جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Computational Biology
• Biophysics
• +۲ مورد دیگر

Jacob Kæstel-Hansen is a Postdoc at the Department of Computer Science , University of Copenhagen , affiliated with the Machine Learning section under the Faculty of Science. His work bridges machine learning with computational biology, focusing on single-particle tracking and biological data analysis. Research Interests : Machine learning applications in biophysics Single-particle tracking and diffusion analysis Protein aggregation and intracellular dynamics Super-resolution microscopy Automated correlation between biological motion and function Publications : His recent contributions (2024-2025) include advanced machine learning techniques for single-particle dynamics, insulin analog trafficking, and defect-engineered nanocarriers for drug delivery. He collaborates extensively with researchers in biophysics and computational biology. Collaborations : Jacob works with institutions like the SCIENCE AI Centre and researchers in biophysics, chemistry, and computational biology, focusing on sustainability and medical data applications.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

• Machine Learning
• Medical Image Analysis
• Deep Learning
• +۳ مورد دیگر

Mathias Perslev is a Guest Researcher at the Department of Computer Science (DIKU) , University of Copenhagen , actively contributing to the Machine Learning Section and the SCIENCE AI Centre . His research focuses on machine learning applications in medical imaging , particularly in the segmentation and analysis of complex biomedical data using advanced deep learning techniques. He completed his PhD in 2023 with a dissertation on "Segmentation of medical images and time series using fully convolutional neural networks" , highlighting his expertise in developing novel computational methods for clinical imaging challenges. His research interests include: Deep Learning for Medical Image Analysis Convolutional Neural Networks (CNNs) and UNet Architectures 3D Medical Imaging & Multi-planar Segmentation Neuroimaging & Stroke Lesion Detection Kidney, Liver, and Knee Tumor Segmentation Biomedical Data Augmentation & Benchmarking His recent publications demonstrate a clear trajectory towards automated, high-precision medical image segmentation , with applications spanning oncology , neurology , and musculoskeletal disorders . His collaborative work includes participation in international challenges such as KiTS23 and LiTS , contributing to open benchmarks for medical AI. While no specific awards are listed, his consistent publication record in high-impact journals and conferences underscores his significant contributions to the field. Currently, he is affiliated with the Machine Learning Section at DIKU, leveraging the department's advanced compute resources and participating in interdisciplinary research initiatives within the SCIENCE AI Centre.

Mostafa Mehdipour Ghaziمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۱۳ مورد دیگر

Mostafa Mehdipour Ghazi is an Assistant Professor at the Department of Computer Science , University of Copenhagen , and a member of the Pioneer AI (P1AI) research group. His work bridges machine learning, medical imaging, and neuroscience to address challenges in healthcare. Education : PhD in Medical Imaging with Deep Learning from University College London. Languages : Azerbaijani, Turkish, Persian, English, Danish. Research Interests : Machine Learning and Deep Learning Medical Imaging Analysis (MRI, CBCT) Robust Representation Learning for Heterogeneous Data Bias, Fairness, and Privacy in Generative Models Domain Adaptation and Transfer Learning Brain Disease Modeling (Alzheimer's, Stroke, Tumors) Article Trends : Recent work focuses on multimodal AI for disease prediction, deep generative models in neuroimaging, and domain adaptation techniques. Applications span Alzheimer's diagnosis, cerebral microbleeds in COVID-19, and adaptive segmentation tools like RARE-UNet and FAST-AID.

Scott Reederمشاهده پروفایل

استاد

• Medical Imaging
• Radiology
• MRI
• +۳ مورد دیگر

Scott Reeder, MD, PhD , is the John H. Juhl Professor and Chair of the Department of Radiology at the University of Wisconsin School of Medicine and Public Health . He also serves as the Director of the UW Liver Imaging Research Program (LIRP), an NIH-funded initiative focused on advancing MRI techniques for liver disease assessment. His academic journey includes medical and graduate training at Johns Hopkins University , followed by residency and fellowship at Stanford University , and an additional fellowship in Germany. Education : Johns Hopkins University School of Medicine (MD, PhD), Stanford University Medical Center (residency, fellowship), Queen’s University (BS in Engineering Physics). Dr. Reeder’s research interests center on quantitative MRI biomarkers for liver diseases, including abdominal adiposity, liver fat, iron overload, perfusion quantification, and contrast agent development . His work has led to over 350 peer-reviewed publications and 40 inventions , supported by $22 million in extramural funding as PI or co-PI. He is a fellow of multiple societies, including the International Society for Magnetic Resonance in Medicine and the American College of Radiology , and has held leadership roles such as past president of ISMRM and SABI. His lab collaborates with Department of Medical Physics , Biomedical Engineering , and Waisman Laboratory , emphasizing interdisciplinary innovation in imaging technologies.

