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Xiaohui XieView profile

Professor

• AI/Machine Learning
• Neural Networks
• Deep Learning
• +3 more

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
• +3 more

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
• +3 more

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
• +6 more

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
• +6 more

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. SunderlandView profile

Associate Professor

• Medical Physics
• Nuclear Medicine
• Radiation Oncology
• +3 more

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 MenzelView profile

Professor

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Quantitative MRI
• +4 more

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
• +4 more

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 FuentesView profile

Associate Professor

• Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

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.

• Applied Econometrics
• Time Series Analysis
• Forecasting
• +3 more

Filip Van den Bossche serves as Professor and Vice Dean of Education and Students at the Faculty of Economics and Business (FEB) of KU Leuven, where he is affiliated with the Department of Decision Sciences and Information Management and the Operations Research and Statistics Research Group (ORSTAT). He also holds membership in the KU Leuven Institute for Mobility and has been a visiting professor at IÉSEG School of Management in Lille, France since 2016. His administrative leadership extends to the FEB Council, Board, International Mobility and Admissions Committee, and multiple university education committees. His research expertise spans applied econometrics, time series analysis, and composite indicator methodology, with primary applications in road safety and transportation policy. He has developed macroscopic safety models, uncertainty assessment frameworks for safety indices, and evaluation tools for road safety campaigns. His work also addresses market efficiency in finance through adaptive markets hypothesis testing and robust CSR investment screening, alongside healthcare logistics optimization for organ transplant systems. Teaching responsibilities include Statistics for Data Science, Predictive Business Analytics, and time series forecasting courses. Analysis of his 15 most recent publications reveals consistent methodological focus on econometric modeling and time series analysis applied across transportation safety, financial markets, and healthcare logistics. The research demonstrates evolution from foundational road safety modeling toward interdisciplinary applications in sustainable development goals and pandemic response evaluation, with growing emphasis on composite indicators and uncertainty quantification. Cross-cutting themes include policy evaluation, optimization under constraints, and translation of quantitative methods to real-world decision support. As an educational leader, Van den Bossche has directed multiple academic programs since 2012 and currently oversees FEB's educational strategy as Vice Dean. His current sabbatical project (2025-2026) focuses on time series analysis for Sustainable Development Goals. He actively contributes to ORSTAT's research on operations research applications and the KU Leuven Institute for Mobility's interdisciplinary transportation studies, fostering collaborations between business, economics, and public policy domains.

• MRI
• Atherosclerosis
• Aneurysms
• +5 more

David Saloner, PhD, is a Professor in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco (UCSF), and Chief of Radiology Research at the San Francisco Veterans Affairs Medical Center (VAMC). He leads the Vascular and Cardiac Imaging Research Group and is Director of the Vascular Imaging Research Center at SFVAMC. Education: BSc in Physics, Math, Applied Math (University of Witwatersrand, South Africa); MSc in Nuclear Physics, Dr.rer.nat in Nuclear Physics (University of Heidelberg, Germany). Postdoctoral training at University of Wisconsin-Madison and Wake Forest School of Medicine. Research focuses on cardiovascular diseases using advanced imaging methods, including MRI and CT. Key areas include atherosclerosis, aneurysms, and hemodynamic analysis. He leads multi-center studies on carotid atherosclerosis and aneurysm progression. His work integrates computational modeling, phantom studies, and in vivo longitudinal imaging. Publications emphasize vessel wall imaging, deep learning applications, and biomechanical analysis of vascular diseases. Recent work includes MRI-based wall stress quantification and CT radiomics for aneurysm monitoring. Director, VAMC Research Programs Member, VA MR Safety Committee Site PI for VA MERIT project on carotid atherosclerosis Awards: 2020 Best Teacher Award (UCSF), 2017 ISMRM Fellowship, 1997 VAMC Scientist of the Year. Labs/Teams: Vascular and Cardiac Imaging Research Group, Vascular Imaging Research Center (SFVAMC).

• Bioelectronic Medicine
• Neural Engineering
• Neuromodulation
• +3 more

Alejandra Gonzalez Gonzalez is a Research Fellow in Pediatrics - Neurology at Baylor College of Medicine, affiliated with the Neurological Research Institute (NRI). Her research focuses on advancing bioelectronic medicine through innovations in neural interfaces, neuromodulation strategies, and biomedical devices. She specializes in developing cutting-edge technologies for sensing and stimulating nerves, with applications in hypertension treatment and organ communication studies. Her work integrates biophysics, materials science, and computational modeling to create precision therapies. Notable contributions include AxoDetect (image segmentation for nerve modeling), platinumized graphene electrodes, and intraneural SiC electrodes for multimodal sensing. She has pioneered studies on peroneal nerve stimulation for blood pressure regulation in animal models. Dr. Gonzalez's research explores the spleen-vagus neural axis and the biomechanics of autonomic nerves using MEMS force sensing. She collaborates across disciplines to address challenges in device durability, selective neuromodulation, and functional neural interface design. Her lab’s future directions include refining bioelectronic therapies for chronic conditions and advancing multidisciplinary approaches in precision medicine.

