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Peter VangorpView profile

Assistant Professor

• Computer Graphics
• Material Perception
• Realistic Rendering
• +4 more

Peter Vangorp is an Assistant Professor in the Visualization and Graphics group of the Department of Information and Computing Sciences at Utrecht University in the Netherlands. He leads research in computer graphics, visual perception, and virtual reality, with a particular focus on material perception and realistic rendering techniques. Dr. Vangorp obtained his Ph.D. in Computer Science at the University of Leuven (Belgium) in 2009. His doctoral research focused on "Human Visual Perception of Materials in Realistic Computer Graphics." Prior to his current position, he held postdoctoral positions at REVES/Inria Sophia-Antipolis (France), Giessen University (Germany), Max Planck Institute for Informatics (Germany), and Bangor University (UK). He also served as a Senior Lecturer at Edge Hill University (UK) from 2016 to 2022. Dr. Vangorp's research interests span several interconnected domains within computer graphics and visual perception. His primary focus is on understanding how humans perceive materials and gloss in computer-generated imagery, which has direct applications in realistic rendering. He has made significant contributions to the study of hazy gloss perception, BRDF modeling, and material editing techniques. His work bridges the gap between computer graphics and human visual perception, using rigorous experimental methods to inform rendering techniques. More recently, his research has expanded into virtual reality applications, particularly in medical visualization and rehabilitation. Analysis of Dr. Vangorp's recent publications reveals a consistent focus on material perception and realistic rendering, with increasing attention to virtual reality applications. His work often combines computer graphics techniques with psychophysical experiments to understand human visual perception. Recent publications show expansion into medical applications of VR, 3D point cloud processing, and gamification in educational contexts. The interdisciplinary nature of his research is evident in collaborations with researchers from computer science, psychology, medicine, and education fields. Dr. Vangorp has received research funding through an NWO grant for the VR4eVR project (Virtual Reality for enhanced Visual Rehabilitation), which runs from 2024 to 2030. This project involves collaboration with multiple institutions including UMCG, Royal Visio, RUG, and UT. Dr. Vangorp has supervised numerous graduate students, including PhD candidate Vanderfeesten (2025) and multiple Master's students in Game & Media Technology and Artificial Intelligence programs. His students have worked on diverse topics including real-time rendering techniques, neural denoising, 3D Gaussian splatting, and volumetric sampling methods. He serves as a PhD supervisor and researcher in the VR4eVR project, mentoring students working at the intersection of computer graphics and medical applications. Dr. Vangorp is actively involved in the Visualization and Graphics research group at Utrecht University, where he contributes to research on advanced rendering techniques, material perception, and virtual reality applications. His work on the VR4eVR project demonstrates his commitment to applying computer graphics research to real-world medical challenges, particularly in visual rehabilitation.

• Artificial Intelligence
• Neurotechnology
• Perception
• +5 more

Dr. Yağmur Güçlütürk is an Assistant Professor and Principal Investigator at the Donders Centre for Cognition and the Donders Institute for Brain, Cognition and Behaviour . She leads the Neural Engineering Lab , focusing on interdisciplinary research at the intersection of artificial intelligence, neurotechnology, and perception. Her work emphasizes computational approaches to interface with human sensory experiences and develop neuroprosthetics like visual prostheses and cochlear implants. Research interests include: Neurotechnology : advancing neural decoding techniques and implants Perception : exploring visual, auditory, and crossmodal aesthetic experiences Artificial Intelligence : applying machine learning and computer vision to sensory restoration Her recent publications focus on neural decoding, computer vision, and neuroprosthetics, with applications in emotion recognition, stimulus complexity modeling, and deep adversarial learning for brain-computer interfaces. She has secured significant research grants, including the H2020 FET Open and NWO Crossover Grant , and holds an Adjunct Professor position at Yeditepe University since 2019.

Asia van de Mortel-FronczakView profile

Assistant Professor

• Model-Based Engineering
• Supervisory Control Systems
• Discrete-Event Systems
• +4 more

Asia van de Mortel-Fronczak is an Assistant Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e) , where she has been since 1997. Her work focuses on model-based engineering and synthesis of supervisory control systems , with applications in cyber-physical systems like wafer scanners, high-volume printers, vehicles with ADAS, and dynamic traffic infrastructure (e.g., water locks, moveable bridges, tunnels). Academic Background : MSc (1982) and PhD (1993) in Computer Science from AGH University of Science and Technology and TU/e, respectively. Research : Formal models for supervisory control, synthesis-based engineering (SBE), and verification techniques using tools like mCRL2. Her work bridges discrete-event control theory with real-time industrial implementations. Recent Trends : Recent articles highlight her focus on distributed supervisory controllers , communication delay robustness , and dynamic traffic management using formal methods like ACP and ILP. Teaching : Offers courses on supervisory control, model-based systems engineering, and cyber-physical systems. Collaborations : Works with Rijkswaterstaat and ASML, applying her research to real-world systems like ship locks and road tunnels.

