جست‌وجوی استاد و پژوهشگر

Dennis Fokمشاهده پروفایل

استاد

• Applied Econometrics
• Marketing Modeling
• Bayesian Statistics
• +۳ مورد دیگر

D. (Dennis) Fok is a Professor of Econometrics and Data Science at the Department of Econometrics, Erasmus School of Economics, Erasmus University Rotterdam. He has been affiliated with the institution since 1999 and serves as a Fellow at the Erasmus Research Institute of Management (ERIM). His research focuses on applied econometrics in marketing contexts, particularly: Modeling unobserved heterogeneity Nonlinear econometric models Bayesian statistical methods Consumer-level decision modeling High-dimensional choice modeling Dynamic marketing mix effects Dennis Fok's work combines econometric theory with practical marketing applications, developing efficient estimation techniques for complex consumer behavior models. His methodological contributions have been adopted in various business contexts including retail analytics and loyalty program optimization. ERIM Top Article Junior Award (2017) ERIM Top Article Award (2010) ERIM Award for Outstanding Performance by a Young Researcher (2006) ERIM Dissertation Award (2004) He has supervised numerous PhD candidates across diverse topics including: Marketing modeling for new products Econometric advances in diffusion models Heterogeneity in customer purchase behavior Brand portfolio management High-dimensional assortment modeling Response style modeling

Tom Oomenمشاهده پروفایل

استاد

• Control Systems
• System Identification
• Machine Learning
• +۷ مورد دیگر

Tom Oomen is a full professor in the Department of Mechanical Engineering at Eindhoven University of Technology. He specializes in control systems, system identification, and mechatronics, with applications in precision engineering, semiconductor technology, and healthcare. His research focuses on data-driven control strategies, integrating machine learning and artificial intelligence to enhance system performance. He has held academic positions at KTH Royal Institute of Technology, The University of Newcastle, and Delft University of Technology. Recipient of the 7th Grand Nagamori Award and NWO Veni/Vidi grants. Editor roles: Senior Editor of IEEE Control Systems Letters and Co-Editor-in-Chief of IFAC Mechatronics. Research interests include advanced motion control, iterative learning control, and fault detection. He teaches courses like Advanced Motion Control and organizes post-academic courses through the Mechatronics Academy. Collaborates with industries in semiconductor equipment, printing, space technology, and healthcare. Recent articles explore topics such as random learning in ILC, nonlinear control for ventilators, and gravitational wave detection systems. Advises PhD students including Max van Meer, Max van Haren, and Koen Classens.

Marco Zullichمشاهده پروفایل

پژوهشگر

• Neural Networks
• Uncertainty Estimation
• Machine Learning
• +۳ مورد دیگر

Marco Zullich holds a Ph.D. in Industrial and Information Engineering from the University of Trieste (2023). His research focuses on neural networks, uncertainty estimation, and machine learning applications. Key areas include Bayesian neural networks, input uncertainty modeling, and reinforcement learning. He has contributed to conferences such as the International Joint Conference on Computer Vision and the European Summer School on AI. Education: Ph.D. in Industrial and Information Engineering, University of Trieste (2023) Research Interests: Neural network architectures and their applications in uncertainty quantification Reinforcement learning for interpretable control systems Deep learning methods for image super-resolution Adaptive prompt tuning in few-shot learning scenarios Recent Work Trends: His publications emphasize integrating uncertainty estimation into neural networks, exploring novel reinforcement learning frameworks, and advancing vision-guided machine learning techniques. Teaching & Outreach: Contributed to courses like the ENLIGHT BIP Course on 'Deep Learning for Forestry' and the European Summer School's 'Uncertainty Quantification in Machine Learning'.

Maurits Kapteinمشاهده پروفایل

استاد

• Statistics
• Statistical Learning
• Causality
• +۴ مورد دیگر

Prof. dr. Maurits Kaptein is a part-time Professor of Applied Causality at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He will deliver his inaugural lecture on February 6, 2025, titled 'Uncertainty Propagation in Causal Effects Estimation.' Current Role : Supervises PhD students, contributes to TRI-DSA research, and develops causal frameworks. Education & Career : MSc in Economic Psychology (Tilburg), PdEng (TU/e), PhD in Industrial Design (TU/e/Stanford), postdoc in Quantitative Marketing (Aalto), and former assistant/associate/full professor at Radboud University and Tilburg University. Research Interests : Focuses on causality, statistical learning, and methodology. Key subtopics include causal effect estimation, uncertainty propagation, Bayesian multivariate logistic regression, and real-world evidence validation. His work bridges theoretical statistics with applied healthcare and technology solutions. Recent Publications : His 2024-2025 articles address causal uncertainty frameworks, preterm birth prediction via machine learning, and real-world evidence validation in oncology. These works emphasize multivariate Bayesian approaches, heterogeneous treatment effects, and cross-disciplinary applications in medicine. Ancillary Activities : CEO of Scailable B.V., data advisor for Luscii/OMRON, and speaker at The Next Speaker Global.