• Deep Learning in Radiation Oncology
• Radiation Treatment Planning
• Medical Image Segmentation
• +۴ مورد دیگر

Joëlle van Aalst is an active academic researcher specializing in deep learning applications for radiation oncology. Her work bridges computational methods with clinical practice in radiation therapy treatment planning. Contributes to UN Sustainable Development Goals through healthcare innovation Active in uncertainty quantification for medical imaging Research focuses on improving auto-segmentation accuracy in radiation oncology, with three recent publications addressing: Network uncertainty analysis for rectal cancer treatment Dosimetric impact of segmentation errors Uncertainty visualization requirements for clinicians She participated as a contributor in the 2025 Uncertainty in Machine Learning Symposium, demonstrating interdisciplinary collaboration.

Xiaohui Xieمشاهده پروفایل

استاد

• AI/Machine Learning
• Neural Networks
• Deep Learning
• +۳ مورد دیگر

Xiaohui Xie is a Professor in the Department of Computer Science at the Bren School of Information and Computer Sciences, University of California, Irvine. He joined UC Irvine in 2007 after completing his PhD at MIT and postdoctoral training at the Broad Institute of MIT and Harvard University. His research focuses on AI/machine learning, neural networks, deep learning, and genomics. PhD in Computer Science, MIT Postdoctoral training at Broad Institute Research interests span AI, neural networks, deep learning, and genomics, with applications in medical imaging, computational biology, and computer vision. Recent publications highlight advancements in 3D convolutional networks for nodule detection, medical image analysis, and hybrid models for DNA sequence function quantification. Scientific awards and honors are not explicitly mentioned in the provided text. His teaching includes courses like CS273P (Machine Learning and Data Mining), CS175 (Project in AI), and foundational classes in computational linear algebra and optimization, reflecting his interdisciplinary expertise.

• Radiology
• Spinal imaging
• Degenerative disc disease
• +۳ مورد دیگر

Linda Berg is an Associate Professor in the Department of Clinical Medicine at UiT The Arctic University of Norway, Tromsø. She is a radiologist whose clinical and research activities center on spinal imaging, degenerative disc disease, and long-term outcomes after lumbar disc replacement surgery. Research Interests Her work integrates advanced imaging techniques—particularly MRI and CT—with clinical outcome data to understand degenerative changes in the lumbar spine. Key themes include: Facet joint arthropathy and its evolution after total disc replacement versus conservative rehabilitation. Reliability and validity of MRI and CT in detecting progressive spinal degeneration. Paraspinal muscle changes (e.g., multifidus fat infiltration) as predictors of surgical and rehabilitative success. Adjacent segment degeneration following disc arthroplasty and its imaging correlates. Publication Trends Berg’s peer-reviewed output (2011–2020) is dominated by prospective and randomized studies comparing surgical and non-operative management of chronic low back pain. Methodologically, she emphasizes longitudinal imaging follow-up and rigorous observer-reliability analyses, providing evidence that informs patient selection and postoperative surveillance protocols. Scientific Distinctions & Funding No specific awards or external funding statements were captured in the provided content. Advising & Collaboration While individual student names are not listed, Berg frequently co-authors with multidisciplinary teams including orthopedic surgeons, radiologists, and rehabilitation specialists—indicative of robust graduate-level supervision and cross-departmental collaboration. Laboratory & Clinical Affiliations She conducts her imaging analyses within UiT’s clinical radiology infrastructure and collaborates closely with the university hospital’s spine surgery and rehabilitation units.

• Medical Imaging
• Neuroscience
• Cognitive Psychology
• +۳ مورد دیگر

Gordon Waiter is a Senior Lecturer at the University of Aberdeen , affiliated with the School of Medicine, Medical Sciences and Nutrition and the Department of Radiology (now part of the Aberdeen Biomedical Imaging Centre). He holds positions as Director of the Aberdeen Biomedical Imaging Centre and Associate Director of the Aberdeen Centre for Health Data Science , alongside Honorary Clinical Scientist and MR Safety Expert at NHS Grampian. He is a member of the North of Scotland Research Ethics Committee . PhD in Medical Physics (University of Aberdeen, 1991–1996) BSc (Hons) in Physical Sciences (Robert Gordon University, 1989) Dr. Waiter’s research spans brain plasticity , cognition , and age-related cognitive decline , with a focus on inflammation and immunosenescence in brain aging. His work integrates computational neuroimaging , biochemistry , and statistical modeling to investigate disorders like Alzheimer’s disease , Long COVID , and avoidant restrictive food intake disorder (ARFID) . Recent publications emphasize MRI method development (field-cycling, fMRI, FDG-PET), machine learning applications in neuroimaging, and cross-cohort studies on brain structure-function relationships. His 2025 articles explore topics such as brain recovery in Takotsubo Syndrome , cerebellar roles in appetite dysregulation , and neuroinflammatory links to fatigue in rheumatoid arthritis . Scientific honors include: Fellow of the Institute of Physics (FInstP) Chartered Physicist (CPhys) Chartered Scientist (CSci) Fellow of the Institute of Physics and Engineering in Medicine (FIPEM) Aberdeen Lead for SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) Dr. Waiter co-ordinates the MSc Medical Imaging program and teaches fMRI theory , image analysis , and SPM (Statistical Parametric Mapping) at the University of Aberdeen. He leads research teams in the Aberdeen Biomedical Imaging Centre , focusing on neuroimaging protocol development and multisystem disorder analysis .