Kees VuikView profile

Professor

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +4 more

Kees Vuik is a Professor at the Delft Institute of Applied Mathematics within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on developing advanced numerical methods for complex engineering and scientific simulations across multiple domains including energy systems, environmental engineering, and computational physics. His research interests center on Numerical Analysis and Scientific Computing, with specialized expertise in Computational Fluid Dynamics, Reservoir Simulation, and Wave Propagation. He develops innovative preconditioning techniques for large-scale linear systems and creates high-fidelity models for subsurface flow, flood prediction, and energy conversion systems. Recent work integrates graph-based frameworks for electrolyzer modeling and advanced triangulation methods for point cloud processing. Analysis of his 2025 publications reveals a strong interdisciplinary focus on sustainable energy solutions (hydrogen/CO2 storage), environmental resilience (flood modeling), and computational efficiency (parallel preconditioning). His work consistently bridges theoretical numerical methods with practical engineering applications, particularly in energy transition technologies and climate-related simulations. Professor Vuik's scientific contributions have earned significant recognition: Officer of the Order of Orange-Nassau (2021) Professor of Excellence award (Leermeesterprijs) from TU Delft (2022) He has supervised 49 students across bachelor, master, and PhD levels, with notable successes including undergraduate research published in leading medical journals. His collaborative network spans 21 documented activities including industrial partnerships with Fujitsu Laboratories and leadership in academic societies, indicating substantial research funding and mentorship impact. As chair of the Bataafsch Genootschap der Proefondervindelijke Wijsbegeerte (2023-2024) and active member of the Delft Institute of Applied Mathematics, he contributes to both foundational mathematical research and real-world applications through international collaborations and industrial knowledge exchange.

Elmar EisemannView profile

Professor

• Real-time rendering
• Perceptual rendering
• Alternative representations
• +3 more

Elmar Eisemann is Professor and head of the Computer Graphics and Visualization group at TU Delft. His research spans real-time rendering, perceptual graphics, visualization, and GPU acceleration techniques. Recent work includes spectral uplifting methods for controllable material appearance, efficient hyperbolic embedding algorithms, light field display optimization, and 3D animation using diffusion models. Eisemann develops novel representations and acceleration structures for rendering and visualization. He received the Netherlands Prize for ICT Research and Eurographics Young Researcher Award, and has chaired major conferences including Eurographics 2018.

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +3 more

Yue Sun serves as a University Researcher in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), affiliated with both the Center for Care & Cure Technology Eindhoven and the Eindhoven MedTech Innovation Center (EMTE). Holding an MSc degree, Sun specializes in computer vision and AI applications spanning medical imaging and agricultural technology. Research interests focus on video-based monitoring systems for clinical and agricultural environments, including premature infant discomfort detection and smart livestock management. Key methodologies involve deep learning, multi-object tracking, and sensor fusion techniques applied to real-world healthcare and farming challenges. Recent work demonstrates strong translational impact through partnerships with clinical and agricultural stakeholders. Publications reveal evolving expertise from foundational work in convex optimization and GPU computing (2011-2015) toward applied computer vision solutions in medical diagnostics and precision farming (2017-2025). Current research emphasizes practical implementations like pig pose estimation systems and breast MRI segmentation tools with direct clinical applications. Professional activities include multiple conference presentations on neonatal monitoring systems and press coverage of agricultural technology implementations. The researcher actively contributes to TU/e's Sustainable Development Goals initiatives, particularly in healthcare technology advancement. Supervision activities and grant details are not explicitly documented in available materials, though collaborative project involvement is evident through multi-institutional publications and the completed EU-funded project 'Advancing Smart Optical Imaging and Sensing for Health' (2016-2019).