• Agricultural Science
• Plant Nutrition
• Statistical Modeling
• +۵ مورد دیگر

Joost van Heerwaarden is an Assistant Professor in Plant Production Systems at Wageningen University & Research. His research focuses on agricultural sustainability, statistical modeling in agriculture, and optimizing crop management practices in Sub-Saharan Africa and tropical regions. He specializes in fertilizer recommendations, crop-nutrient interactions, and leveraging citizen science for decentralized farming solutions. Key areas include: (1) Developing spatial and statistical models for crop productivity (e.g., spatial autoregressive graphical models and copula methods), (2) Evaluating fertilizer efficiency in cocoa and grain legumes, (3) Promoting participatory approaches like the Tricot framework for on-farm testing, and (4) Studying rhizobia-legume symbiosis to enhance nitrogen fixation in African soils. He collaborates widely across institutions such as the University of Groningen and CGIAR centers. His work addresses food security challenges through predictive analytics, precision agriculture, and bridging gaps between scientific research and smallholder farmer needs. Notable projects include the Food Security Intelligence initiative and contributions to the N2Africa project promoting nitrogen fixation technologies. His datasets and models are publicly accessible via platforms like FigShare and GitHub.

Ymkje de Vriesمشاهده پروفایل

پژوهشگر

• Mental Health
• Psychiatry
• Meta-Analysis
• +۷ مورد دیگر

Ymkje de Vries is a researcher at the University of Groningen's Faculty of Behavioural and Social Sciences, within the Developmental Psychology department. Her work focuses on mental health epidemiology, psychiatric treatment efficacy, and methodological critiques of clinical research. She has contributed to studies on comorbid mental disorders, antidepressant effectiveness, and global mental health trends across populations including older adults, children, and LGBTQ+ communities. Key research interests include: Antidepressant efficacy and bias in pharmaceutical reporting Longitudinal mental health outcomes in diverse populations Comorbidity patterns and disability impacts Clinical trial methodology and transparency Recent articles highlight her work on suicidal ideation in depressed elderly patients (2025), Bayesian reanalysis of cancer drug efficacy (2024), and large-scale child psychiatric surveys (2024). She has received awards including the Gratama Wetenschapsprijs (2020) and For Women in Science Rising Talents Prize (2021). Her research often involves international collaborations, with contributions to the World Mental Health Surveys initiative. She has supervised two academic works and contributed datasets to open science platforms.

Dimitris Rizopoulosمشاهده پروفایل

استاد

• Joint Models for Longitudinal and Time-to-Event Data
• Dynamic Predictions
• Causal Inference
• +۷ مورد دیگر

Dimitris Rizopoulos is a Full Professor in the Department of Epidemiology at Erasmus MC, a leading academic medical center in the Netherlands. His work is centered on advanced statistical methodologies with direct applications in clinical and public health research. His primary research interests include: Joint modeling of longitudinal and time-to-event data Dynamic prediction and personalized risk assessment Causal inference in observational studies Machine learning applications in precision medicine Bayesian statistical methods His recent publications reveal a strong trend in developing and applying statistical models to real-world clinical problems, particularly in cardiology (e.g., heart failure monitoring), oncology (e.g., biopsy scheduling), and healthcare delivery (e.g., clinical decision support systems). The articles demonstrate a consistent focus on multivariate longitudinal biomarkers, competing risks, and simulation-based validation of novel methodologies. He has contributed to high-impact journals such as JAMA , Statistics in Medicine , and Insights into Imaging , reflecting the translational nature of his work. His research often involves interdisciplinary collaboration with clinicians, epidemiologists, and health economists. While no specific scientific awards are listed in the provided text, his extensive publication record, dataset contributions, and role as a principal investigator suggest significant recognition in the field of biostatistics and epidemiology. Rizopoulos has supervised at least 10 students or junior researchers, indicating an active role in academic mentoring. His work includes grant-funded research, particularly in the development and evaluation of clinical prediction models and decision support tools, as evidenced by trials such as the MIDAS study. He has co-developed and shared important datasets related to joint modeling and cost-effectiveness analysis, supporting open science and reproducibility. These datasets have been widely referenced and used in the research community, indicating leadership in methodological dissemination.

