جست‌وجوی استاد و پژوهشگر

Greg Zaharchukمشاهده پروفایل

استاد

• Neuroimaging
• Radiology
• Artificial Intelligence in Medical Imaging
• +۴ مورد دیگر

Greg Zaharchuk is a Professor of Radiology specializing in Neuroimaging and Neurointervention at Stanford University's School of Medicine. His academic roles include leadership in clinical and translational imaging research. He holds an MD from Harvard Medical School, a PhD in Applied Physics from Harvard/MIT, and dual undergraduate degrees in Materials Science & Engineering and German Studies from Stanford. Education: MD, Harvard Medical School (2000) PhD, Harvard/MIT (1999) BS & BA, Stanford (1990) Research Focus: Dr. Zaharchuk's work centers on advancing neuroimaging techniques for cerebrovascular diseases, stroke, and neurodegenerative disorders. He pioneers AI-driven innovations in MRI/PET fusion, low-dose imaging, and deep learning models to enhance diagnostic accuracy and clinical decision-making. Key areas include stroke outcome prediction, amyloid PET reconstruction, and cross-modality imaging integration. Research Trends: His recent articles emphasize AI applications in neuroimaging, such as improving low-field MRI quality and PET reconstruction using MRI priors. He also explores stroke lesion segmentation, CBF quantification, and the ethical implications of AI in neuroradiology. Affiliations & Labs: He leads the Center for Advanced Functional Neuroimaging at Stanford, advancing cutting-edge imaging technologies. His research bridges clinical practice and innovation, addressing challenges in ischemic stroke, Alzheimer's imaging, and radiation dose reduction. Grants & Collaborations: His work is supported by NIH grants and industry partnerships, focusing on multimodal imaging systems and AI-driven diagnostics. Collaborations include international neuroradiology societies and interdisciplinary teams in neurology and computer science.

Adrian Sheppardمشاهده پروفایل

استاد

• Materials Physics
• Porous Media Analysis
• X-ray Tomography
• +۳ مورد دیگر

Prof Adrian Sheppard is a Professor in the Department of Materials Physics at The Australian National University (ANU). He holds a B.Sc. (Hons) from the University of Adelaide and a Ph.D. from ANU. His research focuses on porous media analysis, X-ray tomography, and carbon sequestration, with expertise in fluid dynamics, geologic storage, and advanced imaging techniques. He leads projects on CO2 geostorage, multiphase flow modeling, and high-resolution tomographic imaging. His work bridges materials physics, environmental science, and engineering, addressing challenges in energy storage and climate mitigation. He has supervised numerous research students and collaborates on initiatives like the ARC Training Centre for Multiscale 3D Imaging. Key research interests include pore-scale fluid dynamics, supercritical CO2 behavior, and X-ray microtomography applications. Over 200 publications highlight his contributions to understanding fluid trapping mechanisms, beam hardening correction in tomography, and multiscale imaging techniques. He has pioneered methods for analyzing heterogeneous materials and improving tomographic image quality. His projects span geologic carbon storage, additive manufacturing inspection, and environmental fluid dynamics. Prof Sheppard has secured 26 research grants, including projects on CO2 sequestration, X-ray source optimization, and advanced manufacturing. He advises on imaging technologies and leads ANU's efforts in multiscale materials characterization. His lab focuses on applying cutting-edge imaging tools to solve real-world problems in energy and environmental science.

• Nuclear Medicine
• Medical Imaging
• Biomedical Engineering
• +۴ مورد دیگر

Dale Bailey is a Professor in the Faculty of Medicine & Health at the University of Sydney, with clinical appointments at Royal North Shore Hospital's Department of Nuclear Medicine. He previously directed the Sydney Vital Northern Translational Cancer Research Centre and is a member of the Centre for Drug Discovery Innovation, the University of Sydney Nano Institute, and the Charles Perkins Centre. Nuclear Medicine Physicist Theranostics Researcher Clinical Imaging Expert His research focuses on quantitative functional imaging using SPECT and PET, hybrid imaging modalities, radiation dosimetry for personalized cancer therapy, and the biological effects of radiation. He pioneered combined structure/function imaging and advanced radionuclide therapy applications. Recent publications emphasize PET/CT with novel radionuclides, metabolic tumor volume as a prognostic biomarker, and dosimetry validation methods . Key themes include cancer, nuclear medicine, and translational research. PhD in Physics Fellow of the Institute of Physics and Engineering in Medicine (FIPEM) Chartered Scientist (CSci) in the UK International Consensus Guidelines Contributor He supervises research students in radiotheranostics and collaborates on clinical trials for neuroendocrine tumors and glioblastoma. Grants and projects involve hybrid imaging optimization, radiation safety, and personalized treatment planning.

