جست‌وجوی استاد و پژوهشگر

Weverthon Barbosa Machadoمشاهده پروفایل

پژوهشگر

• Social Stratification
• Family Dynamics
• Union Formation
• +۳ مورد دیگر

Dr. Weverthon Barbosa Machado is a postdoctoral researcher in the Department of Sociology at Utrecht University, Netherlands. His work focuses on social stratification, family dynamics, and gender inequality. He holds a Ph.D. in Sociology from the Institute of Social and Political Studies of Rio de Janeiro State University and was a Max Weber Fellow at the European University Institute before joining Utrecht University. Research Interests: His research explores union formation, marriage markets, and the gendered division of labor. He examines how inequalities manifest within and between families, particularly through the lens of socioeconomic status and labor market dynamics. Recent work includes studies on parental earnings trajectories post-parenthood and the growth of evangelical institutions in Brazil. Teaching: He contributes to courses such as 'Measurement and Modeling with Social Data' and 'Methods and Statistics 1: Regression Analysis' at Utrecht University. His pedagogical innovations include developing the R package ggreveal , which facilitates incremental visualization of data plots for teaching and presentations. Recent Publications: Key contributions include analyses of educational assortative mating in Brazil, income inequality trends, and the role of gender in labor market behavior. His work bridges quantitative methods with sociological theory, addressing contemporary issues in family structures and social stratification.

Bettina Speckmannمشاهده پروفایل

استاد

• Computational Geometry
• Geometric Algorithms
• Spatial Data Analysis
• +۱۲ مورد دیگر

Bettina Speckmann is a full Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU Eindhoven), where she leads the Applied Geometric Algorithms group. She holds additional appointments as EAISI Health Professor and EAISI Foundational Professor, and is affiliated with the Data Science Center Eindhoven. Her research bridges theoretical algorithm design with practical applications in spatial computing. Her research interests lie primarily in computational geometry and geometric algorithms, with strong applications in GIScience, Smart Mobility (including moving object analysis and automated cartography), geo-visualization, visual analytics, and e-Humanities. She focuses on developing efficient algorithms and data structures for spatial data, combining rigorous theoretical methods with practical engineering for real-world impact. Her recent publications (2025) show a strong trend in geometric data processing, particularly in polycube segmentations, dual loop algorithms, density estimation for moving groups, and topological analysis using merge trees and Fréchet distances. These works reflect her interdisciplinary focus on computational geometry, visualization, and data structures. Scientific awards received include: Netherlands Prize for ICT Research (2011) NWO Vici Award (2012) PEriTiA Prize (2020) Bettina Speckmann has advised numerous students and researchers through her group and has secured major grants, including the NWO Vici. She has served in leadership roles such as PC co-chair for Graph Drawing (GD 2011), PC chair for ICALP Track A (2015), and PC co-chair for SoCG (2018). She teaches courses such as Data Structures and Heuristic Algorithms. She leads the Applied Geometric Algorithms group, which actively collaborates with industry partners like HERE Global B.V., Fugro NL Land B.V., and OCLC B.V., and contributes to UN Sustainable Development Goals in areas related to data and mobility.

Thijs W. van de Laarمشاهده پروفایل

استادیار

• Probabilistic Programming
• Active Inference
• Bayesian Signal Processing
• +۴ مورد دیگر

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.

• Bayesian Networks
• Probabilistic Graphical Models
• Sensitivity Analysis
• +۴ مورد دیگر

Silja Renooij is an Associate Professor at Utrecht University's Department of Information and Computing Sciences, specializing in Artificial Intelligence and Intelligent Systems . Her work bridges Bayesian networks , probabilistic graphical models , and human-centered AI , with a focus on uncertainty quantification, sensitivity analysis, and legal reasoning applications. Current affiliation: Utrecht University Department: Information and Computing Sciences Academic rank: Associate Professor Contact: s.renooij@uu.nl Research interests include: Bayesian network construction and sensitivity analysis Probabilistic reasoning in legal and medical domains Interpretable AI through scenario-based modeling Conflict detection in black-box systems Hybrid human-AI reasoning frameworks Probability elicitation and evidence evaluation Recent publications demonstrate her focus on explainable AI through MAP-independence analysis, legal evidence modeling , and robust decision support systems . Her work often combines argumentation theory with probabilistic graphical models , emphasizing human-AI collaboration in critical domains like healthcare and law.

