جست‌وجوی استاد و پژوهشگر

• Astronomy & Astrophysics
• Gravitational Lensing
• Machine Learning
• +۳ مورد دیگر

Dr. Jelte de Jong is a researcher and Head of the Computer Group at the Kapteyn Astronomical Institute, part of the Faculty of Science and Engineering at the University of Groningen. His work focuses on gravitational lensing analysis, machine learning applications in astronomy, and large-scale survey data processing. He leads computational efforts for projects like the Kilo-Degree Survey (KiDS) and contributes to datasets such as KiDS+VIKING-450 and KiDS-VIKING-450 cosmic shear. His research interests include weak lensing, galaxy evolution, and cosmological parameter estimation through multi-probe analysis. Key contributions include developing automated lens detection algorithms using Deep Learning (e.g., DenseLens) and analyzing galaxy halo properties using weak lensing data. He has published extensively in journals like Monthly Notices of the Royal Astronomical Society and Astronomy & Astrophysics , with a focus on cosmology and astrophysical data analysis. His work often involves collaborations on international surveys and contributes to the Euclid mission's preparatory efforts. Dr. de Jong's datasets and methodologies are widely cited in cosmological studies, emphasizing his role in bridging computational methods with observational astronomy. He holds an academic position at the Kapteyn Institute and actively participates in the astronomy community through research and institutional roles.

• Trauma Surgery
• Orthopedic Trauma Surgery
• Medical Education
• +۳ مورد دیگر

Dr. Patrick Nieboer is a Clinical Professor and trauma surgeon at the Faculty of Medical Sciences, University of Groningen. His primary affiliation is within the Department of Surgery, focusing on trauma and orthopedic trauma surgery. He holds a PhD in Medical Education from the University of Groningen (2023), emphasizing surgical teaching methodologies in operating room environments. His work bridges clinical practice with educational innovation, particularly in resident training and intraoperative learning strategies. Research interests include surgical education frameworks, entrusted autonomy models for residents, and the application of machine learning in trauma patient prognostication. He has contributed to validation studies of AI-driven diagnostic tools for fractures and surgical outcome prediction systems. His expertise extends to optimizing fluoroscopic protocols in fracture management, as evidenced by his corrections to tibia nailing procedures. Collaborations include the NOTSS-lab (Netherlands Orthopaedic and Trauma Surgery Society) and international networks like the Traumaplatform 3D Consortium. His educational focus is reflected in publications analyzing supervisory strategies, teaching interactions, and resident-perception dynamics in surgical settings.

• Radiotherapy
• Implementation Science
• Healthcare Quality Improvement
• +۳ مورد دیگر

Paul Cremers is a Researcher at Tilburg University's Department of Strategy & Entrepreneurship within the Tilburg School of Economics and Management. His work focuses on healthcare innovation implementation, particularly in radiotherapy, blending strategy development with practical application. He holds a hospitality contract, indicating active research and academic engagement. Research Interests: Cremers specializes in Implementation Science applied to radiotherapy advancements, including AI integration, digital transformation impacts, and patient participation strategies. His work emphasizes translating research into clinical practice through structured methodologies and protocols. Co-developed a research-based implementation protocol for radiotherapy innovations (2025) Explored AI adoption challenges and strategies in radiation therapy (2024) Examined digital tools' effects on radiation therapists' roles (2025) Awards: No specific honors or fellowships are listed in the provided text. Grants/Advising: No explicit grants or student advising details are provided, though his collaborative research suggests involvement in multidisciplinary projects. Labs/Teams: Engaged in cross-institutional collaborations with radiation oncology clinics and research groups focused on healthcare quality improvement and innovation adoption.

Tanja Vosمشاهده پروفایل

استاد

• Software Testing
• Automated Testing
• Behavior-Driven Development (BDD)
• +۳ مورد دیگر

Tanja Vos is a Full Professor at the Faculty of Science , Department of Computer Science , specializing in Software Testing , Automated Testing , and Behavior-Driven Development (BDD) . Her work extends to Gamification in Education and Artificial Intelligence (AI) in Learning Systems . Research Program: Towards High-quality and Intelligent Systems (THIS) Active in Graphical User Interface (GUI) Analysis and Test Automation Research Interests : Vos focuses on advancing software testing methodologies through AI-driven solutions , particularly in 3D game testing , chatbot applications , and gamified educational platforms . Her work bridges theoretical research with industrial implementation . Recent Article Trends : Her publications emphasize automated testing frameworks (e.g., iv4XR ), GUI regression analysis , and AI chatbots for personalized learning . Subfields include evolutionary algorithms , dynamic interface testing , and gamification mechanics . Supervision & Collaboration : Vos leads projects like TESTOMAT and AI Study Coach , collaborating with institutions such as Universidad Politecnica de Valencia .