Alex K. BrattView profile

Professor

• Radiology
• Artificial Intelligence in Medicine
• Thoracic Imaging
• +3 more

Alex K. Bratt, M.D., is a Professor and Radiologist at Mayo Clinic in Rochester, Minnesota, specializing in Thoracic Radiology. He holds leadership roles including Medical Director of Midwest Radiology Informatics Operations and Co-Chair of Visage Run Org. His academic training includes a Thoracic Radiology Fellowship at Stanford University Medical Center (2019), residency at New York-Presbyterian/Weill Cornell Medical Center (2018), and an MD from Columbia University (2013). He earned a B.S. in Bioengineering from the University of Washington (2009) with Magna cum laude distinction. Dr. Bratt's research focuses on artificial intelligence (AI) in medical imaging, particularly thoracic and cardiovascular applications. He pioneered projects like deep learning models for prostate cancer detection and aortic flow quantification. His work emphasizes practical integration of AI into radiology education and clinical practice. He has received accolades such as the Society of Thoracic Radiology’s Best Junior Faculty Scientific Presentation (2020) and the American College of Cardiology Echo Highlights (2019). His professional roles include leading AI subcommittees, informatics innovation groups, and radiology training programs. Dr. Bratt’s academic contributions span over 15 peer-reviewed publications since 2011, addressing topics from genetic stroke factors to machine learning-driven imaging analysis. His current initiatives include advancing photon-counting CT in thoracic imaging, improving interstitial lung disease classification via machine learning, and optimizing AI tools for non-data-scientists in radiology education.

• Machine Learning
• Healthcare Analytics
• Agricultural Technology
• +3 more

Dr. Yu Zhang is a Researcher at the School of Computer Science, University of Sheffield, affiliated with the Pervasive Computing research group. His work focuses on advanced machine learning techniques applied to healthcare and agricultural domains. He specializes in multi-task learning, spatio-temporal data analysis, and tensor methods. Key research areas include Alzheimer’s disease progression prediction using neuroimaging data and precision fertilization management through sustainable farming systems. His contributions bridge computational methods with real-world applications in healthcare analytics and environmental sustainability. Dr. Zhang’s research integrates temporal relation graphs, adaptive control systems, and interpretable AI frameworks to address complex challenges in disease modeling and agricultural optimization. He explores innovations like continuous incremental learning and randomized feature learning to enhance predictive accuracy and scalability. His work emphasizes practical applications, such as the ParallelFarm system for carbon-neutral farming and frameworks for medical decision-making based on MRI data analysis. His research trends show a strong focus on multi-task learning frameworks that adapt to temporal dynamics, with applications spanning healthcare biomarker identification, resource-efficient farming, and robust algorithm design. Collaborations likely involve interdisciplinary teams in computer science, neuroscience, and environmental science. Future directions may include expanding these methods to other healthcare domains and environmental sustainability initiatives. No scientific awards are explicitly mentioned in the provided text. Advising and grant details are not listed here, but his affiliations suggest involvement in collaborative research projects within the School of Computer Science. His work is rooted in the Pervasive Computing group, emphasizing embedded systems and AI-driven solutions for real-world problems.

• Computer Vision
• Robotics
• Image Processing
• +4 more

Professor Sonya Coleman is a Professor of Vision Systems at Ulster University's School of Computing, Engineering and Intelligent Systems. She leads the Cognitive Robotics team within the Intelligent Systems Research Centre and has held roles including Head of the Research Graduate School since 2012. Her expertise spans image processing, robotics, computational intelligence, and financial engineering. She holds a BSc (First Class) and PhD in Mathematics from Ulster University (1999 and 2003, respectively). Research interests include advanced computer vision techniques, robotic systems, and industrial automation. Notable contributions include work on defect detection in additive manufacturing, neuromorphic computing frameworks, and energy consumption forecasting in smart manufacturing. Her research has been funded by EPSRC, the Nuffield Foundation, and the European Commission. Awards include the 2009 Distinguished Research Fellowship from Ulster University. She serves as Secretary of the Irish Pattern Recognition and Classification Society. Her work often integrates innovative solutions for industrial challenges, such as real-time monitoring systems and AI-driven quality control. Professor Coleman has authored numerous peer-reviewed articles, focusing on topics like spiking neural networks, PCB defect analysis, and glacier area change quantification using satellite remote sensing.