• History of Science
• Philosophy of Science
• Methodology of Economics
• +۳ مورد دیگر

Prof. dr. ir. Marcel Boumans is a Professor of History of Economics at Utrecht School of Economics, Utrecht University, specializing in the intersection of the history, philosophy, and methodology of economics. He has held visiting positions at Duke University, UCLA, Université Paris 1, and the London School of Economics. His academic leadership includes heading the Applied Economics section and coordinating the double bachelor program in Mathematics and Economics. PhD in Economics (1992) and MSc in Mathematics (1988) from University of Amsterdam and Delft University of Technology, respectively. Teaching qualifications include Senior Qualification Education (2016) and Basic Education Qualification (2014) in science education. His research explores empirical practices in economics, focusing on measurement, modeling, and the role of mathematics in shaping economic knowledge. Key works include How Economists Model the World into Number and Science Outside the Laboratory . Current projects include a situational approach to misinformation and a book manuscript titled Shaping the Phenomena . Recent publications analyze statistical inference, visual epistemology, and the computerization of economics. His 15 most recent articles (2025-2019) span topics like data reliability, econometric controversies, and the cultural history of mathematical tools. These works reflect interdisciplinary engagement with philosophy, data science, and policy studies. Scientific honors include the KVS Professor of History of Economics (2024–present) and fellowships at Duke University’s Center for the History of Political Economy, NIAS, and Tinbergen Institute. He has served as co-editor of the Journal of the History of Economic Thought and on editorial boards of leading journals. As a supervisor, he co-promoted dissertations on macroeconomic modeling, identity-based inequality, and policy applications of economic theory. He actively participates in academic governance, including roles in the History of Economics Society and advisory boards for Data Science and the Society for Philosophy of Science in Practice.

Lynda Hardmanمشاهده پروفایل

استاد

• Applied Data Science
• Human-centered Artificial Intelligence
• Computer Graphics
• +۴ مورد دیگر

Prof. Lynda Hardman is a Professor in the Department of Visualization and Graphics at Utrecht University's Faculty of Science. Her primary affiliation is with Utrecht University, though she maintains strong ties to the Centrum Wiskunde & Informatica (CWI) in Amsterdam, where her research webpage is hosted. She focuses on interdisciplinary areas such as Human-centered Artificial Intelligence, Applied Data Science, and Augmented Reality applications in scientific research. Her work bridges computer science with fields like neuroscience and ecology, emphasizing tools for literature exploration, data visualization, and user-centric design. Research Themes: Applied Data Science Human-centered AI Augmented Reality for scientific exploration Metadata and digital library systems Her recent publications emphasize AR tools like DatAR for neuroscientists and methods to improve policy workers' search efficiency. She collaborates widely, with projects addressing topics from fish population monitoring to cultural heritage digitalization. She contributes to initiatives like the Digital Humanism Initiative, advocating ethical AI integration.

Eric Verbeekمشاهده پروفایل

پژوهشگر

• Process Mining
• Process Models
• Process Discovery
• +۳ مورد دیگر

H.M.W. (Eric) Verbeek is an ICT Developer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, specializing in Process Analytics. His work focuses on advancing process mining techniques, standardization of event data (e.g., IEEE XES), and the development of tools like ProM and CPN IDE. He has contributed to over 160 publications since 1986, with recent research emphasizing process discovery algorithms, workflow net synthesis, and context-rich process modeling. Research Interests include Process Mining, Process Models, Process Discovery, Petri Net Analysis, and Workflow Management. He has actively participated in projects like the GDPR compliance toolkit and has been involved in defining standards for event data interoperability. Notable contributions include the IEEE XES standard for process mining, tools for event log generation and analysis, and methods for decomposed conformance checking. His work bridges theoretical advancements in process modeling with practical tool development, supporting both academia and industry applications. Collaborations span international academia, focusing on process mining workflows and compliance monitoring. His research trends highlight a balance between algorithmic innovation and real-world applicability in areas like healthcare, logistics, and service management.

