جست‌وجوی استاد و پژوهشگر

Loes Hollesteinمشاهده پروفایل

استادیار

• Dermatology
• Skin Cancer Epidemiology
• Melanoma
• +۶ مورد دیگر

Dr. Loes Hollestein is an Assistant Professor in the Department of Dermatology at Erasmus MC, a prominent medical center in the Netherlands. She also holds an external position as a Researcher at the Netherlands Comprehensive Cancer Organization (IKNL) since June 2015. Her academic work focuses on skin cancer epidemiology and prediction models. Dr. Hollestein's research primarily centers on the epidemiology of skin cancers, with particular expertise in melanoma, cutaneous squamous cell carcinoma, and basal cell carcinoma. Her work investigates the prediction of multiple skin cancers and metastasis, contributing significantly to understanding cancer progression and risk factors. She has developed expertise in cohort analysis, cancer registry research, and actinic keratosis studies, forming a comprehensive approach to skin cancer epidemiology. Her recent publications demonstrate a growing integration of artificial intelligence in dermatological research, particularly for early detection of metastatic cutaneous squamous cell carcinoma and automated assessment of skin histological structures. Dr. Hollestein's work spans population-based studies across the Netherlands, examining skin cancer risks in various patient populations including those with hematological malignancies. Her research portfolio shows a consistent focus on improving detection methods, understanding risk factors, and evaluating treatment approaches for various skin cancers. Dr. Hollestein has received attention for her work, with several publications picked up by news outlets and discussed on social media platforms. Her research outputs have been cited and captured the interest of readers on academic platforms like Mendeley. As a supervisor, Dr. Hollestein has guided four supervised works, contributing to the development of the next generation of researchers in dermatology and oncology. Her collaborative approach is evident in her numerous co-authored publications with researchers across various specialties. Dr. Hollestein's laboratory and research team focus on epidemiological studies of skin cancer, utilizing large population datasets and cancer registries. Her work contributes to Sustainable Development Goals related to good health and well-being, particularly in cancer research and prevention.

Ed Nijssenمشاهده پروفایل

استاد

• Technology Marketing
• Servitization
• Strategic Sales
• +۵ مورد دیگر

Ed Nijssen is a Professor of Technology Marketing at the Eindhoven University of Technology (TU/e) within the Innovation, Technology Entrepreneurship and Marketing (ITEM) group of the School of Industrial Engineering. His research integrates marketing, innovation, and sales strategy, with a focus on servitization and digital transformation in B2B contexts. He has held visiting professorships at Michigan State University, NYU, and Bocconi University. Research interests include: Servitization (solutions-based business models) Marketing/Sales-R&D interface Digital solution selling Brand advocacy in frontline services Product-service systems Scientific contributions span over 143 research outputs, including systematic reviews on B2B branding and effectual entrepreneurial marketing. His awards include multiple Teacher of the Year recognitions and publication prizes. He teaches courses in Entrepreneurial Marketing , Marketing and Innovation , and Validation to Sales , while serving on editorial review boards for Industrial Marketing Management and Journal of Product Innovation Management .

Yingqian Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Reinforcement Learning
• +۷ مورد دیگر

Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.

• Model-Based Design Methods
• Design Automation
• Optimization
• +۵ مورد دیگر

Marc Geilen is an Associate Professor at the Electronic Systems group of Eindhoven University of Technology (TU/e) . He leads the Model-Based Design Lab within the CompSOC Lab and High Tech Systems Center .

Annekoos Schaapمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Annekoos Schaap is a Doctoral Candidate at Eindhoven University of Technology's Department of Electrical Engineering, working within the Electronic Systems research group and Eindhoven MedTech Innovation Center. Supervised by Professor Sveta Zinger and Part-time Professor Danny Ruijters, her research bridges AI-driven medical imaging and clinical applications. Her primary research interests focus on AI-powered medical image processing for healthcare optimization. Key areas include: Development of machine learning algorithms for cerebral aneurysm treatment Radiogenomic analysis linking MRI imaging with genomic/transcriptomic data Workflow automation to reduce cognitive load in medical interventions Non-invasive prostate cancer prognosis systems Recent publications demonstrate her specialization in correlating quantitative imaging features with underlying genomic landscapes, particularly in prostate cancer research. Her work emphasizes clinical usability through reader studies and real-world implementation of AI-assisted procedural guidance systems. Current projects target reducing X-ray exposure and procedural time in minimally invasive interventions while improving diagnostic accuracy. Annekoos is embedded in two key research ecosystems: Electronic Systems Group : Focusing on hardware/software co-design for medical applications Eindhoven MedTech Innovation Center : Translating engineering solutions to clinical practice

