, and probabilistic analysis. Key research interests include the analysis of local search algorithms, approximation inefficiencies in optimization heuristics, and probabilistic models for algorithm performance. He has published extensively on topics like k-means clustering, TSP variants, and the application of smoothed analysis to understand algorithm behavior under realistic conditions. Manthey has organized multiple conferences and workshops, including the Cologne-Twente Workshop on Graphs and Combinatorial Optimization. His contributions to the field are reflected in over 100 research outputs, with recent work addressing the complexity of Euclidean clustering and the effectiveness of ant colony algorithms. His academic activities span editorial roles, committee memberships, and invited talks on topics such as random metrics in algorithm analysis. Despite the absence of explicitly listed awards, his prolific output and conference involvement highlight his significant contributions to theoretical computer science and operations research.

• Formal Methods
• Model-Based Testing
• Process Algebra
• +۴ مورد دیگر

Ed Brinksma is a Researcher in the Formal Methods and Tools department at the University of Twente. His work focuses on formal methods, model-based testing, process algebra, and real-time systems verification. He has contributed to foundational research in temporal logic, concurrent systems, and formal verification techniques, with over 148 publications. Brinksma has been actively involved in international conferences and workshops, delivering invited talks on topics like model-based testing and verification methodologies. His research collaborations span theoretical advancements and practical applications in embedded systems, safety-critical systems, and industrial software validation. Key research areas include: formal semantics of concurrent systems, automated test generation frameworks (e.g., Torx), and integration of formal methods with real-time systems. He has edited books and special issues on topics like process algebra and stochastic systems modeling. Brinksma's work bridges theoretical computer science with practical engineering challenges, emphasizing rigorous validation approaches for complex systems. He has supervised 18 academic works and participated in 38 professional activities including keynote lectures at major venues. His research network includes collaborations with institutions globally, particularly in Europe, focusing on probabilistic systems modeling and verification challenges.