Find Academic Supervisors

• Medical Imaging
• Positron Emission Tomography
• MRI
• +5 more

Dr. Jens Maus is a Research Associate at the Helmholtz Center Dresden-Rossendorf (HZDR), affiliated with the Institute of Radiopharmaceutical Cancer Research and the Department of Positron Emission Tomography. He holds a Ph.D. in Health Sciences and has a computer science background. His research focuses on advancing quantitative methodologies in medical imaging, particularly in PET/MRI systems. His primary research interests include: Development of motion correction and attenuation correction algorithms for PET/MRI Quantitative accuracy validation in oncological and neurological imaging Image reconstruction techniques and system matrix optimization Clinical applications in lymphoma, head/neck cancer, and glioblastoma Publications (2012-2015) demonstrate consistent focus on improving PET/MR quantification accuracy, with key themes including: Correction methods for artifacts (metal implants, truncation, bone attenuation) Time-dependent SUV quantification in oncology Validation studies for lymph node staging Novel reconstruction algorithms and perfusion modeling He collaborates with clinical researchers on studies involving head/neck cancer, lymphoma, and brain tumors, leveraging HZDR's PET/MR infrastructure. No awards, students, or grants are mentioned in available sources.

Sebastian WallotView profile

Professor

• Nonlinear Dynamics in Psychology
• Methodology and Evaluation Research
• Reading Processes and Comprehension
• +7 more

Sebastian Wallot is a Professor of Psychology at Leuphana University of Lüneburg, Germany, specializing in Methodology and Evaluation Research. His academic position centers on developing and applying advanced analytical methods to psychological phenomena, particularly focusing on nonlinear dynamics and time series analysis. Wallot maintains an active research profile with 90 publications spanning from 2011 to 2025, demonstrating consistent scholarly productivity across multiple domains of psychological science. Wallot's research interests bridge complex systems theory with empirical psychological research. He has developed significant expertise in recurrence quantification analysis (RQA) and its applications to diverse psychological phenomena including reading processes, joint action coordination, and psychophysiological measures. His work demonstrates how nonlinear dynamics can reveal patterns in human cognition and behavior that traditional linear methods might miss. Wallot approaches psychological research from a complex systems perspective, emphasizing the interconnectedness of cognitive, physiological, and social processes across multiple timescales. His publication record shows a clear trajectory from foundational work on reading processes and eye movement dynamics toward increasingly interdisciplinary applications in sports science, music cognition, developmental psychology, and occupational health. Recent publications highlight methodological innovations in analyzing time series data of unequal length, multivariate approaches to leader-follower relationships, and applications of nonlinear methods to practical problems in sports medicine and employee recovery. Wallot leads several significant research projects including 'The role of (non-)synchronous coordination and its quantification in joint action,' 'Regularities in Reading Times,' and the prestigious Heisenberg Professorship project 'Quantifying nonlinear dynamics in psychological data.' His collaborative work spans multiple institutions and disciplines, as evidenced by his co-authorship on the MusicLab Copenhagen Dataset and various cross-institutional studies in sports medicine and developmental psychology.

Ilgin GulerView profile

Associate Professor

• Transportation Engineering
• Intelligent Transportation Systems
• Traffic Signal Control
• +8 more

Ilgin Guler is an Associate Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University’s College of Engineering. Her research focuses on intelligent transportation systems, traffic operations, infrastructure sustainability, and equitable mobility solutions. She contributes to interdisciplinary projects such as the Equitable Optimization of Transportation Systems and Services addressing climate and sustainability challenges. Her research interests span Transportation Engineering , Intelligent Transportation Systems , Traffic Signal Control , Connected Vehicles , Pavement Management , and Transit Signal Priority . She applies advanced techniques including deep learning and optimization to improve urban and rural transportation systems. Her work emphasizes safety, equity, and sustainability in infrastructure planning and operations. The recent publications highlight a strong trend in integrating machine learning with classical traffic theory, enhancing infrastructure resilience through data-driven maintenance, and evaluating shared mobility impacts on safety and equity. Her research bridges civil engineering with data science, focusing on real-world applications in Pennsylvania and beyond. Scientific Awards: No awards explicitly mentioned in the provided text. She advises graduate students in transportation engineering and has been involved in multiple research projects, including seed-funded initiatives from the Institute for Energy and the Environment (IEE). While specific grant amounts and student names are not listed, her collaborative work with faculty like Vikash Gayah and Anik Sengupta indicates active mentorship and team leadership. Her research is associated with initiatives such as the Equitable Communities and the Built Environment and leverages tools from connected vehicle technology, impact-echo testing, and generative AI for infrastructure assessment.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +4 more