Syed Hassan Shahمشاهده پروفایل

استاد مدعو

• Wireless Networks
• Cyber-Physical Systems
• Smart Cities
• +۷ مورد دیگر

Dr. Syed Hassan Shah is a distinguished adjunct faculty member at California State University, Fullerton's Department of Computer Science, where he teaches graduate-level courses. Concurrently, he serves as a Wi-Fi connectivity subject matter expert and Product Director for Short Range Technologies at Quectel Inc., and previously held roles as Product Manager for Mobile & Compute Connectivity at Qualcomm Inc., focusing on Mi-Fi, CPE, and UWB technologies. Ph.D. in Computer Science & Engineering, Kyungpook National University, South Korea (2013-2017) BS in Computer Science, Kohat University of Science & Technology, Pakistan (2007-2012) Dr. Shah's research spans interdisciplinary domains in Wireless Communications , Cyber-Physical Systems , and Smart Cities , with significant contributions to Vehicular Networks , Internet of Things , and Future Internet Architectures . His recent publications demonstrate expertise in Neural Networks , Optimization Theory , and Security & Privacy applications. Scientific recognition includes: Qualcomm Innovation Award (2016) IEEE Senior Member (2018) ACM Distinguished Speaker (2018) Best Research Contribution Award (Brain Korea, 2016) Multiple travel grants from ACM and IEEE KNU Honors Scholarship (2013) Dr. Shah actively contributes to academic governance as: Editorial Board Member for 60+ special issues in top-ranked journals TPC Member for 100+ international conferences including IEEE Globecom and ACM MobiHoc IEEE Vehicular Technology Society Board appointee (2018-2019)

• Mass Spectrometry
• Food Safety
• Analytical Chemistry
• +۴ مورد دیگر

Marco Blokland is a researcher at Wageningen University & Research, specializing in mass spectrometry and analytical chemistry for food and feed safety. He leads projects focused on detecting veterinary drugs, growth promotors, and pesticides using advanced techniques like LC-HRMS, GC-HRMS, and ambient ionization methods. His research spans food authenticity, contaminant screening, and endocrine disruption studies. Recent work includes on-site analysis of bovine urine for steroids, rapid antibiotic residue detection in livestock, and pesticide exposure assessment via metabolite analysis in urine and diets. He also contributes to method validation and software tool development for automated data processing. Marco collaborates in workshops and conferences, emphasizing innovative sampling strategies and portable mass spectrometry. His projects include EU-level regulatory frameworks, environmental impact studies of veterinary pharmaceuticals, and biomarker discovery for hormone abuse. He has co-authored over 97 research outputs and participated in 8 activities, including oral presentations and workshops on analytical methodologies.

Juseong Leeمشاهده پروفایل

استادیار

• Predictive Maintenance
• Industrial Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Juseong Lee is an Assistant Professor at the Eindhoven University of Technology (TU/e), affiliated with the Department of Industrial Engineering and Innovation Sciences. His research focuses on integrating data-driven models and artificial intelligence into industrial processes, with a specific emphasis on predictive maintenance approaches for engineering systems. PhD in Aerospace Engineering from Delft University of Technology (2022) MSc and BSc in Aerospace Engineering from Korea Advanced Institute of Science and Technology (2018, 2016) His work employs methodologies such as digital twins, multi-objective optimization, and deep learning to enable real-time decision-making in business contexts. He aims to improve the reliability, efficiency, and sustainability of Industry 4.0 systems through his research. Recent publications highlight his expertise in predictive maintenance, with applications to aircraft systems, landing gear brakes, and turbofan engines. Key techniques include Gaussian process learning, adaptive sampling, deep reinforcement learning, and stochastic Petri nets. These studies often involve collaborations with researchers like Mihaela Mitici. Lee's research is supported by the European Union's Horizon 2020 program (Grant No. 769288). He teaches courses on quality and reliability engineering, maintenance optimization, and service logistics.

