جستجوی استاد راهنمای دانشگاهی

Dr. Irfan Ahmad serves as an Associate Professor in the Department of Information and Computer Science at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, where he teaches undergraduate and graduate courses in Computer Science and Software Engineering while conducting research and advising graduate students. His academic service includes committee roles on graduate studies, program development, and competitions. His research expertise centers on Pattern Recognition with specialized focus on Document Image Analysis , Handwriting Recognition , and Machine-Printed Text Recognition . He actively explores Machine Learning applications including Deep Learning and Natural Language Processing , with significant contributions across Artificial Intelligence, Computer Vision, Data Mining, Neural Networks, and Computational Linguistics as evidenced by his PeerJ subject area specializations. Recent publications reveal a strategic emphasis on adaptive deep learning architectures for document analysis, particularly generative methods for handwritten text recognition and knowledge distillation techniques. His editorial work on feature extraction and multilingual fake news detection further demonstrates applied research bridging theoretical machine learning with real-world language processing challenges. As an active Academic Editor for PeerJ Computer Science with 1,205 contribution points, Dr. Ahmad provides substantial service to the scholarly community through manuscript evaluation and editorial oversight across emerging technologies in data science and artificial intelligence.

• Pattern Recognition
• Document Image Analysis
• Handwriting Recognition
• +۷ مورد دیگر

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +۷ مورد دیگر

Ewa Miedzobrodzka is a postdoctoral researcher at Utrecht University's Faculty of Social and Behavioural Sciences , specializing in the JEDi consortium project (Youth and Digitalization). She holds a PhD in Communication Science from Vrije Universiteit Amsterdam (2023) and has held academic roles at multiple institutions. Current position: Researcher at Utrecht University (2023-present) Previous roles: Vrije Universiteit Amsterdam (2013-2023), University of Amsterdam (2020-2022) Research Focus: Investigates digital media's impact on adolescent development through behavioral, neuroimaging (ERP), and longitudinal studies. Key areas include: Violent video game effects on empathy and social cognition Digital inequality during pandemic education Self-regulated learning in digital environments Cross-contextual media landscape analysis Academic Contributions: Published 13+ research outputs (journal articles, book chapters, reports) with international collaborators. Active in academic conferences and media outreach. Teaching & Supervision: Supervised 13+ Master's students across institutions, taught courses on media psychology and assessment methods, and contributed to systematic reviews on pandemic educational impacts.

• Media Psychology
• Adolescent Development
• Digital Media
• +۵ مورد دیگر

Maria Peeters is an Associate Professor at Utrecht University's Faculty of Social and Behavioural Sciences, Department of Social, Health and Organisational Psychology. Since 2017, she has also held a part-time full Professor position at Eindhoven University of Technology. Her research focuses on sustainable employee performance in changing work environments, with particular attention to technological innovations and workplace diversity. She has published extensively on work psychology topics, with over 178 research outputs spanning her career. Dr. Peeters' research interests center around work-related well-being, motivation, performance, engagement, job crafting, work-life balance, implementation of technological innovations, and the 'three M's' (Menstruation, Motherhood, and Menopause). Her work examines how these factors influence employee motivation and performance in contemporary organizations. She has made significant contributions to understanding how human-centered work design is crucial for successful technological implementations. Her recent publications demonstrate a clear trend toward examining the intersection of technological change and employee well-being, with increasing attention to gender-specific issues in the workplace. The research shows a progression from traditional work stress studies toward more nuanced examinations of sustainable performance in digital work environments, with growing emphasis on how biological factors affect women's work experiences. Maria Peeters has contributed significantly to the field through her publications, including the second edition of 'An Introduction to Contemporary Work Psychology' (2024) which serves as a key textbook for advanced undergraduate and graduate students. Her research spans multiple domains including occupational health psychology, work-family interactions, and the impact of technological innovations on work quality. Throughout her career, Professor Peeters has maintained active collaborations with researchers across Europe and internationally, contributing to numerous multi-country studies on work psychology topics. Her research has practical applications for organizations seeking to implement sustainable work practices and improve employee well-being in rapidly changing work environments.