• Financial Econometrics
• Time Series Econometrics
• Term Structure Modeling
• +۷ مورد دیگر

Michel van der Wel is a Full Professor at the Econometrics Institute within the Erasmus School of Economics, Erasmus University Rotterdam, where he also serves as Vice Dean Education. He is an active researcher and educator in the fields of financial econometrics and macro-finance. Full Professor, Econometrics Institute, Erasmus School of Economics Vice Dean Education, Erasmus School of Economics Research Focus: Financial Econometrics, Time Series, Term Structure, Macro-Finance His research interests lie at the intersection of econometrics, finance, and macroeconomics. He specializes in modeling financial time series, particularly yield curves and volatility, with a focus on the implications of monetary policy and macroeconomic conditions on financial markets. His work often employs advanced econometric techniques to analyze asset pricing, term structure dynamics, and financial stability. The recent trends in his publications show a strong emphasis on modeling challenges during periods of low interest rates, including the zero lower bound, as well as the use of machine learning in credit risk assessment and the macroeconomic impacts of central bank interventions. His work bridges theoretical econometrics with practical financial applications. Michel van der Wel has published in leading journals such as the Journal of Finance , Journal of Econometrics , and Journal of Financial Economics . His research has been widely cited and downloaded, indicating significant academic impact. He supervises academic work and contributes to doctoral and master’s education, particularly in quantitative finance. While specific grant details are not listed, his collaborative and policy-relevant research suggests involvement in funded research projects. He has delivered an inaugural speech on integrating macroeconomics and finance through econometrics, underscoring his leadership in the field. He is associated with key research centers at Erasmus University, including the Erasmus Research Institute of Management (ERIM), and collaborates extensively with other leading economists and financial researchers across Europe.

Agnieszka Borowskaمشاهده پروفایل

مدرس

• Computational Statistics
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Agnieszka Borowska is a Lecturer in the Econometrics and Data Science department at Vrije Universiteit Amsterdam's School of Business and Economics. She also serves as an Honorary Research Associate at the University of Glasgow's School of Mathematics and Statistics. Her research focuses on computational statistics, Bayesian methods, and machine learning applications in biomedical engineering, particularly in cardiovascular mechanics and stochastic modeling. She has published extensively on topics like cardiac mechanics modeling, parameter inference in biomedical systems, and neural network-based medical image analysis. Key research interests include developing statistical methodologies for complex biological systems, integrating machine learning with traditional statistical approaches, and applying these to problems in cardiology and biomedical engineering. Her work often involves collaboration with interdisciplinary teams to solve real-world clinical challenges. Recent research has explored Bayesian optimization for cardiac model parameterization, Gaussian process-enhanced approximate Bayesian computation for chemotaxis systems, and neural network-based prediction of left ventricle geometries from cardiac MRI data. These studies highlight her expertise in bridging statistical theory with practical biomedical applications.

Richard A.J. Postمشاهده پروفایل

پژوهشگر ارشد

• Bayesian Statistics
• Survival Analysis
• Causal Inference
• +۵ مورد دیگر

Richard A.J. Post is a Research Fellow at the Department of Statistics , Mathematics and Computer Science School at Eindhoven University of Technology . His work bridges statistical methodology with biomedical applications, particularly in causal inference and survival analysis. PhD thesis: Statistical inference for induced seismicity in the Groningen gas field (2017) ICMS Core Member since 2022 Active in cardiovascular risk prediction and causal effect modeling Research interests focus on Bayesian survival analysis , causal inference , and statistical modeling applied to biomedical and epidemiological contexts. His work addresses methodological challenges in handling heterogeneity in causal effects and uncertainty quantification in personalized risk assessments. Publication trends show expertise in: Bayesian computational methods (2024-2025) Statistical epidemiology (2024) Causal effect modeling (2024-2025) Cardiovascular disease analysis (2024) Seismicity statistics (2017) Public health policy evaluation (2021) Scientific awards include: ICMS Fellowship (2022) Collaborative activities span European research networks in pandemic response and cardiovascular epidemiology. Current work explores statistical methods for individualized treatment effect estimation and uncertainty quantification in health predictions.

Ondřej Rokošمشاهده پروفایل

استادیار

• Computational Mechanics
• Materials Engineering
• Multiscale Modeling
• +۴ مورد دیگر

