برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱-۱۵ از ۵۳ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
استادیار
Tom Verhoeff is an Assistant Professor at the Faculty of Mathematics and Computing Science of Eindhoven University of Technology (TU/e) , working within the Software Engineering & Technology group. His research focuses on Model-Driven Engineering (MDE) , Domain-Specific Languages (DSLs) , and the intersection of mathematics, computing, and the arts . He teaches courses in data analytics, programming, algorithms, theoretical computer science , and logic . Verhoeff earned both his MSc and PhD in Technical Science (Mathematics and Computer Science) from TU/e. He is actively involved in promoting mathematics and informatics through initiatives like the annual Bridges conference , and serves as board member and treasurer of the Dutch Mathematics Olympiad , as well as chair of the Koos Verhoeff MathArt foundation . He has also held roles as guest lecturer in Lithuania and Finals Director for the ACM International Collegiate Programming Contest . Research Interests: Verhoeff’s work spans Model-Driven Engineering , domain-specific language development , and 3D geometric modeling . His scholarship often explores symmetry, recursion, and mathematical visualization , particularly through computational art and algorithmic puzzles . Recent publications highlight 3D rotation methods , knot theory , and mathematical art using lattice paths and geometric transformations . Scientific Awards: ACM ICPC European Founders Award (2004) IOI Distinguished Service Award (2007) Second Place in the 2022 Wolfram Computational Art Contest Notable Collaborations and Affiliations: He is affiliated with the Esprit Working Group on Asynchronous Circuit Design (ACiD-WG) , WIRE (TUE Mathematics Alumni) , ACM (Senior Member) , CSTA , IEEE Computer Society , and Royal Dutch Mathematical Society (KWG) .
دانشیار
Dr. Zhiming Zhao is an Associate Professor and Chair of the Multiscale Networked Systems (MNS) research group at the Informatics Institute (IvI), University of Amsterdam (UvA). He serves as the technical manager of the Virtual Lab and Innovation Center (VLIC) of LifeWatch ERIC, a European research infrastructure for ecology and biodiversity science. Zhao holds an IEEE Senior Member designation and is the Managing Editor of the Journal of Cloud Computing . He earned his Ph.D. in Computer Science from UvA in 2004. His research focuses on quality-critical distributed computing, data-intensive workflows, virtual research environments, and digital twins. He leads projects such as LTER-LIFE (Dutch research infrastructure for digital twins) and coordinates UvA contributions to EU initiatives like ENVRI-HUB Next , EVERSE , and BlueCloud-2026 . Zhao’s work spans technical development in EU projects (e.g., ENVRI-FAIR , ARTICONF , CLARIFY ) and leadership roles in international workshops and conferences. His team develops frameworks like NaaVRE (Jupyter-based collaborative environments) and CloudsStorm (dynamic infrastructure planning). Current research emphasizes trustworthy AI in cloud systems, federated learning, and edge-cloud resource optimization. Key achievements include over 150 peer-reviewed publications, supervision of numerous PhD students, and contributions to open science initiatives. His lab actively explores interdisciplinary applications in environmental science, medical imaging, and blockchain-based decentralized systems.
Dr. W.J. (Wilson) dos Santos Silva is an Assistant Professor at the Faculty of Science , University of Utrecht, specializing in AI & Data Science and Biology . His research focuses on creating explainable and robust AI models for multimodal multi-centre medical data , with emphasis on privacy-preserving machine learning and out-of-distribution generalization . PhD in Electrical and Computer Engineering (2022), University of Porto Master's and Bachelor's in Electrical and Computer Engineering (2016), University of Porto Research Interests include: Explainable AI for medical decision-making transparency Privacy-Preserving Machine Learning in healthcare Multi-Centre Data Analysis across institutions Medical Imaging applications in oncology and neurology Recent Publications demonstrate expertise in: Medical image segmentation techniques Cross-modal learning approaches Federated learning for privacy Biomedical data interpretation Generalization in heterogeneous datasets Scientific Contributions include organizing the iMIMIC workshop at MICCAI 2024 and mentoring students receiving competitive awards. Students & Collaborators : PhD Candidates: Valentina Corbetta, Daan Boeke, Miriam Cobo, Aniek Eijpe, Jan van Eck Postdoctoral Researchers: Soufyan Lakbir Former Students: Tingyang Jiao, Laura Latorre, Filipe Campos, etc. Laboratory develops AI solutions for medical imaging , multi-centre collaboration , and ethical AI in healthcare contexts.