• Work stress
• Motivation
• Performance
• +۸ مورد دیگر

R.H. Bemthuis serves as an Assistant Professor in the Pervasive Systems Research Group at the University of Twente, affiliated with the Department of Computer Science within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Education: Ph.D. in Electrical Engineering, Mathematics, and Computer Science from University of Twente Master's in Industrial Engineering and Management (specialization: production and logistics management) from University of Twente Bachelor's in Industrial Engineering and Management (specialization: production and logistics management) from University of Twente Research Focus: Bemthuis develops IoT- and Big Data-driven solutions for emerging behavior management, with expertise spanning multi-agent systems, logistics simulation, and applied process mining. His work integrates real-world applications through projects like ECOLOGIC (sustainable construction logistics) and ECOCHECK, emphasizing IoT-based approaches to complex logistical challenges. Leadership & Service: As principal investigator for major research projects, Bemthuis directs significant grant-funded initiatives. He demonstrates institutional commitment through extensive service: serving as Treasurer and Vice-President of the PhD Network (P-NUT) from 2019-2022, and co-chairing annual PhD Day events throughout this period.

• Internet of Things (IoT)
• Big Data
• Multi-agent systems
• +۳ مورد دیگر

Prof. Dr. ir. Hans Heesterbeek is a full Professor of Theoretical Epidemiology at Utrecht University's Faculty of Veterinary Medicine. He holds a dual appointment in the Department of Farm Animal Health within the Population Health Sciences sector. His research focuses on the dynamics of infectious diseases at the intersection of biology, medicine, and mathematics, with emphasis on wildlife epidemiology and complex systems. He chairs Utrecht University's Committee for Research Integrity and has held leadership roles in departments like Animals in Science and Society. Education: MSc Phytopathology (Wageningen University, 1986), MSc Mathematics (University of Amsterdam, 1988), PhD in Mathematical Biology from Leiden University (1992) Affiliations: Editor-in-Chief of Epidemics journal, Senior Editor at Proceedings of the Royal Society B , Chair of ZonMw's Open Competition grant program Research interests include: Mathematical modeling of disease spread Ecological epidemiology of zoonotic pathogens Role of wildlife in disease transmission Historical analysis of epidemic understanding Key contributions: Co-developer of the R₀ reproduction number framework, foundational to global epidemiological modeling. Active in interdisciplinary work linking theoretical ecology to societal systems. Currently writing a book on the history of epidemic science. Grants: Vici laureate (€1.3M, 2005-2010). Organizes biannual Epidemics conferences. Advises on public health policy during outbreaks like the 2020 COVID-19 pandemic.

• Infectious Diseases
• Theoretical Epidemiology
• Mathematical Modelling
• +۳ مورد دیگر

Remco M. Dijkman serves as Full Professor in Information Systems at Eindhoven University of Technology (TU/e), chairing the Information Systems group within the Industrial Engineering and Innovation Sciences school. He additionally holds a Full Professor position at EAISI High Tech Systems and acts as research director for high-tech supply chains at the European Supply Chain Forum—a network of over 50 multinational companies. His research centers on Business Process Management with emphasis on data-driven optimization of business processes. His academic background includes both PhD and Master's degrees in Computer Science from the University of Twente. Publications span Information Systems, Computers in Industry, and Transactions on Software Engineering and Methodology, with over 100 papers and service on the editorial board of Information Systems. He has held visiting positions at New York University, Hasso Plattner Institute, IBM Zurich Research Lab, Humboldt-University Berlin, and Queensland University of Technology. Dijkman's research interests focus on detecting, diagnosing, and predicting optimal execution scenarios in business processes, developing mathematical models for quantitative process analysis , and resource assignment optimization . These are primarily applied in transportation logistics and high-tech supply chains, where he investigates data-driven predictions for transport order assignment and supply chain planning. His work bridges artificial intelligence with practical business applications. Recent publications (2024-2025) reveal concentrated efforts in deep reinforcement learning for resource allocation, process pattern discovery, and software library development (GymPN, SimPN). Key trends include predictive process monitoring for healthcare applications, event data enrichment frameworks, and uncertainty handling in logistics planning—demonstrating strong interdisciplinary integration. Scientific recognition includes: Best Demo Award (2019) Best Reviewer Award (2016) Test of Time Award (2019) He has supervised 150 students, including Lotte Vugs who received the Dow Chemical Best OML Master Thesis Award in 2020. Grant leadership spans eight projects: NXTGEN Smart Industry (2023-2030), CollChain (2023-2029), CERTIF-AI (2020-2025), FENIX (2019-2023), and DynaPlex (2021-2024), focusing on digital twins, federated networks, and AI-driven supply chain solutions. Dijkman directs the Information Systems group at TU/e and leads the European Supply Chain Forum's high-tech supply chain research. His work integrates with semiconductor manufacturing and transportation logistics through collaborations with industry partners, while his 2023 invited talks at Technical University of Munich and Humboldt University Berlin highlight his international engagement.