• Process Mining
• Process Modelling
• Conceptual Modelling
• +۵ مورد دیگر

Jan Martijn van der Werf serves as Associate Professor in Process Science at Utrecht University's Faculty of Science, Department of Information and Computer Science. Since September 2022, he has held the position of Programme Director for the Bachelor Information Sciences, overseeing curriculum development and academic operations for both Business Informatics and Information Sciences programs. Education: Dual PhD in Computer Science from Eindhoven University of Technology and Humboldt Universität zu Berlin (Thesis: 'Compositional Design and Verification of Component-based Information Systems') Research Focus: Van der Werf's work centers on process mining and behavioral modeling in software architectures, with emphasis on the interplay between data and processes. His expertise spans Conceptual Modelling of Information Systems , Enterprise Architecture , and Service-Oriented Architecture , addressing challenges in process discovery, verification, and practical implementation within complex organizational contexts. Current research explores the human dimensions of process mining adoption and AI-driven event log extraction. Publication Trends: Recent work (2023-2024) reveals increasing focus on methodological rigor in process discovery, human factors in process mining initiatives, and formal verification techniques for Petri nets. His publications bridge theoretical foundations with practical applications, particularly in event log extraction using large language models and visualization of complex process chronologies from heterogeneous data sources. Academic Contributions: Van der Werf teaches core courses in Process Modelling and Software Architecture , actively participates in academic workshops (including the 2020 'Information System Modeling' workshop), and supervises graduate research. His Scopus profile indicates 77 research outputs and supervision of 3 students, reflecting sustained scholarly engagement in process science and information systems.

• Structural Equation Modeling
• Bayesian Statistics
• Adolescent Mental Health
• +۳ مورد دیگر

Dr. G Smid is a Researcher in the Department of Psychology within the Faculty of Social and Behavioural Sciences at Utrecht University, specializing in Clinical Psychology. His expertise centers on advanced statistical methodologies applied to psychological research, with particular emphasis on structural equation modeling under constrained data conditions. His primary research domains include Structural Equation Modeling, Bayesian Statistics, Adolescent Mental Health, Clinical Psychology, Data Synthesis, and Systematic Reviews. He has pioneered critical methodological frameworks for Bayesian SEM applications with small samples, developed interpretable synthetic data techniques, and investigated adolescent mental health determinants including harmful sexual behavior and immigration-related impacts. His work bridges complex statistical theory with clinical psychology applications. Analysis of Dr. Smid's 13 publications (2014-2023) reveals a dominant methodological trajectory focused on overcoming small-sample limitations in psychological research. His most influential contributions address Bayesian prior specification in SEM, measurement equivalence validation, and adolescent mental health risk factors. The publications demonstrate consistent innovation in statistical methodology while maintaining strong clinical relevance, particularly in adolescent psychology and mental health assessment.

• Bayesian Statistics
• Artificial Intelligence
• Natural Language Processing
• +۷ مورد دیگر

Rens van de Schoot is a full professor of Collaborative Methods in AI and Data Science at Utrecht University in the Netherlands and serves as an extra-ordinary professor at North-West University in South Africa. He directs the AI-LAB on AI-aided Knowledge Discovery and coordinates the open-source ASReview project (Active learning for systematic text reviewing), which has become a significant tool for researchers conducting systematic reviews across various disciplines. His research spans multiple domains with a strong focus on Bayesian statistics, artificial intelligence applications in systematic reviews, and psychological methodology. Van de Schoot's work bridges technical AI development with practical applications in social sciences and healthcare research, particularly in PTSD and mental health studies. His methodological contributions include significant work on measurement invariance, small sample size solutions, and the development of the WAMBS checklist for Bayesian statistics. The analysis of his recent publications reveals a clear trajectory toward increasingly sophisticated AI applications in systematic reviewing, with a strong emphasis on Bayesian methodology. His work demonstrates how machine learning can enhance traditional systematic review processes while maintaining methodological rigor. The interdisciplinary nature of his research connects statistics, computer science, psychology, and healthcare research in innovative ways. APA award for best dissertation of division 5 Recipient of prestigious VENI and VIDI research grants from the Netherlands Organization for Scientific Research Member of the Young Academy (Jonge Akademie) of the Royal Netherlands Academy of Arts and Sciences (KNAW) Elected member of the Society of Multivariate Experimental Psychology (SMEP) Van de Schoot has supervised numerous PhD candidates, as evidenced by his research on PhD delays and academic careers. His research program has been supported by multiple competitive grants, including his VENI project on integrating trauma-related background knowledge into statistical models and his VIDI project on expert knowledge and limited data. His work bridges theoretical methodological advances with practical applications across multiple domains. As director of the AI-LAB, he leads a team developing cutting-edge AI tools for knowledge discovery. His coordination of the ASReview project has created an international community of researchers applying active learning to systematic reviewing. He's also actively involved with the Utrecht Platform for Applied Data Science, fostering interdisciplinary collaboration on data-intensive research projects.