Jiawei Zhang is an active researcher with primary affiliations at institutions like the University of California, Davis and other universities in China and the USA. His work spans Computer Science , Artificial Intelligence , and Biomedical Engineering , focusing on applications in image processing , autonomous vehicles , and remote sensing . Research interests include deep learning , graph neural networks , 3D reconstruction , and medical image analysis . Recent publications emphasize transformer models , lightweight AI frameworks , and multimodal data fusion . His work has been published in prestigious venues such as IEEE Transactions on Biomedical Engineering , CVPR , and IEEE Internet of Things Journal . Collaborations include researchers from institutions in China, the US, and Europe. Key trends in his 2024-2025 articles involve industrial anomaly detection , liver tumor segmentation , autonomous vehicle control , and AI-driven sensor networks . Methodologies often integrate attention mechanisms , generative models , and real-time optimization .

• Radiation protection
• Radiation dosimetry
• Nuclear physics
• +5 more

Peter Covens is a researcher at Vrije Universiteit Brussel (Brussels, Belgium) specializing in nuclear medicine, radiation protection, and dosimetry. His work focuses on optimizing radiopharmaceutical production (e.g., Astatine-211), advancing computational frameworks for nuclear dose assessment, and analyzing radiation effects in preclinical models. He leads projects like Exploit-PeptIT (peptide-based imaging/therapy) and collaborates with EURADOS-EANM on radiation safety standards. Key research areas: Nuclear medicine, radiation dosimetry, and radiochemistry Supervises Master's theses on topics like extremity dosimetry and radiopharmaceutical optimization Active in conferences/workshops (e.g., 2024 presentation on Astatine-211 accessibility) Contributes to European radiation safety policies and clinical dosimetry protocols

• Medical Image Analysis
• Artificial Intelligence
• Deep Learning
• +3 more

Carlos Cano Espinosa is an Assistant Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante's Higher Polytechnic School II. His work integrates artificial intelligence with medical imaging to advance diagnostic technologies and digital health applications. PhD in Computer Science (international distinction) from University of Alicante MSc in Automation and Robotics His research focuses on medical image analysis using deep learning and machine learning, with applications in: Assisted diagnosis Biomarker quantification Automatic segmentation of anatomical structures Detection of pathological indicators in medical imaging Digital health solutions Metabolic activation analysis in adipose tissue Coronary artery disease assessment He has contributed to high-impact journals such as IEEE Transactions on Medical Imaging and NPJ Digital Medicine , with technical expertise in programming and data structures teaching. María Zambrano program postdoctoral fellowship recipient

• Remote Sensing
• Atmospheric Science
• Environmental Science
• +7 more

Gerrit Kuhlmann is a Senior Scientist at Empa - Swiss Federal Laboratories for Materials Testing and Research, specializing in remote sensing of air pollutants and greenhouse gases. His work bridges satellite data analysis, machine learning applications, and climate change mitigation strategies. Research Focus: Atmospheric remote sensing, emission quantification from cities and power plants, NO2-to-NOx conversion models, and deep learning for climate data. Teaching: External Lecturer (Privatdozent) at the University of Zurich, teaching Python programming and atmospheric remote sensing courses. His recent articles highlight advancements in satellite-based CO2 and methane emission analysis, synthetic data experiments, and development of open-source tools like the ddeq Python library. Collaborations span institutions like University of Zurich, ESA, and climate modeling teams.

Nello BlaserView profile

Research Fellow

• Topological Data Analysis
• Statistics
• Machine Learning
• +3 more

Nello Blaser is a postdoctoral research fellow at the Department of Mathematics, University of Bergen. He holds a PhD in mathematical modeling and biostatistics from the University of Bern and an undergraduate degree in mathematics from ETH Zürich, with a focus on probability theory and stochastic processes in mathematical finance. His current research bridges topological data analysis, statistics, and machine learning, emphasizing practical applicability in biomedical sciences. Blaser’s work focuses on advancing topological methods for large datasets, integrating them into biostatistical workflows, and validating their use with supervised learning techniques. His recent publications span deep learning, parameterized complexity theory, and computational topology, with applications in biomedical data analysis, network modeling, and medical imaging. His research includes 15 recent publications (2022–2025) on topics such as rotation-equivariant neural networks, bifiltration stability, and HIV/TB modeling. Key trends involve topological data analysis, uncertainty quantification in machine learning, and multi-dimensional biomedical data validation.