Nalan BastürkView profile

Associate Professor

• Bayesian Inference
• Time Series Analysis
• Mixture Models
• +3 more

Nalan Bastürk is an Associate Professor at Maastricht University within the School of Business and Economics , Department of QE Econometrics. Prior to this, she held roles as Assistant Professor at Maastricht University (2014-2019) and Erasmus University Rotterdam (2010-2014). Her research focuses on Bayesian inference, time series analysis, and mixture models, with applications in econometrics, healthcare, and financial modeling. Her key research project, funded by NWO, explores patient heterogeneity and time-variation in the course of depression . She contributes to methodological advancements in statistical modeling, including neural networks for density estimation and Bayesian causal analysis. Recent publications span diverse topics such as text analysis of tax news, portfolio optimization with genetic algorithms, and clinical phenotyping in schizophrenia. Her work bridges theoretical statistics and real-world applications, emphasizing interdisciplinary collaboration. Notably, she co-developed the R package BayesMultiMode for Bayesian mode inference. While no scientific awards are explicitly listed, her contributions to econometrics and computational methods reflect significant scholarly impact. Her advising and grant activities include the NWO-funded Vividi195187 project. No specific lab or team affiliations are mentioned, but her research often involves collaborations across disciplines within the School of Business and Economics.

Carel F.W. PeetersView profile

Associate Professor

• Statistics
• Bayesian theory
• Biostatistics
• +6 more

Carel F.W. Peeters is an Associate Professor of Statistics at the Department of Mathematical & Statistical Methods (Biometris) , Wageningen University & Research . He previously held academic positions at Amsterdam University Medical Centers (AUMC) as Assistant Professor in Biostatistics , and during his PhD studies, he was a research fellow at VUA and research scientist at CITO . Education: M.Sc. in Statistics (with great distinction) from Katholieke Universiteit Leuven (KUL) Ph.D. in Bayesian Statistics from Utrecht University Research Focus: His work operates at the intersection of high-dimensional multivariate statistics , statistical machine learning , and complex data analysis . He develops computational methods for small-sample massive-features data to automate discovery of data-generating mechanisms, with applications in genomics , medical imaging , and social network analysis . Recent Trends: His publications demonstrate expertise in genomic prediction models , graphical modeling , high-dimensional regularization , and cross-domain data integration . Methodological innovations include rags2ridges package development and targeted penalization techniques. Technical Expertise: Bayesian theory Multivariate analysis Machine learning High-dimensional data Network analysis Statistical software development (R packages) Data science Graphical models Current Position: He serves as contact person for Biometris subdivision at Wageningen University, with physical location at Droevendaalsesteeg 1, 6708PB Wageningen, Netherlands. His work continues to bridge theoretical and applied statistics through practical implementation of advanced methodologies.

• Econometrics
• Financial Economics
• Machine Learning in Finance
• +3 more

Dr. Maria Grith is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research focuses on econometric methodologies applied to financial markets, with particular emphasis on machine learning techniques in finance, risk management, and time series analysis. She actively contributes to academic supervision, as evidenced by her involvement in bachelor thesis projects in Econometrics and Economics. Her research interests span quantitative finance, including applications of neural networks to volatility forecasting, cryptocurrency risk analysis, and reinforcement learning in high-dimensional settings. She also explores traditional econometric challenges such as delta-hedged options pricing, sovereign default risk, and macroeconomic persistence heterogeneity. Her publication record includes over 20 peer-reviewed articles, with recent work addressing cutting-edge topics like neural tangent kernels in finance and graphical models for multivariate time series. While no scientific awards are explicitly mentioned, her prolific output reflects a strong research trajectory in financial econometrics and computational methods.

• Perceptual Psychology
• Visual Design
• Art History
• +3 more

Maarten Wijntjes is a Researcher at the Delft University of Technology (TU Delft), affiliated with the Faculty of Industrial Design Engineering. His primary department is Industrial Design Engineering, with a focus on Human-Centered Design and Perceptual Intelligence. He teaches courses such as Multi-Sensory Design Studio and Visual Communication Design, integrating theoretical research with practical design education. His research bridges perceptual psychology, art history, and technology, with a strong emphasis on material perception in visual depictions. Key areas include analyzing how humans perceive materials in paintings, the impact of lighting and color on visual interpretation, and the application of perceptual principles in design. He collaborates across disciplines, combining computational methods with traditional art analysis. Recent work explores topics like time-of-day perception in artworks, material diagnosticity in oil paintings vs. engravings, and peripheral visual processing. His research outputs highlight interdisciplinary approaches, often involving crowdsourced experiments and algorithmic tools for art analysis. Though no formal awards are listed, his contributions to perceptual design and digital art history reflect significant academic engagement. Dr. Wijntjes advises on graduate studies and contributes to TU Delft’s research labs, focusing on perceptual intelligence and human-centered design solutions. His work aims to enhance understanding of how visual cues shape human perception, with applications in both digital interfaces and traditional art interpretation.