• Business Process Management
• Data-Driven Process Optimization
• Supply Chain Management
• +۳ مورد دیگر

Gonzalo Nápoles is an Assistant Professor at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. He holds a Doctoral Degree in Computer Science from Rough Cognitive Networks (2014–2017). His research focuses on AI applications in cognitive modeling, pattern classification, and neural networks with interdisciplinary applications in healthcare, finance, and social sciences. Key research areas include Fuzzy Cognitive Maps, data augmentation techniques for neuroimaging, and interpretable machine learning systems. He actively contributes to UN Sustainable Development Goals related to education and innovation. Recent work explores AI ethics, financial risk assessment using dynamic networks, and sensory processing disorder analysis through neural networks. Education: Doctoral Degree in Computer Science, 2017 (Thesis: Rough Cognitive Networks) Prize-winning research includes Best Paper Awards at CIARP 2021 and IWAIPR 2023. He collaborates internationally, hosting academic visitors and serving on multiple PhD committees. Current projects involve stock prediction using graph neural networks and fMRI data augmentation methodologies. Awards: Best Paper Award - CIARP 2021 Best Paper Award - IWAIPR 2023 Nápoles advises on PhD theses in cognitive science and AI applications. His work bridges theoretical advancements with practical implementations in healthcare, finance, and urban systems.

• Artificial Intelligence
• Machine Learning
• Cognitive Science
• +۵ مورد دیگر

Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants

• Learning Analytics
• Human-Technology Interaction
• Artificial Intelligence
• +۶ مورد دیگر

Oktay Türetken is a Full Professor at Eindhoven University of Technology (TU/e) in the Industrial Engineering and Innovation Sciences school, specializing in Information Systems . He chairs the Industrial Engineering Bachelor Program and contributes to the European Research Center for Information Systems (ERCIS) in Münster, Germany. PhD in Information Systems Research Fellow at European Research Institute in Service Science (2008-2012) Adjunct Lecturer at METU’s Software Management program (pre-2008) His research focuses on digital innovation and collaborative business models , particularly in urban mobility. He develops methods for operational design , business process management , and data analytics maturity models . Key projects include MobilitEU and EU-funded initiatives in smart mobility. Recent publications address KPI frameworks for business models, platform design for shared mobility, and maturity models for organizational capabilities. He leads multi-disciplinary teams and has co-authored over 129 research outputs, including 2025 articles in Production Planning & Control and Business & Information Systems Engineering . 2021 : 1st Prize at DESRIST for paper prototype 2022 : 1st Prize for MaaS Platform Features research He supervises PhD candidates Ginger Korsten , Yasmin Rettab , and Stella Lo Giudice , and teaches courses like Digitalization and Enterprise Systems and Green and Digital Transformation . His work bridges academic research with industry consultancy via Cogniton Business Engineering Consultancy.

• Business Process Management
• Digital Business Models
• Smart Mobility Solutions
• +۳ مورد دیگر

Anna Wilbik is a Professor in Data Fusion and Intelligent Interaction at the Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University (The Netherlands). Her research bridges data understanding and human-machine synergy in complex systems, focusing on multi-criteria decision making, explainable AI, and data fusion techniques. PhD in Computer Science (with honors), Systems Research Institute, Polish Academy of Science (2010) Postdoctoral Fellow, University of Missouri (2011) Stanford University TOP500 Innovators Program Alumnus Research Pillars: Intelligent human-machine interaction for joint decision making Data fusion methods for heterogeneous data integration Contextualized multi-criteria decision frameworks Fuzzy logic and linguistic summaries for explainability Federated learning systems Article Trends: Recent work focuses on intuitionistic fuzzy sets for knowledge-intensive processes, federated learning with uncertainty handling, and linguistic summarization techniques for interpretable AI. She actively explores explainability , collaborative business models , and driver behavior analysis through attention-based models. Professional Leadership: Vice-chair of IEEE Fuzzy Systems Technical Committee Organizer of IEEE World Congress on Computational Intelligence (2024)