Kees Goossensمشاهده پروفایل

استاد

• Embedded Systems
• Real-Time Systems
• Networks-on-Chip
• +۳ مورد دیگر

Kees Goossens is Full Professor of Real-time Embedded Systems in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads the CompSOC Lab focused on predictable and composable embedded systems. His research spans composable virtualization, real-time systems, low-power design, and FPGA-based dynamic partial reconfiguration. Previously at NXP Semiconductors, he pioneered networks-on-chip research including the Aethereal NoC architecture. His research interests include: Composable and predictable embedded systems Real-time networks-on-chip (NoC) Memory management and controllers FPGA reconfiguration Hardware verification Low-power electronic design Publication analysis shows consistent focus on real-time systems, networks-on-chip, memory controllers, and embedded systems design, with recent expansion into machine learning applications for networking and error-correction coding. His work frequently addresses automotive and industrial control applications. Editorial contributions include: ACM TODAES editorial board (since 2009) Associate editor for Springer DAEM journal (since 2006) Guest editor for multiple NoC special issues He leads the CompSOC laboratory developing virtualized execution platforms for mixed-criticality systems. Current educational activities include courses on Systems-on-Chip, Embedded Systems, and Computer Architecture.

Bram van Esمشاهده پروفایل

استادیار

• Medical Informatics
• Cardiac Risk Management
• Machine Learning
• +۸ مورد دیگر

Bram van Es is an Assistant Professor at the University Medical Center Utrecht (UMC Utrecht), where he combines expertise in Medical Informatics and Economic History . His work bridges computational methods with historical analysis, focusing on applications such as machine learning in cardiology , epidemic financial impact , and conflict-induced socioeconomic dynamics . He collaborates across disciplines, contributing to journals like Open Heart and Journal of Cerebral Blood Flow and Metabolism . University : University Medical Center Utrecht Rank : Assistant Professor Email : bes3@umcutrecht.nl His research spans two distinct yet interconnected domains: Medical Informatics : Developing large language models for healthcare, automated text classification in cardiac risk management, and diagnostic data extraction from unstructured clinical reports. Economic History : Analyzing epidemic-induced wealth redistribution , warfare consequences , and institutional dynamics in early modern Europe. He explores themes like resource allocation , state formation , and socioeconomic adaptation during crises. Publications reflect this duality: recent works include machine learning applications in coronary imaging and haematology, alongside historical studies on epidemic impacts and warfare economics. He emphasizes methodological rigor, applying dimensionality reduction to clinical datasets and reproducibility frameworks in historical research.

• Neurophysiology
• Electroencephalography (EEG)
• Epilepsy
• +۳ مورد دیگر

Michel J.A.M. van Putten is a Full Professor of Clinical Neurophysiology at the TechMed Centre, University of Twente , with an h-index of 52 and 9261 Scopus citations. His research focuses on: EEG-based outcome prediction in post-cardiac arrest coma Deep learning applications for seizure and interictal discharge detection Neurophysiological modeling of epilepsy and traumatic brain injury Cultured cortical neural networks for disease mechanism inference AI-driven analysis of brain connectivity and excitation-inhibition balance His work contributes to UN Sustainable Development Goals for Health and Well-being , Human-AI Interaction , and Economic Impact of AI . Recent research includes: 2025: Automated inference of disease mechanisms in patient-derived neuronal networks 2025: Expert-level deep neural network for interictal discharge detection 2024: Grassmann manifold methods for invariant EEG/MEG feature extraction Scientific recognition includes: 2011: NVvTG Congres Prize 2012: Tripartite prize for Near Infrared Spectroscopy and EEG research His datasets on EEG analysis and cortical network modeling are publicly available through Zenodo, and he has presented 17 oral presentations on topics including: Post-ictal brain recovery mechanisms Mathematical models of peripheral axons Ischemic cerebral damage pathways