Fons van der Sommen is an Associate Professor in Electrical Engineering at Eindhoven University of Technology, specializing in Video Coding & Architectures. He leads research on computer-aided detection systems for early cancer diagnosis, particularly focusing on esophageal and colorectal neoplasia through advanced AI and computer vision techniques. His research interests span medical image analysis, AI-assisted diagnostics, and developing robust systems for clinical deployment. Recent publications focus on overcoming real-world implementation challenges of AI in endoscopy and enhancing the trustworthiness of diagnostic systems. Recent research trends show strong emphasis on surgical AI applications (robot-assisted procedures), generative models for medical data augmentation, and quality assurance frameworks for clinical AI deployment. His work integrates deep learning with clinical validation across gastrointestinal and pulmonary oncology. TU/e Best PhD Thesis Award (2018) Best Poster Presentation (2017, 2013) He coordinates multiple research projects including TASTI-XECS221002 (Advanced AR for AI-based Servitization) and XL-ARGOS (extended reality solutions). Manages collaborations with medical centers on AI implementation for cancer screening.
پژوهشگر
Corina Brussee is a Researcher in the Department of Epidemiology at Erasmus Medical Center (Erasmus MC). Her primary affiliation involves conducting population-based ophthalmic research with a focus on retinal diseases and epidemiological methodologies. Her research concentrates on the epidemiology of vision disorders, particularly: Population-level studies of myopia complications and macular degeneration Development of retinal imaging analysis techniques Validation of ophthalmic diagnostic methods Large-scale cohort studies of age-related eye diseases Genetic and environmental risk factor analysis Brussée's publications demonstrate consistent focus on advanced retinal imaging analysis, epidemiological study design, and population health approaches to eye disease. Her most cited works involve methodological innovations in detecting retinal pathologies like pseudodrusen, with applications across diverse populations including the Dutch European cohort in the Rotterdam Study.
استادیار
Andreas Bayerl is an Assistant Professor of Marketing at the Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Quantitative Marketing from the University of Mannheim and conducts interdisciplinary research at the intersection of digital behavior, consumer psychology, and data science. His research focuses on how individuals generate, process, and are influenced by digital information, particularly in the context of online reviews and influencer marketing. Using a mixed-method approach—combining large-scale observational data, text and image analysis, and field and laboratory experiments—Andreas investigates behavioral patterns in digital ecosystems. His work has been published in leading journals including Journal of Marketing , Harvard Business Review , MIT Sloan Management Review , and Nature Human Behavior . The most recent publications reveal a consistent focus on digital influence, credibility, and consumer decision-making. Key themes include the effectiveness of micro-influencers, the psychological impact of fake reviews, multimodal analysis of visual and textual content, and experimental validations of marketing strategies across platforms. His methodological rigor and real-world relevance are evident across these works. Andreas has received significant recognition for his contributions, including: H. Paul Root Award for groundbreaking research in influencer marketing He actively teaches in the area of data science and marketing analytics, contributing to the next generation of analytically skilled marketers. While no formal advisees are listed, his role as an assistant professor suggests involvement in student supervision and academic mentorship. His research program appears to be supported by empirical rigor and industry relevance, though specific grants or lab affiliations are not mentioned in the provided text.
پژوهشگر
Anne de Jong is a researcher in the Molecular Genetics department at the University of Groningen, specializing in bioinformatics and computational biology. Her work focuses on developing user-friendly pipelines and web servers for integrating data mining and statistics, particularly in bacterial genetics and transcriptomics. She contributes to tools like BAGEL3, PePPER, and Genome2D, which aid in analyzing prokaryotic genome and transcriptome data. Her group utilizes Linux servers to manage large datasets from techniques such as Next Generation Sequencing and proteomics analysis. Her research interests include RNA folding, biospectroscopy, and translating big-data into biological knowledge. She has published extensively on bacteriocin detection, promoter prediction, and data visualization frameworks for prokaryotic systems biology.
