جست‌وجوی استاد و پژوهشگر

• Data Analytics
• Probability Density Functions
• Peer-to-Peer Networks
• +۲ مورد دیگر

Dr. Rogier Brussee is an Assistant Professor in Data Analytics at Eindhoven University of Technology. His research focuses on computational modeling, statistical analysis, and network systems. Key projects include reverse stress testing in supply chains (RESTRETCH) and peer-to-peer positioning algorithms. Research interests span probability density functions, thruster engineering, ship maneuvering dynamics, and distributed network systems. His work frequently bridges data science with industrial applications. Recent publications demonstrate a strong interdisciplinary trend, combining computer science with environmental studies, finance, and social sciences. Articles frequently employ machine learning for predictive modeling and system optimization. Dr. Brussee leads research within the RESTRETCH project and collaborates internationally on supply chain resilience. No specific awards or student advising roles are detailed in available records.

Frans M.J. Willemsمشاهده پروفایل

استاد

• Information Theory
• Data Compression
• Digital Communication
• +۵ مورد دیگر

Frans M.J. Willems is a Full Professor Emeritus at Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems group within the Department of Electrical Engineering. His primary research focuses on information theory, including multi-user communication systems, data compression, digital communication, biometrics, and privacy/security aspects of information systems. Education: MSc in Electrical Engineering (1979), TU/e PhD in Mathematics (1982), University of Leuven Research Contributions: Willems has authored over 295 publications and holds multiple patents. His work spans foundational topics like context-tree weighting algorithms, biometric privacy frameworks, and MIMO communication systems. He served as IEEE Transactions editor (1988–2006) and became an IEEE Fellow in 2005. Projects: DIGI-OPT : Optical fiber communication technologies (2018–2026) MyWave : Energy-efficient massive MIMO systems (2019–2023) RESCURE : Cybersecurity for critical infrastructure (2018–2020) Awards: IEEE Fellow (2005). Advisory Roles: Advised Philips Research on information theory applications (1999–2018). Supervised 35 students and contributed to 11 major research projects. Labs/Teams: Leads the Signal Processing Systems group at TU/e, collaborating on topics like nonlinear optical channels and secure communication systems.

Giulia Ferrandiمشاهده پروفایل

پژوهشگر

• Statistics
• Linear Algebra
• Optimization
• +۳ مورد دیگر

Giulia Ferrandi is a Researcher and Guest Researcher at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). She completed her PhD under the supervision of Prof. Michiel Hochstenbach (TU/e) and Prof. Rosário Oliveira (IST, Lisbon) as part of the European BIGMATH project. Her research focuses on Statistics, Linear Algebra, and Optimization applied to statistical problems, with contributions to gradient methods, trace ratio problems, and Markov chain analysis. She holds a master’s degree from Università degli Studi di Milano (Italy), specializing in Probability and Statistics. Prior to her academic roles, she briefly taught in high school and worked in industry. Research Interests : Her work bridges numerical linear algebra and optimization, with emphasis on: Development and analysis of gradient methods (e.g., limited memory, harmonic frameworks) Applications of Rayleigh quotients in optimization and eigenvalue problems Robust multigroup classification via trace ratio techniques Statistical analysis of Markov chains for farmland transitions and non-stationary processes Publications : Her recent work demonstrates contributions to: Subspace methods for large-scale trace ratio problems Advancements in unconstrained optimization via limited-memory gradient approaches Integration of Rayleigh quotients into gradient-based algorithms Harmonic frameworks for optimizing stepsizes in numerical methods Collaborations & Grants : Her PhD was funded by the EU’s BIGMATH project. Collaborators include Prof. Michiel Hochstenbach (TU/e) and Prof. Nataša Krejić (University of Novi Sad). No explicit advising roles or grants are listed beyond her doctoral funding. Technical Expertise : Expertise in numerical analysis, statistical modeling, and linear algebra applications.

• Biomedical Engineering
• Computational Biology
• Medical Data Science

Dr. Arjen-Kars Boer is a University Researcher at Eindhoven University of Technology specializing in Biomedical Engineering and Computational Biology. His work advances wearable sensor technologies for clinical monitoring, with applications in postoperative care, COVID-19 patient management, and cardiovascular health. Research contributes to UN Sustainable Development Goals through innovations in medical diagnostics and remote patient monitoring systems. Key projects include developing the CoLab score for ICU COVID-19 response tracking and validating wrist-worn photoplethysmography devices for abdominal surgery patients. His multidisciplinary approach integrates data science with biomedical engineering to create predictive tools like the Heartmarker Score for cardiac patients. Collaborative work spans academic institutions and healthcare systems across Europe. Media coverage highlights his COVID-19 diagnostic innovations, including international recognition for rapid blood test development. Teaches 'Applied Clinical Data Science' and maintains active research output with 36+ publications in journals including Scientific Reports and European Journal of Surgical Oncology.