• Transportation Planning
• Sustainable Transportation
• Electric Vehicles
• +۶ مورد دیگر

Cinzia Cirillo is a Full Professor at Politecnico di Torino's Interuniversity Department of Regional and Urban Studies and Planning (DIST). Her research focuses on transportation planning and energy, with expertise in behavioral models for electric vehicle adoption, digital twins, discrete choice analysis, and survey methodology. Transportation Planning Sustainable Transportation Electric Vehicles Digital Twins Discrete Choice Analysis Survey Methodology Her recent publications explore behavioral responses to electric vehicle adoption, complete streets design impacts, and synthetic population generation for transport modeling. She contributes to SDG Goal 7 (Affordable and Clean Energy) and Goal 8 (Decent Work and Economic Growth).

• Artificial Intelligence
• Computer Vision
• Domain Adaptation
• +۶ مورد دیگر

Tatiana Tommasi is an Associate Professor at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin, actively contributing to advanced AI research and education. She serves as Deputy Coordinator of the National Doctoral College in Artificial Intelligence and Scientific Advisor for the European AI, Data, and Robotics Association (ADRA) and ELLIS Network. Her affiliations include the SmartData@PoliTO Laboratory and VANDAL Visual and Multimodal Applied Learning Laboratory. Her research focuses on algorithm fairness, computer vision, domain adaptation, autonomous driving, and robot learning. Recent work includes improving neural network reliability through fault-aware design, 3D semantic novelty detection, and visual relationship reasoning for robotic grasp planning. She has contributed to European research projects like ELSA (Secure and Safe AI) and commercial initiatives in space technologies and autonomous vehicle uncertainty quantification. Selected 2024-2025 publications demonstrate expertise in foundation models, fairness metrics, uncertainty estimation, and geometric deep learning. Her team leads commercial research projects on industrial robotics trajectory generation and satellite data analysis. She teaches core courses in Computer Engineering, Automotive Engineering, and Data Science, mentoring PhD students in Artificial Intelligence and Computer Systems Engineering. Research keywords: Artificial Intelligence, Machine Learning, Computer Vision, Domain Adaptation, Robot Learning, Transfer Learning, Fairness in AI, Uncertainty Quantification, Autonomous Driving, 3D Point Cloud Analysis, Vision Transformers, Industrial Robotics.

John J. Sunderlandمشاهده پروفایل

دانشیار

• Medical Physics
• Nuclear Medicine
• Radiation Oncology
• +۳ مورد دیگر

John J. Sunderland is an Associate Professor at the University of Iowa with primary appointments in the Department of Radiology and secondary appointments in the Department of Physics and Astronomy. As Director of the PET Imaging Center, he spearheads advancements in positron emission tomography, nuclear spectroscopy, and image analysis algorithms. Institution: University of Iowa Academic Rank: Associate Professor Departments: Radiology, Physics and Astronomy Research Interests His research focuses on medical physics, particularly in radiation detector development, physiological modeling of radiopharmaceutical kinetics, and cross-disciplinary collaboration between engineers, physicians, and physicists. Current projects emphasize improving PET scanner calibration, reducing dosimetry variability, and integrating artificial intelligence into nuclear medicine. Article Trends Recent publications highlight innovations in PET/CT standardization, Monte Carlo-based dosimetry, and AI-driven image analysis. Key subfields include radiopharmaceutical safety, image harmonization, and computational modeling for precision therapies. Labs & Facilities The PET Imaging Center at Iowa houses a medical cyclotron, three hybrid PET/CT scanners, and a research-dedicated PET scanner, supporting multidisciplinary training in cyclotron operations, imaging software, and simulation tools.