Ondřej Rokoš is an Assistant Professor in the Mechanics of Materials section at Eindhoven University of Technology's Department of Mechanical Engineering. He obtained his PhD in Civil Engineering from Czech Technical University in Prague (2014) and joined TU/e as faculty in 2020 after postdoctoral positions at CTU, TU/e, and University of Luxembourg. His research focuses on multiscale phenomena in materials engineering, particularly computational homogenization techniques for metamaterials. Research Interests: Rokoš specializes in computational methods for bridging scale-dependent material behaviors, including: Mathematical modeling of microstructure-property relationships Homogenization techniques for metamaterials and inelastic materials Quasicontinuum methods and molecular statics simulations Experimental-computational approaches using digital image correlation Stochastic modeling of structural dynamics His work aims to enable optimal material design through fundamental understanding of cross-scale physical phenomena. Publication Trends: Rokoš's 52+ publications show evolving focus from fundamental homogenization methods (2016-2019) toward machine learning applications in materials design (2020-2025). Recent work integrates graph neural networks, reduced-order modeling, and experimental mechanics for programmable metamaterials. Key domains include mechanical metamaterials (67% of works), computational homogenization (47%), and multiscale engineering (45%). Teaching & Service: Courses taught include: Solid Mechanics (since 2013) Machine Learning for Multi-Physics Modelling (since 2020) Computer Aided Engineering (since 2013) He leads Group Rokos and collaborates internationally on metamaterials research. Media frequently cites his work on micromechanical parameter identification techniques.

Jacques de Swartمشاهده پروفایل

استاد

• Bayesian Statistics
• Responsible AI
• Strategic Decision Making
• +۵ مورد دیگر

Prof. Jacques de Swart is a Professor of Applied Mathematics at Nyenrode Business University, where he is part of the Faculty Research Center for Accounting, Auditing & Control. He holds a unique part-time role, dedicating one day per week to academia while serving as a Partner at PwC’s Consulting practice for the remaining four days. His expertise bridges mathematical rigor with business applications, focusing on audit processes, Bayesian statistics, and strategic decision-making aligned with ESG principles. Prof. de Swart’s academic journey includes a Mathematics degree from Utrecht University, with studies at La Sapienza University in Rome. He completed his PhD in Computational Science at the University of Amsterdam under the Centre for Mathematics and Computer Science (CWI). Later, he earned an MBA from Rotterdam School of Management, Erasmus University. His research interests revolve around Bayesian statistical methods, responsible AI integration, and strategic decision-making frameworks that prioritize environmental, social, and governance (ESG) considerations. He actively develops and teaches modules in machine learning, statistics, and programming, emphasizing their practical applications in auditing and business analytics. His publications highlight the application of Bayesian approaches to audit sampling and transparency, advocating for open-source tools to bridge auditors and statisticians. Recent work explores strategic decision-making frameworks that align business goals with sustainability and ethical practices. Earlier contributions include foundational research on computational methods for orbit calculations and Fekete point configurations. Prof. de Swart supervises students in their master’s thesis projects, fostering the next generation of analytical thinkers. His work reflects ongoing engagement with industry partnerships and data-driven innovation. He also chairs the Steering Group for Statistical Audit and the Watersportpark Ouderkerkerplas Foundation, advocating for accessibility in water sports for people with disabilities.

Eric Nalisnickمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +۵ مورد دیگر

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.

Remco Renkenمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Functional MRI
• Parkinson's Disease
• +۳ مورد دیگر

Dr. Remco Renken is a neuroimaging researcher at the University of Groningen's Faculty of Medical Sciences , specializing in advanced MRI and PET scan analysis. His work bridges clinical neuroscience and computational methods , with a focus on movement disorders, visual processing, and neurodegenerative diseases. Department of Radiology and Nuclear Medicine (UMCG) Clinical Cognitive Neuropsychiatry Research Program (CCNP) Perceptual and Cognitive Neuroscience (PCN) group Research Interests : Using fMRI , PET , and machine learning to study brain connectivity , visual cortex plasticity , and neurodegenerative patterns in Parkinson’s, multiple sclerosis, and glaucoma. His innovations include algorithms for 3D motion perception tracking and neuroimaging harmonization . Notable Contributions : Co-developed IRMA (Machine learning harmonization for multicenter PET scans) Co-inventor of a 3D motion perception evaluation system Co-supervisor for PhD research on auditory cortex responses and schizophrenia hallucinations Collaborations span neurology, psychiatry, and biomedical engineering, with datasets shared via Mendeley and DataverseNL.

• Artificial Intelligence
• Logic
• Human-centered Artificial Intelligence
• +۲ مورد دیگر

Dr. Natasha Alechina is an Associate Professor at Utrecht University's Faculty of Science , specializing in the Intelligent Systems subdomain. Her research focuses on multi-agent systems, formal verification, and logic-based AI, with particular emphasis on resource constraints and human-centered applications. Research Themes : Specification and verification of multi-agent systems Resource-bounded reasoning in coalitions Temporal and probabilistic logic for AI Causal modeling in autonomous agents Article Trends show consistent contributions to multi-agent systems , formal methods , and responsible AI . Her 2023-2025 work explores causal dependencies, intention progression under uncertainty, and ethical frameworks for group responsibility. Contact : n.a.alechina@uu.nl at Buys Ballot Building, Utrecht.