Iris Huijbenمشاهده پروفایل

پژوهشگر

• Representation Learning
• Sleep Medicine
• Medical Signal Processing
• +۳ مورد دیگر

Iris Huijben is a Researcher and Doctoral Candidate in the Signal Processing Systems group at Eindhoven University of Technology (TU/e). Her work bridges signal processing and deep learning in sleep medicine and fetal health monitoring. She holds an MSc in Electrical Engineering (cum laude, 2019) from TU/e and is pursuing a PhD under Prof. Ruud van Sloun, focusing on discrete representation learning for sleep recordings. Research Interests: Representation learning for biomedical signals, fetal health assessment using cardiotocograms, sleep stage analysis, and machine learning-driven data compression. Key Projects: Developed attention mechanisms for sleep stage classification, improved fetal health anomaly detection with contrastive predictive coding, and analyzed temporal dynamics of parasomnia awakenings. Collaborations: Research internship at Qualcomm AI Research (Amsterdam) on data compression. Publications highlight advancements in medical AI, including real-time fetal monitoring systems and sleep dynamics analysis. Awards include TU/e’s MSc Thesis Award 2020 for work on sub-Nyquist sampling in medical ultrasound.

Sjoerd van Altenمشاهده پروفایل

پژوهشگر

• Human Capital Formation
• Genoeconomics
• Health Economics
• +۳ مورد دیگر

Sjoerd van Alten is a Researcher and PhD candidate at the Department of Economics, Vrije Universiteit Amsterdam (VU), within the School of Business and Economics. His research focuses on human capital formation, genoeconomics, and health economics, with particular attention to genetics' role in socioeconomic outcomes and health disparities. Van Alten holds an MSc in Economics from the Tinbergen Institute (2019) and a BSc in Economics & Business Economics from Utrecht University (2017). He teaches courses including 'Quantitative Research Methods II' and 'Human Capital.' His work has been recognized through awards like the Charles J. Epstein Award (semifinalist) and an ODISSEI Microdata Access Grant. His research addresses selection bias in genetic studies using large datasets like the UK Biobank, with articles in Nature Communications and International Journal of Epidemiology . Key themes include volunteer bias correction in GWAS, genetic determinants of socioeconomic status, and health-related economic behaviors. Media engagements include expert commentary on genetics and inequality for Dutch outlets. Grants: ODISSEI MAG (2021), Charles J. Epstein Award (2021) Media: 2 expert commentaries (2025) Courses: Quantitative Research Methods II (2024-2025)

Mark Boonsمشاهده پروفایل

استادیار

• Digital Innovation
• Crowdsourcing
• Gig Economy
• +۳ مورد دیگر

Dr. Mark Boons is an Assistant Professor at the Department of Knowledge, Information and Innovation within the School of Business and Economics at Vrije Universiteit Amsterdam. He holds a PhD from Erasmus University Rotterdam (2013). His research focuses on digital innovation, crowdsourcing, gig economy dynamics, and digital platform organizations. Boons investigates how organizations leverage crowdsourcing for knowledge integration, process innovation, and addressing grand challenges like sustainability. His work spans topics such as platform design impacts on service providers, algorithmic HR management, and the psychological drivers of crowd participation. Notable contributions include studies on failure effects in crowdfunding, cooperative behaviors in crowdsourcing platforms, and distributed knowledge dynamics. Boons has collaborated internationally, evidenced by his projects on algorithmic accountability and the Future of Learning Challenge. Boons has received the SBE Education Award (2021) for contributions to collaborative co-creation initiatives. His research has been published in leading journals like Research Policy and IEEE Transactions on Engineering Management , with over 15 peer-reviewed articles. Ancillary activities include serving as an external academic reviewer at BI Oslo. Current projects include exploring governance frameworks for digital transformation and student teacher co-creation in education. His work bridges organizational behavior, innovation management, and information systems to address 21st-century challenges in digital ecosystems.

Reinout de Vriesمشاهده پروفایل

استاد

• Organizational Psychology
• Personality Psychology
• HEXACO Personality Traits
• +۳ مورد دیگر

Reinout de Vries is a Full Professor at the Faculty of Behavioural and Movement Sciences and IBBA (Institute for Behavioural and Brain Science) at Vrije Universiteit Amsterdam, specializing in Organizational Psychology. His research focuses on personality traits (e.g., HEXACO, TNT), their applications in work and organizational settings, and the use of AI for personality assessment. He has authored over 176 publications and actively contributes to academic discourse through editorial roles and media commentary. Research Interests: Personality Psychology (HEXACO Model) Workplace Dynamics and Leadership Psychometrics and Assessment Technologies AI-driven Personality Evaluation Social and Organizational Behavior Recent Articles: Highlighting advancements in personality assessment via large language models, the impact of nonnormative traits on social preferences, and meta-analyses of HEXACO traits. These studies bridge theoretical models with practical applications in hiring and leadership. Awards/Grants: Secured the NWO Open Science Fund (2023) to support transparent research practices. Media contributions include expert commentary on leadership, assessments, and ethical hiring. Advising & Grants: Supervised 8 PhD theses and collaborates on grants promoting open science. Teaching includes courses like Personality at Work and Personality Theory and Assessment. Labs/Teams: Active in interdisciplinary teams at IBBA and VU, focusing on behavioral and brain sciences research.