• Artificial Intelligence
• Data Mining
• Process Mining
• +۵ مورد دیگر

Faiza Allah Bukhsh is an Associate Professor specializing in Artificial Intelligence, Data Mining, Process Mining, Health Informatics, Cybersecurity, and Ethical AI. Her work bridges technical innovation with societal impact, particularly in healthcare systems analysis, telecommunications resilience, and ethical data governance. Digital Society Institute TechMed Centre Datamanagement & Biometrics Her research focuses on Explainable AI , Process Mining , and Privacy Assurance in healthcare systems, with recent work on AI music perception, sepsis treatment analysis, and privacy-utility trade-offs. Key article trends include: AI in music and creative domains Process Mining for healthcare insights Explainable Machine Learning workflows Privacy-preserving analytics Telcom infrastructure resilience

Mahdi Khodadadzadehمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Process Mining
• +۹ مورد دیگر

Mahdi Khodadadzadeh is an Assistant Professor in the Department of Geo-information Processing, specializing in machine learning, data mining, and geospatial analysis. His work bridges traditional and deep learning methods with applications in mineral exploration, environmental modeling, and circular economy initiatives. Research outputs include novel cross-validation techniques for geospatial machine learning (Spatial+, dissimilarity-adaptive methods), hyperspectral mineral mapping for ore characterization, and hybrid models combining artificial neural networks with optimization algorithms. He actively contributes to open-access datasets and methodologies, particularly in drill-core analysis and multi-source data fusion. Collaborations with researchers like R. Zurita-Milla and R. Gloaguen are evident through co-authored publications and shared datasets. His work impacts domains such as mineral exploration, geospatial modeling, and sustainable resource management, with a focus on robust statistical evaluation frameworks and spectral-spatial analysis.

• Artificial Intelligence
• Systems Engineering
• Cyber-Physical Production Systems
• +۵ مورد دیگر

Dr. Saber Darmoul is an Associate Professor specializing in Systems Engineering and Multidisciplinary Design. His research bridges artificial intelligence with industrial applications, focusing on cyber-physical production systems, operational resilience, and smart transportation. He actively explores knowledge representation, reinforcement learning, and agent-based modeling to address complex system challenges. Key Research Interests: Operational resilience, AI in manufacturing, multi-agent systems, smart mobility Technical Expertise: Ontology modeling, simulation platforms, immune-inspired control architectures His recent publications (2025-2019) demonstrate a consistent focus on integrating artificial immune systems into control architectures for transportation and manufacturing. Notable trends include: Development of knowledge-based systems for dynamic reconfiguration Application of multi-criteria decision frameworks in production environments Advancing predictive maintenance strategies through distributed systems Exploring 6G system-of-systems engineering While no explicit awards or student advisement information appears in available records, his 15 most recent publications reflect sustained academic productivity and evolving focus from foundational control systems (2017-2019) to advanced applications in Industry 4.0 (2020-2025).

Sebastian Thiedeمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Smart Industry
• Human-Centered Manufacturing
• +۸ مورد دیگر

Sebastian Thiede is a Full Professor specializing in Manufacturing Systems , with extensive research contributions to Smart Industry , Sustainable Manufacturing , and Human-Centered Production . His work bridges advanced technologies like Machine Learning , Simulation , and Artificial Intelligence with industrial applications, addressing critical challenges in Circular Economy , Energy Efficiency , and Factory Decarbonization . Research focuses on Battery Manufacturing , Edge Computing , and Real-Time Locating Systems (RTLS) . Key methodologies include Digital Twinning , Surrogate Modeling , and Data-Driven Process Optimization . Recent publications highlight trends in Autonomous Sensor Data Analysis (2025), Cyber-Physical Architectures for reconfigurable systems, and Circular Transition Methodologies for manufacturing. His work often integrates Human Factors with Smart Automation , emphasizing environmental and economic impacts.