• Bioinformatics
• Antimicrobial Resistance
• Antibiotic Resistance
• +۳ مورد دیگر

Saskia Hiltemann is a Researcher at the Department of Pathology at Erasmus University Medical Center. Her work focuses on advancing bioinformatics methodologies and their application in antimicrobial resistance (AMR) research. She actively contributes to the Galaxy platform ecosystem, promoting reproducible and FAIR data analysis practices. Her research interests include microbial genomics, single-cell RNA sequencing workflows, and developing educational resources for bioinformatics training. She has co-created platforms like BenchAMRking and microGalaxy to address challenges in AMR gene prediction and microbial data analysis. Her recent publications emphasize enhancing reproducibility through standardized workflows, integrating sensitive clinical data securely, and improving access to bioinformatics tools for global research communities. Collaborations span institutions worldwide to address pressing issues in pathogenomics and precision medicine. Her datasets, such as the Galaxy Training Network tutorial resources, reflect her commitment to open science and educational accessibility. While no formal awards are listed, her contributions to open-source platforms demonstrate significant impact in computational biology.

Mehmet Ozdemirمشاهده پروفایل

مدرس

• Digital Fabrication
• Computational Design
• Mass Personalization
• +۳ مورد دیگر

Mehmet Ozdemir is a Lecturer and researcher at the Faculty of Industrial Design Engineering at Delft University of Technology. His work focuses on digital fabrication, computational design, and sustainable design engineering. He holds dual PhDs in Mechanical Engineering (Politecnico di Milano) and Product Development (University of Antwerp), focusing on mass personalization methodologies in digital manufacturing. Expertise: Multi-material 3D printing, computational design tools (e.g., Grasshopper), and design for additive manufacturing Research: Explores novel structures/materials for FDM 3D printing, programmable foaming (4D printing), and texture/gradient control in printing His research emphasizes transforming manufacturing processes into design tools, pushing boundaries in multi-material printing applications. Ozdemir has been recognized with a Best Demo Honorable Mention (ACM UIST 2024) for his work on augmented reality component selection tools.

• Biostatistics
• Agricultural Science
• Metabolomics
• +۵ مورد دیگر

Carel Peeters is an Associate Professor in the Mathematical and Statistical Methods (Biometris) department at Wageningen University & Research. His research focuses on statistical methodologies applied to agricultural, biomedical, and genomic data. Key areas include Bayesian modeling of methane emissions in livestock, metabolomic and proteomic profiling in neurodegenerative diseases, and multi-omic data integration using machine learning techniques. He collaborates on projects addressing dairy cow methane production monitoring, preterm infant microbiota dynamics, and genomic prediction in plant breeding. Peeters has co-supervised numerous PhD projects, including studies on adaptive management in agriculture, latent factor representations in plant phenotypes, and causal learning for kwashiorkor etiology. His work integrates statistical innovation with real-world applications, such as developing the cubicle hood sampler for methane measurement and identifying CSF protein biomarkers for Alzheimer’s and Frontotemporal Dementia. He is actively involved in interdisciplinary teams, contributing to open-access datasets on strawberry metabolomics and dementia biomarkers. His research outputs span high-impact journals in bioinformatics, agriculture, and neuroscience, emphasizing precision agriculture and personalized medicine approaches.

• Statistical Genetics
• Graphical Models
• Genomics
• +۵ مورد دیگر

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

John van den Elstمشاهده پروفایل

مدرس

• Graphical Password Security
• Cybersecurity
• Information Security
• +۳ مورد دیگر

John van den Elst is a Lecturer at the Academy for AI, Games & Media within The Hague University of Applied Sciences. His research focuses on cybersecurity, particularly graphical password schemes and dynamic layering techniques, alongside business model analysis in media industries. He collaborates on projects like Picassopass, exploring secure authentication methods through visual interfaces. His work bridges technical and strategic domains, addressing both user authentication challenges and media industry partnerships. Notable outputs include studies on security evaluation of graphical passwords and business model interrelations in media concepts. Despite no listed awards or grants, his contributions reflect interdisciplinary innovation in applied computing and media studies.