Giulia Ranziniمشاهده پروفایل

استادیار

• Social Media
• Privacy Concerns
• Online Relationships
• +۴ مورد دیگر

Giulia Ranzini is an Assistant Professor in Social Media at the Department of Communication Science, Vrije Universiteit Amsterdam. She holds a PhD in Strategy and Management from the University of St. Gallen. Her roles include teaching courses on organizational communication, branding, and media entertainment, and she serves as Teaching Coordinator for the department. Affiliated with the Network Institute and the Communication Choices, Content and Consequences (CCCC) research group, her work focuses on privacy concerns and technology-mediated decision-making, particularly in online relationships and data management. Education: PhD in Strategy and Management, University of St. Gallen (2014). Research Interests: Privacy paradox, online dating dynamics, sharenting behaviors, and the intersection of technology and identity. Her work emphasizes privacy cynicism and agency constraints in digital environments, with contributions to understanding surveillance capitalism and sharing economy ethics. Grants & Projects: Recipient of the NWO Starters Grant (2019). Current project: 'CSR efforts: corporate approaches and consumers' attitudes' (2023–2029). Media Contributions: Public commentary on online dating trends and digital privacy via platforms like Universiteit van Nederland, reaching 21+ news outlets.

Luuk Lagerwerfمشاهده پروفایل

دانشیار

• Communication Studies
• Political Communication
• Health Communication
• +۵ مورد دیگر

Dr. Luuk Lagerwerf is an Associate Professor in the Department of Communication and Information Sciences at Vrije Universiteit Amsterdam’s Faculty of Social Sciences and Humanities. He also holds an affiliation with the Network Institute. His research focuses on communication message design and effects in news, political, and health contexts, employing methodologies like linguistic analysis, corpus studies, experimentation, and media effects research. He has contributed to educational program development, including Communication Science programs at Vrije Universiteit Amsterdam and the University of Twente. Lagerwerf has served in academic management roles, including acting chair positions and eight years as interim head of Communication Science and Language and Communication departments until 2020. He is editor of Tekstblad since 2007 and secretary of the Vereniging Interuniversitair Overleg Taalbeheersing (VIOT) until 2019. Current roles include Programme Director of Communication and Information Studies since 2021. Education Contributions: Developed courses across linguistics, sociology, and communication studies; led program renewals and educational committees. Research Interests: News and political communication effects, visual metaphors, health communication strategies, and advertising effectiveness. Projects: Recent initiatives include Valuing Visuals (visual element analysis in art and social media) and Clickworthiness (headline characteristics predicting audience engagement). Teaching: Courses include Communication and Effect Research, Forensic Linguistics, and Narrative Analysis. His work bridges interdisciplinary research and practical application, with over 70 publications and active roles in academic governance and professional journalism.

Marvin Neumannمشاهده پروفایل

استادیار

• Personnel Selection
• Algorithmic Decision-Making
• Psychological Assessment
• +۵ مورد دیگر

Dr. Marvin Neumann is an Assistant Professor in Organizational Psychology at the Faculty of Behavioural and Movement Sciences , Vrije Universiteit Amsterdam. He also holds an appointment at the IBBA. His research focuses on algorithmic decision-making in HR contexts, emphasizing predictive validity, autonomy, and ethical implementation of mechanical prediction systems. Neumann has been awarded the 2023 Abbas prijs and the 2024 Best dissertation award for his contributions to selection psychology. His work bridges psychological theory with practical applications in personnel selection and educational testing. Notable contributions include guidelines for decision-rule design and studies on overcoming algorithm aversion in organizational settings. Neumann teaches advanced courses like Human Resource Development and M&D3: Individual and Organizational Diagnosis, emphasizing evidence-based practices. Neumann has published widely in journals such as the Journal of Business and Psychology and Computers in Human Behavior , with a focus on improving advisor systems, transparency in algorithms, and the human factors influencing algorithm adoption. His research network includes collaborations with institutions like the University of Minnesota and frequent peer-review roles for leading journals in selection assessment.