Stein Ørnمشاهده پروفایل

استاد

• Cardiology
• Heart Failure
• Myocardial Infarction
• +۵ مورد دیگر

Stein Ørn is a Professor of Cardiology at the University of Stavanger and a senior researcher at Stavanger University Hospital's Department of Cardiology. With an MD and PhD, he has established himself as a leading researcher in exercise cardiology and cardiac biomarkers. University of Stavanger - Professor since February 2016 Stavanger University Hospital - Continuous position since 1996 University of Bergen - Associate Professor (2013-2014) University of Oslo - Medical degree (1987-1994) Professor Ørn's research primarily focuses on cardiac responses to exercise, particularly the diagnostic implications of exercise-induced cardiac biomarker elevations. His groundbreaking NEEDED study has demonstrated that patterns of troponin elevation following endurance exercise can help identify individuals with occult coronary artery disease. His work extends to cardiac imaging, where he has developed innovative machine learning approaches for automatic scar assessment in cardiac MRI. His publication record shows consistent output in high-impact cardiology journals, with recent work exploring the relationship between exercise intensity, cardiac biomarkers, and underlying cardiovascular conditions. His research has important clinical implications for athlete screening protocols and understanding the cardiovascular effects of endurance exercise. Professor Ørn leads an active research group focusing on: Cardiac biomarker dynamics during exercise Advanced cardiac imaging techniques Machine learning applications in cardiology Heart failure prognostic markers Risk stratification in athletes His research has received substantial attention in the field, with over 6,600 citations across his 144 publications, demonstrating significant impact on cardiovascular medicine and exercise physiology.

Mark Schuuringمشاهده پروفایل

پژوهشگر

• Cardiology
• eHealth
• Artificial Intelligence
• +۶ مورد دیگر

Mark Schuuring is a Non-Invasive Cardiologist at Medical Spectrum Twente (MST) and a Researcher at the Biomedical Signals and Systems department of the University of Twente. He holds certifications in CMR (level 3), TTE, and TOE, and is an alumnus of the Postgraduate Course in Heart Failure. His research focuses on eHealth and artificial intelligence in cardiology, with a particular emphasis on heart failure and adult congenital heart disease. University of Twente | Biomedical Signals and Systems Medical Spectrum Twente | Non-Invasive Cardiologist His publications span machine learning for cardiac imaging, digital health interventions, and clinical applications of AI in cardiology. Recent work includes automated echocardiography segmentation, ChatGPT evaluation in heart failure care, and 3D printing for mitral valve surgery. His research trends align with integrating AI into clinical workflows and improving patient outcomes through digital solutions. He supervises multiple PhD candidates and has secured grants from organizations including the Netherlands Federation of University Medical Centers and Hartstichting. He founded the online platform digitalcardiology.net to advance digital cardiology education and innovation.

Suhyb Salamaمشاهده پروفایل

دانشیار

• Remote sensing of water quality
• Hydrological and water-resources management
• Radiative transfer modelling
• +۶ مورد دیگر

Suhyb Salama is Associate Professor of Remote Sensing of Water Quality and Water Resources Management at the Department of Water Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, the Netherlands. His research integrates radiative-transfer physics, machine-learning analytics and multi-sensor satellite data to unravel hydrological and water-quality processes from micro-scale spectroscopy to meso-scale land–atmosphere interactions, directly supporting UN Sustainable Development Goals 6 and 14. Education: PhD in Civil Engineering, Katholieke Universiteit Leuven, Belgium (2003) MSc in Hydraulic Engineering (magna cum laude), KU Leuven (1999) BSc in Civil Engineering, Damascus University, Syria (1993) Research interests: Salama’s scholarly work spans remote sensing of biogeophysical variables, spatiotemporal modelling of suspended sediments and algal blooms, radiative-transfer theory across visible to microwave domains, development of smartphone-based water-quality apps, detection of floating plastic litter, and assessment of groundwater–vegetation feedbacks. He combines optical, thermal and microwave observations with physical and data-driven models to deliver actionable information on water scarcity, pollution extremes and climate-change impacts. Recent publication trends (2023-2025): His latest articles focus on machine-learning quantification of estuarine sediment variability, advanced detection of floating plastics, smartphone calibration for citizen-based water-quality monitoring, spectral-textural mining mapping, and analytical derivation of water-clarity time series from Sentinel-2 imagery. Collectively these works push toward operational, open-source solutions for high-frequency, basin-scale water-quality monitoring and UN SDG reporting. Editorial & professional service: Salama serves as topical editor for the journal Remote Sensing (2020-2024) and has chaired sessions on long-term earth-observation of suspended particulate matter. He actively collaborates across Europe, Africa and Asia on projects that merge remote sensing, hydrodynamic modelling and capacity development. Teaching & capacity building: He teaches MSc courses on optical and microwave remote sensing for water resources, supervises MSc and PhD research, and champions problem-based learning that equips students with enterprising skills to tackle complex water and climate challenges.