• Design
• Sustainable Packaging
• Brand Management
• +۱۲ مورد دیگر

Maaike Mulder Nijkamp is a Lecturer in Product–Market Relations at the University of Twente, where she has been actively contributing to research and education. Her work bridges design, branding, and sustainability, with a strong focus on packaging innovation and transdisciplinary collaboration. Research Interests: Her research spans Design , Sustainable Packaging , Brand Management , and Transdisciplinary Collaboration . She integrates design science with behavioral insights to explore how brands can innovate sustainably. Her work emphasizes the balance between brand differentiation and standardization, particularly in environmentally conscious markets. Publication Trends: Over the past decade, her publications reveal a consistent trajectory toward sustainable design, brand evolution, and educational innovation. Recent works highlight the role of designers in corporate reputation, the development of brand extensions, and the creation of learning ecosystems for packaging innovation. Her research is applied and interdisciplinary, often involving case studies and designerly approaches. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: While no formal students or grants are listed, her PhD thesis and collaborative publications suggest active mentorship and research project involvement. She has co-authored works with colleagues on transdisciplinary learning and design innovation, indicating engagement in academic collaboration and possibly funded projects. Labs and Teams: She is involved in initiatives promoting transdisciplinary collaboration, particularly in packaging development. Her work on learning ecosystems suggests participation in educational or research teams focused on sustainability and design integration.

• Inverse Problems
• Variational Methods
• Total Variation Regularization
• +۷ مورد دیگر

Jose A. Iglesias Martínez is an Assistant Professor in the Department of Applied Mathematics at the University of Twente, affiliated with the Mathematics of Imaging and AI (MIA) group within the Faculty of Electrical Engineering, Mathematics and Computer Science. He conducts research at the intersection of variational analysis, inverse problems, shape analysis, and machine learning, with a geometric perspective on regularization and optimization. University: University of Twente School: Faculty of Electrical Engineering, Mathematics and Computer Science Department: Department of Applied Mathematics Research Group: Mathematics of Imaging & AI (MIA) His research focuses on analytic and variational methods for inverse problems in imaging, particularly involving total variation and related regularization models. The geometric structure of function domains and convex geometry in minimization problems plays a central role in his work. He applies these methods to imaging, shape analysis, and machine learning, with strong mathematical foundations in PDEs, functional analysis, and optimization. The recent publications reflect a consistent focus on variational regularization, extremal point analysis, sparse optimization, and PDE-constrained problems. Key themes include total variation, TGV, nonlocal perimeters, and shape optimization, with applications in image denoising, optical flow, and fluid dynamics. His work spans both theoretical convergence analysis and algorithmic development, often with computational implementations. Scientific Awards and Recognition: Member of the Editorial Board, Numerical Functional Analysis and Optimization (since August 2023) He actively teaches in applied mathematics, including courses such as Analysis 3, Mathematics behind Data-Driven Methods, and interdisciplinary topics like Art, Mathematics and Technology. He has supervised tutorials and projects in calculus, linear algebra, and modeling. His academic training includes dual MSc degrees in Mathematics and Telecommunication Engineering from Madrid, a PhD (2015) and habilitation (2021) in Mathematics from the University of Vienna, followed by postdoctoral work at the Radon Institute in Linz. He has no listed students yet, but is actively publishing and contributing to the mathematical imaging community. He is involved in editorial work and maintains a strong publication record in top-tier applied mathematics journals.

Paul M.J. Van den Hofمشاهده پروفایل

استاد

• Data-driven modeling
• Control systems
• Dynamic networks
• +۶ مورد دیگر

Paul M.J. Van den Hof is a Full Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), specializing in data-driven modeling and control of dynamic systems. He leads the Control Systems Group and is affiliated with multiple research centers, including EAISI High Tech Systems, Mobility, and Foundational, as well as the EIRES Research initiative. Research Interests: His work focuses on developing fundamental techniques for dynamic network modeling, closed-loop identification, data analytics, experimental design, and model-based control. Applications span industrial process control, oil reservoir engineering, high-tech mechatronic systems, and cyber-physical systems. Recent projects include SYSDYNET (dynamic network identification), NAPAS (nanometer-accurate actuation), and CADUSY (data-driven modeling using symbolic methods). Scientific Contributions: Van den Hof has authored over 250 research outputs, including foundational work on identifiability in dynamic networks, frequency domain identification, and distributed control algorithms. His 2024-2025 publications address challenges in fault detection, Bayesian network topology identification, and multi-step least squares methods. He emphasizes practical implementation through tools like the SYSDYNET MATLAB toolbox. ERC Advanced Research Grant (2015) Honorary Member, Hungarian Academy of Sciences (2016) IEEE Fellow (2007) and IEEE Life Fellow (2023) IFAC Advisor (2020)