• Visual Analytics
• Data Visualization
• Computer Graphics
• +2 more

Martijn van Dortmont is a researcher at Eindhoven University of Technology's Faculty of Mathematics and Computer Science, specializing in visual analytics and data visualization with 25 Scopus citations. His work intersects computer science, human-computer interaction, and predictive modeling, contributing to UN Sustainable Development Goals through data-driven solutions. His research profile reveals expertise in Visual Analytics, Predictive Analytics, Human Interaction, and Synthetic Data generation. Key contributions include ChronoCorrelator for temporal event analysis, mobile phone data visualization techniques for development contexts, and GPU-accelerated 3D volume processing. His work consistently bridges theoretical computer science with practical applications in complex data domains. Publications demonstrate progressive specialization from foundational 3D graphics (2006) to advanced visual analytics for mobile data (2015) and temporal correlation systems (2019), reflecting evolving expertise in making complex datasets interpretable through visualization. Scientific recognition includes: Best Visualization Award at D4D Challenge 2015 for mobile data analysis work No advisory roles, grants, or laboratory affiliations are documented in available records. His research trajectory shows sustained focus on visualization techniques for human-centric data analysis without indication of team leadership roles.

Dmitry BagaevView profile

Researcher

• Bayesian Inference
• Probabilistic Programming
• Reactive Message Passing
• +2 more

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.

• Bioinformatics
• Theoretical Computer Science
• Computational Biology
• +3 more

Atze van der Ploeg is an Assistant Professor at the Vrije Universiteit Amsterdam's Faculty of Science, within the Theoretical Computer Science department. He is also affiliated with the Network Institute. His research focuses on interdisciplinary topics at the intersection of computer science and computational biology, with expertise in algorithm design, bioinformatics tools, and programming languages. He holds an MSc and a PhD, though specific academic details about his degrees are not provided in the text. His teaching responsibilities include courses such as Introduction to Programming (PYTHON), Object-Oriented and Functional Programming, and Systems Programming Project. These courses reflect his commitment to foundational programming skills and advanced software development practices. Key research interests include developing tools for bioinformatics (e.g., the PRALINE alignment toolkit), functional reactive programming, and visualization systems. His work emphasizes practical, principled approaches to solving complex computational problems. Though no specific grants or awards are listed, his publications span topics like type-safe dynamic typing, modular language implementation, and algorithm optimization, showcasing contributions to both theoretical and applied computer science.

Patrick GroenenView profile

Professor

• Data Science
• Statistics
• Multidimensional Scaling
• +3 more

Patrick Groenen is a Professor of Statistics and Dean of the Erasmus School of Economics at Erasmus University Rotterdam. His research focuses on data science, numerical algorithms, and multidimensional scaling. He co-authored influential textbooks on these topics and publishes in top journals such as the Journal of Machine Learning Research and Psychometrika . His work spans statistical methodologies, including principal component analysis, clustering, and genetic studies. Key research interests include multidimensional scaling, machine learning applications, and statistical genetics. Notable contributions include advancements in hierarchical clustering, genomic relationship modeling, and robust statistical methods. Recent publications address reliability theory in cognitive tasks and genetic influences on risk tolerance. He received the 2015 ESE Societal Impact Award and contributes to editorial boards, including Statistical Software and Nature Reviews Methods Primers . His leadership roles include organizing conferences on data science and statistical visualization.

Vera van de SeypView profile

Lecturer

• Computational Design
• Generative Systems
• Art and Technology
• +4 more

Vera van de Seyp is a computational designer, artist, and educator affiliated with the Design Academy Eindhoven (DAE). She holds a Lecturer role and actively engages in teaching, workshops, and mentorship. Her work explores generative systems, computational tools, and the intersection of technology with craftsmanship, resulting in artifacts such as generative design tools, tactile knitted graphics, and multimedia installations. She has collaborated with prominent institutions including WIRED Magazine, Serpentine Galleries London, and Google Arts and Culture. Recent academic involvement includes a Research Assistantship with MIT Media Lab's Future Sketches group and organizing the annual ITERATIONS creative coding symposium. Vera emphasizes experimental approaches to design, advocating for embracing mistakes as catalysts for discovery. She is a distinguished member of the Alliance Graphique Internationale (AGI). Her research interests focus on generative systems, computational typography, and the application of code as a primary medium. She frequently lectures on process-driven design methodologies, as evidenced by her upcoming DAE Lecture Series talk on redefining creative practice through error analysis.