Marion Menzelمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Quantitative MRI
• +۴ مورد دیگر

Marion Menzel is a Professor and Vice Dean at the Faculty of Electrical Engineering and Information Technology (Technische Hochschule Ingolstadt). She specializes in Biomechatronics and Sensor Data Analysis, with a focus on Medical Imaging and Magnetic Resonance Imaging (MRI). Her research emphasizes quantitative MRI techniques, including MR fingerprinting, deep learning applications in medical imaging, and hyperpolarized MRI for metabolic analysis. Education highlights include a Ph.D. (1999–2002) and Diplom-Chemiker (1994–1999) from RWTH Aachen. She has held roles at GE Healthcare, Siemens AG, and Forschungszentrum Jülich before joining THI in 2021. Her work bridges engineering and clinical practice, addressing challenges in imaging reconstruction, parameter mapping, and AI-driven diagnostics. Research interests span MRI acceleration, uncertainty quantification, and multimodal data fusion. She has pioneered methods like StoDIP and MRI2Qmap, advancing 3D imaging and compressive sensing. Her contributions to hyperpolarized 13C MRI enable non-invasive metabolic profiling in oncology and diabetes research. Key awards include recognition as an Alumna of the German Academic Scholarship Foundation. Her lab collaborates on projects like CHAIMELEON, a Pan-European initiative for AI-driven cancer management tools. Recent grants focus on improving diagnostic accuracy through AI and spatiotemporal reconstruction algorithms. Menzel’s team develops open-source frameworks for MRI analysis and actively participates in standardizing medical imaging protocols. Her work has direct clinical impact in neuro-oncology, radiology, and precision medicine.

• Signal Processing
• Computer Vision
• Ocean Remote Sensing
• +۴ مورد دیگر

Ronan FABLET is a Professor in the Mathematical and Electrical Engineering (MEE) department at IMT Atlantique, a member of the Institut Mines-Télécom. His research focuses on signal processing, computer vision, and machine learning applied to ocean remote sensing and geophysical data analysis. He has held academic and research positions since 2002, including postdoctoral fellowships and industry collaborations. Education and Career: Bachelor's degree from École Nationale Supérieure de l'Aéronautique et de l'Espace (SUPAERO), France (1997) PhD in Signal Processing and Telecommunications from University of Rennes (2001) Postdoctoral fellowship at Brown University, USA (2002) Researcher at Ifremer Brest (2003–2007) Associate Professor at Telecom Bretagne (2008–2012) Professor at IMT Atlantique since 2012 Visiting researcher at IRD/IMARPE (Peru) and IMEDEA (Spain) Research Interests: Dr. FABLET specializes in data assimilation, neural networks for geophysical dynamics, and ocean-atmosphere interactions. His work integrates machine learning with physical models to enhance predictions of climate phenomena like the Atlantic Meridional Overturning Circulation (AMOC) and ocean eddies. Key areas include: Neural data assimilation for regime shift monitoring SWOT satellite data integration for ocean mapping Machine learning for particle trajectory prediction and sediment trap analysis Uncertainty quantification in high-dimensional systems Recent Contributions: His team develops frameworks like 4DVarNet for spatiotemporal interpolation of satellite and in situ data, advancing applications in ocean color reconstruction, wind speed estimation, and submesoscale dynamics. Published work emphasizes hybrid modeling, end-to-end learning, and uncertainty-aware systems. Labs and Collaborations: Active in the Lab-STICC (Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance) and the ODYSSEY team (Océan Dynamique Observations Analyse). Collaborates with institutions like Mercator Ocean International and Naval Group on operational oceanography and maritime applications.

Olac Fuentesمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Olac Fuentes is an Associate Professor in the Computer Science Department at the University of Texas at El Paso. His primary research focuses on developing machine learning systems for scientific data analysis, with applications in astronomy, geology, biology, and optics. He specializes in leveraging unlabeled data, active learning, feature selection, and noise-aware algorithms to extract insights from complex datasets. Education: B.S. in Industrial Engineering from Instituto Tecnológico de Chihuahua M.S. in Computer Science from University of Texas at El Paso Ph.D. in Computer Science from University of Rochester Research Focus: Dr. Fuentes develops intelligent systems for interdisciplinary scientific challenges. His work spans machine learning theory (optimization, noise handling) and applied domains including: computer vision for environmental monitoring (Arctic change detection, glacier segmentation), neuroinformatics (brain mapping), and multimodal data fusion (prosodic analysis for communication disorders). He frequently employs deep neural networks, active learning, and novel regularization techniques. Publication Trends: Recent works demonstrate strong cross-disciplinary focus on climate science (satellite image analysis for coastal erosion), biomedical applications (neuroanatomy mapping, histology), and fundamental ML theory. His publications consistently integrate advanced neural architectures (CNNs, LSTMs) with domain-specific challenges in geosciences, neuroscience, and linguistics.