Bella Struminskayaمشاهده پروفایل

دانشیار

• Survey Methodology
• Smartphone Surveys
• Data Donation
• +۶ مورد دیگر

Bella Struminskaya is an Associate Professor at Utrecht University’s Department of Methodology & Statistics, Faculty of Social and Behavioural Sciences. She is also an affiliated researcher at Statistics Netherlands and previously held a senior researcher role at GESIS - Leibniz Institute for the Social Sciences. Her work bridges survey methodology with digital data collection techniques, focusing on smartphone sensors and passive data donation. Education: PhD in Survey Methodology, Utrecht University M.A. in Sociology, University of Mannheim B.A. in Sociology, Novosibirsk State University Her research explores innovative data collection methods, including smartphone surveys, mixed-mode designs, and ethical frameworks for data donation. She investigates how mobile technologies can reduce survey burden while addressing nonresponse bias and measurement errors. Recent work emphasizes integrating digital trace data with traditional surveys to enhance accuracy and scope. Key article trends highlight applications of smartphone sensors in health studies, GDPR compliance for data access, and methodological advancements in smart surveys. Her contributions span technical tools like the Port software and theoretical insights on panel conditioning and ethical data augmentation. Scientific Awards: GOR Thesis Award 2015 She serves as a board member for the German Society for Online Research, Program Chair for the General Online Research Conference (GOR), and holds advisory roles in organizations like the European Social Survey, SHARE ERIC, and ODISSEI. She is an associate editor for multiple journals, including the Journal of Survey Statistics and Methodology and Survey Research Methods.

C. (Chrysa) Papagianniمشاهده پروفایل

استادیار

• Network Softwarization
• Software-Defined Networking
• Network Slicing
• +۷ مورد دیگر

Dr. C. (Chrysa) Papagianni is an Assistant Professor at the Informatics Institute within the Faculty of Science, Mathematics and Computer Science at the University of Amsterdam . She holds a Ph.D. in Electrical and Computer Engineering from the National Technical University of Athens and has held academic and research positions at institutions including the University of Maryland, Hellenic Open University, and Nokia Bell Labs (Belgium). Ph.D., Electrical and Computer Engineering, NTUA (2009) M.Sc., Information Systems (2017-2019), Hellenic Open University Her research focuses on network softwarization , SDN , network slicing , and AI/ML-driven network intelligence for next-generation networks. Recent work explores machine learning for resource allocation , programmable data planes , and intent-based networking . Her publications span journals like IEEE Transactions on Network and Service Management and conferences including IEEE ICC and IEEE CoNEXT . Key projects include NSF FABRIC , H2020 FED4FIRE+ , and 5Growth platform development. She contributes to technical standards in edge computing and cloud-native networks via 5GPPP white papers.

• Machine Learning
• Artificial Intelligence
• Ethics
• +۷ مورد دیگر

Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.

• Transport Policy
• Machine Learning
• Behavioral Economics
• +۵ مورد دیگر

C.G. Chorus is a Professor at the Faculty of Industrial Design Engineering at Delft University of Technology (TU Delft). His work bridges transport policy , machine learning , and behavioral economics , focusing on decision-making, moral psychology, and mobility systems. Key Research Areas : Transport modeling, participatory value evaluation, AI ethics, and behavioral decision theory. Collaborations : Active in interdisciplinary projects with institutions like 4TU.ResearchData and editorial roles in journals such as European Journal of Transport and Infrastructure Research . Recent publications highlight applications of random forests and association rules to analyze complex choice experiments, alongside investigations into moral foundations in judicial contexts and system safety in public-sector AI. His work often integrates data science and policy analysis to address real-world challenges in infrastructure and mobility. Scientific Awards 2008 IEEE Intelligent Transportation Systems Society Best PhD Dissertation Award BIVEC-GIBET PhD Award (2007) Honourable Mention for Best Transportation Science and Logistics Paper (2007) Traveler Response to Information Prize (2009) Chorus contributes to open-access datasets and software, including travel behavior studies and moral decision-making experiments. His editorial and conference activities underscore his leadership in advancing transport and infrastructure research.

• Social Cognition
• Facial Emotion Recognition
• Mental Health Interventions
• +۲ مورد دیگر

Dr. Barbara Montagne is a Lecturer in the Faculty of Social and Behavioural Sciences at Utrecht University , affiliated with the Experimental Psychology department. Her work bridges psychology and computer science, focusing on facial emotion recognition, social cognition, and digital mental health interventions. Her research spans clinical populations (stroke survivors, OCD, psychosis, Korsakoff syndrome) and tech-driven applications (LLMs in mHealth, gamified systems). Recent studies examine LLM-generated treatment goals, social touch deficits, and crisis dynamics modeling. She collaborates with institutions like the Helmholtz Institute and ACM conferences. Doctoral thesis on facial expressions in psychiatric/neurological disorders (2005) Key areas: emotion perception, mental health technology, social cognition Her 2024-2025 work highlights mHealth gamification, personalized psychiatry, and trauma impacts on borderline disorder. Despite no listed awards, her 29+ peer-reviewed outputs and Mendeley readers indicate significant academic impact.