• Genetic Heterogeneity
• Epigenetics
• Molecular Biology
• +۶ مورد دیگر

Tanya Phung is a Researcher affiliated with the Complex Trait Genetics department at the Vrije Universiteit Amsterdam, within the Faculty of Science. She is also part of the Amsterdam Neuroscience research group. Her work focuses on genetic heterogeneity, epigenetics, X chromosome inactivation, and molecular mechanisms in health and disease. Key research areas include placental development, alcohol use behaviors, respiratory pathology, and evolutionary adaptations in human populations. Her research interests span multiple disciplines, with a strong emphasis on genomic studies and their applications to clinical and evolutionary contexts. Notable contributions include investigations into the genetic basis of alcohol-related behaviors, the molecular mechanisms of squamous metaplasia in respiratory disorders, and the role of epigenetic regulation in placental biology. She also explores genomic adaptations in populations such as the Turkana people, highlighting human-environment interactions. Publications highlight trends in genetic and epigenetic analysis, with recent work emphasizing sex chromosome studies, tumor heterogeneity, and computational methods in genomics. Collaborations include projects with the COGA Investigators and researchers in oncology and anthropology.

Jiahuan Peiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Dialogue Systems
• +۳ مورد دیگر

Dr. Jiahuan Pei is an Assistant Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Department of Artificial Intelligence, the Network Institute, and Social AI. His research focuses on advanced AI applications in dialogue systems, natural language processing (NLP), and large language models (LLMs), with particular emphasis on clinical and therapeutic contexts. Recent work includes improving conversational recommendation systems, enhancing psychotherapy dialogue generation through motivational interviewing strategies, and developing multilingual dialogue management techniques. Key research topics include information retrieval robustness in LLMs, parameter-efficient fine-tuning methods (e.g., MELoRA), and the ethical integration of AI in healthcare. He has contributed to seminal studies on dialogue alignment, faithfulness metrics for citations, and perceptual quality in 3D point clouds. His work often bridges theoretical NLP advancements with practical applications in healthcare, education, and mixed reality environments. Pei's articles reflect a strong focus on innovation in dialogue systems, with over 15 peer-reviewed publications since 2020. He teaches the Project Conversational Agents course, emphasizing hands-on AI development. Collaborations span academic and industry partners, addressing challenges in cross-lingual communication, therapeutic AI, and multimodal training systems.

• Independent Component Analysis
• Clustering Methods
• Model Selection
• +۵ مورد دیگر

Jeffrey Durieux is a Researcher at the Department of Econometrics, Erasmus School of Economics, Erasmus University Rotterdam, specializing in advanced statistical methods for brain imaging data analysis. His work bridges econometrics, computational neuroscience, and machine learning to develop novel approaches for fMRI data interpretation. His research focuses on Independent Component Analysis , clustering methodologies , and model selection for high-dimensional neuroimaging datasets. Key contributions include Clusterwise ICA (C-ICA) frameworks for identifying neurofunctional subtypes, optimization of overlapping clustering strategies, and evaluation of data reduction techniques in big-data neuroscience contexts. His methodologies particularly address challenges in resting-state fMRI analysis and subject stratification. Durieux's publication record demonstrates consistent innovation in computational neuroscience since 2019, with increasing focus on practical implementations (e.g., R packages) and methodological rigor in clustering subject populations based on brain connectivity patterns. His work shows strong interdisciplinary impact across statistics, computer science, and clinical neuroscience. Scientific recognition includes: IOPS Best Paper Award 2022 (awarded June 8, 2023) IFCS Cluster Benchmarking Challenge 2022 Collaborative research efforts with S.A.R.B. Rombouts, T.F. Wilderjans, and other neuroimaging specialists reflect his integration within major neuroscience research networks, though specific grant funding or student supervision details remain unreported in available sources.

• Distillation
• Nanofiltration
• Membrane Technology
• +۳ مورد دیگر

Gerrald Bargeman is a Full Professor specializing in fluid separation processes, particularly focusing on distillation, nanofiltration, and membrane technology. His research spans chemical engineering, thermodynamics, and materials science, with a strong emphasis on optimizing separation processes for industrial applications. He has contributed to advancements in reactive distillation, solvent selection algorithms, and membrane design for extreme conditions. His work addresses challenges in desalination, sustainable chemistry, and process intensification. Research interests include thermodynamic modeling of phase equilibria, membrane performance under extreme pH, and solvent engineering for green chemical processes. Collaborative efforts involve developing novel nanofiltration membranes and improving reactive distillation column designs. Publications highlight contributions to membrane stability, solvent selection methodologies, and energy-efficient separation techniques. His research aligns with industrial needs for cost-effective and environmentally sustainable solutions. Notably absent from the profile are awards or formal student advisees. Key collaborations focus on optimizing separation technologies across chemical, food, and water industries.