Oddrun Anita GudbrandsenView profile

Professor

• Nutritional Biochemistry
• Metabolic Syndrome
• Cholesterol Metabolism
• +7 more

Oddrun Anita Gudbrandsen is a Professor at the University of Bergen , affiliated with Clinical Institute 1. Her research focuses on nutritional biochemistry, metabolic syndrome, and health effects of marine-derived proteins and fatty acids. Key Research Areas: Omega-3 fatty acid metabolism, dietary interventions for obesity-related disorders, protein hydrolysates in cardiovascular health, and PPAR ligand interactions with mitochondrial function. Publications: Over 200 peer-reviewed articles since 1996, with recent trends examining fish protein impacts on cholesterol regulation, blood pressure, and gut microbiota. Supervision: Mentored numerous students including Lina Haug (2014), Ida Ulrikke Karlsen (2014), and Simon N Dankel (2017) in topics spanning cod protein effects, metabolic health, and dietary interventions. Technical Contributions: Developer of R-based adipose tissue segmentation tools and advocate for improved duodenal enzyme analysis automation. Her work bridges basic science and clinical applications, with systematic reviews on fish oil impacts and popular science outreach in Bergen's Newspaper. Current projects focus on sustainable marine protein utilization and metabolic disease prevention.

• Infrared Thermography
• Non-Destructive Testing
• Image Processing
• +4 more

Xavier Maldague is a Professor in the Department of Electrical and Computer Engineering at Université Laval's Faculty of Science and Engineering, with over 35 years of research in infrared thermography and non-destructive testing methodologies. His work spans theoretical algorithm development to practical industrial applications across aerospace, cultural heritage, medical diagnostics, and infrastructure inspection domains. Dr. Maldague's research interests focus on advanced thermal imaging techniques, sensor fusion systems, and machine learning applications for defect detection and material characterization. His work has evolved from fundamental thermographic algorithms in the 1990s to current research integrating deep learning, drone-based platforms, and multi-sensor fusion systems for industrial inspection. Key areas include pulsed thermography, line-scan techniques, coplanar capacitive sensing, and hyperspectral infrared imaging. Analysis of his 15 most recent publications reveals a strong emphasis on deep learning integration with traditional thermographic methods, particularly for defect detection in composite materials, cultural heritage objects, and infrastructure components. His recent work shows increasing focus on multi-modal data fusion, drone-enabled inspection platforms, and synthetic data generation for training machine learning models. The research spans both theoretical algorithm development and practical implementation across diverse application domains. Dr. Maldague has maintained a highly productive research trajectory with consistent publication output, particularly in high-impact journals such as Sensors, IEEE Access, and Remote Sensing. His work demonstrates strong international collaboration, with numerous co-authors from European institutions alongside his primary Canadian research team. His research group appears to focus on developing practical inspection systems that bridge theoretical image processing with real-world industrial applications, with recent emphasis on automation, deep learning integration, and multi-sensor approaches to enhance detection capabilities and reliability in non-destructive testing scenarios.

Ketson R Maximiano Dos SantosView profile

Assistant Professor

• Stochastic engineering dynamics and mechanics
• Uncertainty quantification for complex systems
• Structural reliability
• +2 more

Ketson R Maximiano Dos Santos is an Assistant Professor in the Department of Civil, Environmental and Geo-Engineering at the University of Minnesota. He is affiliated with the Stochastics Lab and the Center for Transportation Studies, focusing on stochastic methods for civil engineering systems. His research expertise spans stochastic engineering dynamics and mechanics, uncertainty quantification for complex systems, structural reliability, risk-based optimization, and signal processing for stochastic systems. These methodologies are applied to masonry structural analysis, infrastructure health monitoring, and construction process optimization. Recent publications (2024-2025) demonstrate strong emphasis on computational approaches for masonry structures, featuring topological data analysis for damage assessment, uncertainty-aware deep learning in crack detection, and geometric optimization for natural stone construction. His work consistently bridges civil engineering fundamentals with advanced computational techniques. He serves as Principal Investigator for a Minnesota Department of Transportation project developing temperature-based pavement performance prediction tools, and as Co-Investigator on a 3M-funded initiative applying machine learning to polymer dielectrics design. He directs the Stochastics Lab, which specializes in probabilistic methods for engineering dynamics, and maintains active collaboration with the Center for Transportation Studies on infrastructure resilience research.