Syed Muhammad Anwar serves as an Associate Professor in Software Engineering at the University of Engineering and Technology (UET) Taxila, Pakistan. He maintains a significant dual affiliation with the Sheikh Zayed Institute at Children's National Hospital in Washington, DC, USA. Additionally, he holds leadership roles as Co-founder and CTO of Sense Digital PVT. Ltd. and Director of both the Virtual Reality and Machine Learning Lab and the Signal Image Multimedia Processing and Learning (SIMPLE) Group at UET Taxila. Dr. Anwar's research spans multiple cutting-edge domains at the intersection of signal processing, machine learning, and medical applications. His primary research interests include: Multimedia Communication and Signal Processing Image and Video Coding and Quality Assessment Biomedical Signal Processing and Brain-Computer Interfaces Medical Imaging including Segmentation, Detection, and Diagnosis Deep Learning applications in healthcare diagnostics Emotion Classification and Human Behavior Modeling His recent scholarly output demonstrates a strong emphasis on applying deep learning techniques to medical image analysis challenges, particularly in brain tumor segmentation, liver tumor detection, and Alzheimer's disease classification. There's also significant work in EEG-based applications including emotion recognition, stress quantification, and game expertise classification. His research effectively bridges theoretical machine learning advances with practical healthcare applications, showing particular strength in adapting deep learning architectures to medical imaging challenges across multiple organ systems. Dr. Anwar actively mentors the next generation of researchers through his leadership of the SIMPLE research group. His current advisees include: PhD Students: Sanay Muhammad Umar Saeed (Quantification of human stress), Romana Farhan (Security in body area networks), Nosheen Sohail (Medical Image Analysis), Amin Ullah (Knowledge extraction), and Saqib Mehboob (Structural health monitoring) MS Students: Haseeb Iftikhar (Doctor recommender system), Faizah Malik (Sentiment analysis), Samreena Aslam (Fashion image retrieval), Huma Shabbir (Fashion image tagging), Khola Rafiq (Ischemic stroke detection), and Saba Naseem (Blood vessel segmentation) As Director of the Virtual Reality and Machine Learning Lab and the SIMPLE research group, Dr. Anwar oversees a dynamic research environment focused on advancing signal processing, multimedia analysis, and machine learning applications, particularly in healthcare contexts. His lab maintains strong collaborations between UET Taxila and international institutions, including Children's National Hospital in Washington DC, facilitating technology transfer between academic research and clinical practice.
Misha D.P. Luyer is a researcher at Amsterdam University Medical Center with significant contributions to surgical oncology, medical imaging technologies, and neuroimmunomodulation. Their work spans clinical research, AI integration in radiology, and surgical technique optimization. Key Affiliations: Amsterdam University Medical Center Research Focus: Specializing in Cytoreductive Surgery , Hyperthermic Intraperitoneal Chemotherapy , and AI applications in radiology . Their studies address perioperative inflammation modulation, cancer detection technologies, and surgical quality improvement. Recent Publications demonstrate interdisciplinary work combining neurostimulation , machine learning , and clinical outcomes research across pancreatic cancer, esophagectomy complications, and AI-human interaction in diagnostics. Collaborative Networks: Active in the E/MTIC Oncology Collaborative Group , working with multidisciplinary teams in radiology, surgery, and computer science.
Prof. Wouter Nagengast is a Professor at the University of Groningen's Faculty of Medical Sciences, affiliated with the Robotics and image-guided minimally-invasive surgery department at the University Medical Center Groningen (UMCG). His research focuses on advanced medical imaging technologies, particularly in oncology, gastrointestinal disorders, and neurosurgery. He specializes in fluorescence imaging, minimally invasive surgical techniques, and AI-driven diagnostic tools. Notable contributions include studies on tumor detection using bevacizumab-based imaging agents and computer-aided diagnosis in gastrointestinal neoplasia. His academic activities include supervising PhD students (e.g., Jolien Tjalma), editorial work for Gastroenterology , and presentations on topics like targeted VEGF imaging and fluorescence-guided surgery. He has published over 127 peer-reviewed articles, with recent work emphasizing molecular imaging applications, endoscopic advancements, and preoperative cancer staging. Research Interests: Medical Imaging, Oncology, Surgical Innovation, Barrett's Esophagus, Neuroendocrine Tumors, AI in Diagnostics Key Activities: Fluorescence imaging trials, robotic surgery development, meta-analyses in diagnostic modalities
Stefan Klein is a Full Professor at Erasmus Medical Center in the Department of Radiology & Nuclear Medicine. His academic work bridges medical imaging, artificial intelligence, and clinical applications, with a strong focus on translating technical innovations into practical healthcare solutions. His research has established him as a leading figure in medical image analysis and AI applications in radiology. Dr. Klein's research spans multiple critical areas in medical imaging technology. His work primarily focuses on Magnetic Resonance Imaging (100%), Radiomics (92%), Deep Learning Methods (78%), and Neoplasm analysis (55%). Additional significant research areas include Systematic Reviews (49%), Optical Coherence Tomography (37%), Artificial Intelligence (32%), and Image Analysis (29%). His interdisciplinary approach combines computer science methodologies with clinical medicine to address complex diagnostic challenges. Recent publications demonstrate a clear trajectory toward increasingly sophisticated AI applications in medical imaging, with emphasis on tumor detection, congenital anomaly identification, and establishing rigorous standards for AI deployment in healthcare. His work shows strong collaboration across institutions and disciplines, with notable contributions to international consensus guidelines for trustworthy AI in medicine. Dr. Klein has supervised 14 research projects according to institutional records, indicating an active role in mentoring the next generation of researchers in medical imaging and AI. His work has attracted significant attention in academic circles and beyond, with multiple publications picked up by news outlets and referenced in policy sources.