• Data Fusion
• Intelligent Interaction
• Fuzzy Logic
• +۴ مورد دیگر

Robert Pollice is a Lecturer at the Faculty of Science and Engineering , University of Groningen , specializing in Homogeneous Catalysis . His research integrates computational chemistry , machine learning , and automated experimentation to accelerate molecular design and catalyst development . Research Interests focus on homogeneous catalysis , quantum chemistry , and machine learning applications. His work addresses challenges in reaction mechanism modeling , noncovalent interactions , and inverse molecular design , leveraging closed-loop optimization and large language models for chemical data analysis . Publications span quantum chemical simulations , solvation energy calculations , excited state engineering , and automated catalyst discovery . His recent studies explore inverted singlet-triplet gaps , machine learning for reaction modeling , and SELFIES for molecular string representations . Peer-review Contributions include evaluations for journals like Organic Process Research & Development , Materials Advances , and Chem , reflecting his expertise in catalysis , quantum chemistry , and AI-driven chemical discovery .

• Computational Chemistry
• Quantum Chemistry
• Machine Learning
• +۶ مورد دیگر

Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.

• Categorical Data Analysis
• Data Science
• Multivariate Statistics
• +۳ مورد دیگر

Dr. Damian Zaitch is an Associate Professor at the Willem Pompe Institute for Criminal Law and Criminology, School of Law, Utrecht University. He has been with Utrecht University since 2009, having previously taught at the Department of Criminology at Erasmus University Rotterdam from 2000 to 2009. His academic work spans three interconnected research domains: illegal drug trafficking and policies, cross-border organized and corporate crime, and green criminology. Zaitch's research interests include Corporate Crime, Drug Trafficking and Policies, Organized Crime, Latin America, and Environmental Criminology. His methodological expertise lies in qualitative research methods, particularly virtual ethnography and visual criminology. He has conducted extensive fieldwork on drug trafficking through the port of Rotterdam, synthetic drug markets in Amsterdam, and alternative drug policies in Latin America, with a focus on harm reduction approaches. His recent publications demonstrate a strong focus on green criminology, particularly examining environmental crimes related to resource extraction in Latin America, as well as continuing work on transnational organized crime networks. His research often examines the intersection of state and corporate power in creating environmental harms and human rights violations. Willem Nagel Prize (2003) for his PhD research Founding and board member of CIROC (Center for Information and Research on Organized Crime) Coordinator of the Erasmus Mundus Doctorate in Cultural and Global Criminology Coordinator of the Utrecht PhD Summer School on Advanced Qualitative and Legal Methods in Criminology Zaitch teaches courses including International Organized Crime, Crimes of the Powerful, and Green Criminology at both undergraduate and graduate levels. He co-supervises numerous PhD projects on topics ranging from Nigerian cocaine trafficking networks to environmental rights violations by multinational corporations. His research is conducted within Utrecht University's strategic themes of Institutions for Open Societies and Pathways to Sustainability.

• Corporate Crime
• Drug Trafficking
• Organized Crime
• +۶ مورد دیگر

Maarten van Steen is a Professor active in the fields of Distributed Systems , Artificial Intelligence , and Cybersecurity . With an h-index of 35 and over 5,400 citations, his work focuses on Edge AI , Privacy Preservation , and WiFi-Based Sensing . His research emphasizes non-intrusive authentication, anonymization techniques, and crowd monitoring without compromising individual privacy. Key research areas: Distributed Systems, Privacy Preservation, WiFi Security Recent projects: RoomKey, LocKey, FlowPrint Crowd-monitoring applications: Subway travelers, pedestrian dynamics Van Steen's work combines Machine Learning with Homomorphic Encryption to develop privacy-first solutions. He has contributed to mobile app fingerprinting , WiFi authentication , and blockchain scalability challenges. His 2024–2025 publications reveal trends in contextual security , crowd behavior analysis , and automated threat intelligence . Notable methods include Bloom Filters, automata learning, and WiFi beacon frame analysis. Dutch Cyber Security Best Research Paper Award 2024 Runner-up (shared prize) Van Steen supervises research teams and collaborates on datasets like Code for Threat Intelligence Processing and DeepCASE . His work spans 20+ years , with 208 total research outputs and significant contributions to decentralized systems, network traffic analysis, and urban mobility.

• Distributed Systems
• Artificial Intelligence
• Edge AI
• +۴ مورد دیگر