Edward AvezovView profile

Professor

• Neuroscience
• Cell Biology
• Dementia Research
• +4 more

Edward Avezov serves as a Professor and Group Leader in the Department of Clinical Neurosciences at the University of Cambridge's School of Clinical Medicine. He is affiliated with the UK Dementia Research Institute and Cambridge Neuroscience, focusing on neuronal cell biophysics within the research themes of Neurons, Circuits and Networks, Beyond the Neuron, and Lifelong Brain Development and Brain Ageing. His research investigates endoplasmic reticulum (ER) structure-function relationships in neuronal physiology and dementia pathogenesis. Avezov's laboratory employs advanced techniques including super-resolution microscopy, genetically encoded biosensors, iPS cell models, and computational modeling to study organelle interactions, cellular neurophysiology, and protein homeostasis. Key clinical conditions under investigation include Alzheimer's disease, hereditary spastic paraplegia, and motor neurone disease. Analysis of Avezov's publication record reveals a consistent focus on ER dynamics in neurodegeneration. His work demonstrates how ER morphology regulation (e.g., via RTN4), luminal transport mechanisms, and stress responses contribute to neuronal dysfunction in dementia. Recent publications highlight innovative methodological developments like ERnet for quantitative ER topology analysis and discoveries regarding ER-mitochondria crosstalk, calcium signaling limitations, and stress-induced protein disaggregation. Avezov maintains active collaborations with leading Cambridge researchers including Michele Vendruscolo, Cahir O'Kane, John Skidmore, Clemens Kaminski, Gabriele Kaminski Schierle, David Rubinsztein, and David Klenerman. His laboratory actively participates in neuroscience community events such as NeuroBioUK and CellBio conferences, as evidenced by their social media presence.

Xiaodong WuView profile

Professor

• Algorithm Design
• Biomedical Image Analysis
• Geometric Optimization
• +3 more

Xiaodong Wu serves as Professor in Electrical and Computer Engineering at the University of Iowa College of Engineering, with a joint appointment in Radiation Oncology. He directs research at the Iowa Institute for Biomedical Imaging, Iowa Initiative for Artificial Intelligence, and Iowa Technology Institute, having joined the university in 2005. His academic credentials include: PhD in Computer Science and Engineering, University of Notre Dame (2002) MS in Computer Science, Peking University (1995) BS in Computer Science, Peking University (1992) Wu's research spans Algorithm Design, Geometric Optimization, and Biomedical Image Analysis, focusing on computational methods for medical diagnostics. His work develops AI-driven solutions for computer-aided surgery, data mining, and bioinformatics, emphasizing clinical applicability through algorithmic innovation. Analysis of his 15 most recent publications reveals dominant themes in medical image segmentation using diffusion models and deep learning. Key applications target ophthalmology (retinal imaging, myopia diagnosis), cardiology (takotsubo syndrome analysis), and oncology, with innovations in latent space representations, robust feature extraction, and outlier detection for diagnostic enhancement. No scientific awards were documented in the source material. Regarding academic mentorship, the provided information lists no advised students or specific research grants. Wu leads the Xiaodong Wu Research Group and leverages cross-institute collaborations at the University of Iowa to advance AI applications in medical imaging, particularly through the Iowa Institute for Biomedical Imaging's interdisciplinary framework.

Nicolás Vila BlancoView profile

Assistant Professor

• Medical Image Processing
• Deep Learning
• Biomedical Engineering
• +4 more

Nicolás Vila Blanco is an Assistant Professor at the University of Santiago de Compostela, primarily affiliated with the Information Technologies Research Center (CiTiUS). His work bridges Medical Image Processing and Deep Learning through software development and algorithm optimization. Education includes: PhD in Information Technology Research (Universities of Santiago de Compostela and Coruña) M.Sc. in Data Science and Computer Engineering (University of Granada) B.Sc. in Computer Science (University of Vigo) Research focuses on applying Artificial Intelligence to biomedical challenges, particularly in dental imaging and cardiac diagnostics . Key contributions include: Developing IDALib (Image Data Augmentation Library) Creating DenTiUS Deep Plaque software for dental plaque analysis Designing PrimerEvalPy for microbiome primer evaluation Building gVARVI for heart rate response analysis Recent publications demonstrate expertise in: Clinical software development Deep learning architectures (U-Net, Few-Shot Learning) Medical imaging applications Biomedical data processing AI in dental diagnostics Microbiome sequencing techniques

Vagelis HarmandarisView profile

Associate Professor

• Computational Mathematics
• Polymer Physics
• Materials Science
• +4 more

Vagelis Harmandaris is an Associate Professor at the Department of Mathematics and Applied Mathematics , University of Crete, and affiliated with the Institute of Applied and Computational Mathematics, FORTH . His research focuses on computational mathematics, polymer physics, and multiscale modeling , particularly through molecular dynamics simulations and coarse-graining techniques. Department of Mathematics and Applied Mathematics, University of Crete Institute of Applied and Computational Mathematics, FORTH Research Interests include: Computational modeling of polymer systems Multiscale simulations for material science Statistical mechanics of complex fluids Mathematical modeling of nanocomposites Publications reveal expertise in polymer melts, graphene-based composites, and interfacial dynamics, spanning disciplines like Polymer Science, Materials Science, and Computational Physics . Key subtopics include coarse-graining, force matching, interfacial properties, and dynamic heterogeneity . Teaching includes courses like Applied Statistics, Parallel Computing, and Mathematical Biology , often involving computational tools such as MATLAB and C++.