Dr. Mirjam van Zuiden is an Associate Professor in Clinical Psychology at the Faculty of Social and Behavioural Sciences, Utrecht University , specializing in psychotraumatology and PTSD research. Her work bridges clinical psychology, neuroscience, and endocrinology, focusing on precision prevention strategies for trauma-related disorders. She leads the 2-ASAP consortium , a longitudinal study developing sex-specific screening tools for PTSD using advanced statistical modeling and interdisciplinary methodologies. Her research explores three core areas: (1) psychobiological mechanisms of trauma susceptibility, (2) early risk detection via neuroendocrinological markers, and (3) oxytocin-based interventions to prevent chronic PTSD. This aligns with her involvement in the AI-aided knowledge discovery lab , integrating computational approaches with neuroimaging data from global consortia like ENIGMA-PGC. Co-investigator in Dutch Famine Birth Cohort (Hongerwinter) studies Principal investigator in HELIUS multi-ethnic urban health research Contributor to ABCD child development studies Van Zuiden serves on editorial boards for the European Journal of Psychotraumatology and Chronic Stress , and collaborates with institutions including Amsterdam UMC, Arq National Psychotrauma Center, and Erasmus Medical Center. Her teaching includes coordinating the Loss & Psychotrauma master's course and supervising clinical and research theses.
B.H.W. Hendriks is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Medical Instruments & Bio-Inspired Technology. His research develops optical and signal processing solutions for medical applications, particularly in surgical environments and tissue analysis. His primary research domains include: Biomedical Engineering Optical Spectroscopy (Diffuse Reflectance) Medical Device Development Surgical Technology Cardiac Signal Processing Tissue Characterization Analysis of his 55+ publications reveals dual expertise: (1) Intraoperative optical sensing (e.g., fiber-optic tissue identification during spine surgery and electrosurgery), and (2) Advanced signal processing for cardiac diagnostics (atrial fibrillation mapping via electrograms/ECG). His work bridges mechanical engineering with clinical practice through real-time surgical workflow analysis and tissue-mimicking phantoms. Hendriks actively supervises research students and has generated 3 significant datasets for tissue characterization. His fingerprint shows dominant activity in spectroscopy (100%), surgery (93%), and tissue analysis (91%), with emerging work in human pose tracking for cardiac catheterization laboratories.
پژوهشگر
Ties Mulders is a Researcher in the Department of Radiology & Nuclear Medicine at Erasmus University Medical Center (Erasmus MC), specializing in advanced imaging techniques with a focus on PET-CT applications in cardiac and thoracic pathologies. His work bridges nuclear medicine, radiology, and clinical cardiology to improve diagnostic accuracy for complex conditions. His core research spans: Cardiac PET-CT Imaging : Investigating FDG-PET/CT variations in prosthetic heart valves and post-sternotomy complications to differentiate infection from inflammation Lung Cancer Diagnostics : Validating AI-driven CAD systems for lung nodule characterization and optimizing radiation-safe follow-up protocols for pulmonary carcinoids Thoracic Lesion Management : Leading international Delphi initiatives to standardize diagnostics for mediastinal cystic lesions through multidisciplinary consensus Analysis of his 2023-2025 publications reveals a strong trend toward clinical translation of imaging biomarkers, particularly in reducing false positives in cardiac prostheses evaluation and minimizing radiation exposure in lung cancer surveillance. His work consistently integrates quantitative imaging with real-world clinical decision-making across nuclear medicine and radiology domains. No scientific awards were documented in the provided profile. While student mentoring details are absent, his collaborative research involves extensive cross-institutional partnerships with cardiologists, thoracic surgeons, and oncology teams. Current projects include the DETECTION initiative for mediastinal lesions and validation studies for AI-based nodule detection systems, though specific grant information isn't disclosed. Dr. Mulders operates within Erasmus MC's Radiology & Nuclear Medicine department, contributing to multidisciplinary teams focused on cardiac imaging innovation and thoracic oncology diagnostics through active participation in prospective cohort studies and international consensus projects.