Jenny Collierمشاهده پروفایل

استاد

• Marine Geophysics
• Geodynamics
• Continental Margins
• +۷ مورد دیگر

Professor Jenny Collier is a Professor of Marine Geophysics at Imperial College London's Department of Earth Science & Engineering within the Faculty of Engineering. She holds affiliations with the Dynamic Earth research group, Earth Observation Network, Earth and Planets, Geodynamics: Core to Surface, Hazards, and Marine and Coastal Environments. Her research focuses on mantle plumes, continental breakup, subduction zone processes, and the solid Earth water cycle, with notable contributions to understanding volcanic margin formation and catastrophic megaflood events in the English Channel. Collier has led expeditions using seismic imaging, sidescan sonar, and multibeam bathymetry to investigate oceanic crust structure and tectonic evolution. Education: PhD in Geophysics, University of Cambridge (1986-89) MSc in Geology, University of Durham (1985-86) Research Interests: Collier's work integrates geophysical field data and numerical modeling to study continental rifting, magmatism at volcanic margins, and fluid dynamics in subduction zones. Key projects include the VOILA initiative examining volatile recycling in the Lesser Antilles and the English Channel megaflood hypothesis. She investigates seaward-dipping reflectors, crustal accretion mechanisms, and the role of mantle plumes in continental breakup. Advising & Grants: Supervises PhD students and postdocs through grants from UK research councils. Recent projects include NERC-funded studies on South Atlantic magmatism and Imperial College studentships exploring marine geophysics. She advises on competitive fellowship applications and has secured funding for ocean-bottom seismometer deployments in the Caribbean. Labs & Teams: Core member of the Geodynamics: Core to Surface research group at Imperial. Collaborates with institutions like ION-GXT (South Atlantic studies) and NAG-TEC (North Atlantic research). Active in the Earth Observation Network and Grantham Institute for climate-related geohazards.

Melissano Germanoمشاهده پروفایل

استاد

• Vascular Surgery
• Endovascular Surgery
• Aortic Surgery
• +۳ مورد دیگر

Melissano Germano is a Full Professor of Vascular Surgery at Vita-Salute San Raffaele University, Milan, Italy. He holds leadership roles including Director of the School of Specialization in Vascular Surgery and the II Level Master in Aortic Surgery. His career spans over 30 years in vascular surgery, including roles as Deputy Chief and Senior Staff Surgeon at San Raffaele Hospital. Education includes a B.A. in Humanities (1977–1982), followed by a Medical Degree (1982–1988) and surgical specializations in General Surgery (1988–1993) and Vascular Surgery (1993–1999). He has extensive international collaborations and guest professorships at institutions like Stanford University and Baylor College of Medicine. Research focuses on endovascular techniques, aortic surgery, and surgical outcomes. He has organized over 20 international courses and congresses, including the annual 'Aortic Surgery: How to do it.' Awards include the 'Distinguished Visiting Professor' title at Stanford and the 'Young Vascular Surgeons’ Award.' He has authored over 700 publications and serves on editorial boards of journals like the European Journal of Vascular and Endovascular Surgery. His surgical experience exceeds 12,000 cases, specializing in complex aortic interventions.

Mark Personمشاهده پروفایل

استاد

• Paleohydrology
• Geothermal Systems
• Induced Seismicity
• +۵ مورد دیگر

Mark Person is a Professor in the Department of Earth and Environmental Science at New Mexico Tech. His research focuses on paleohydrology, low-temperature geothermal systems, induced seismicity, and numerical modeling of subsurface fluid flow systems. He leads projects investigating freshwater emplacement in continental shelf environments, offshore groundwater dynamics, and the role of glaciation in modifying groundwater systems. Current active grants include NSF-funded studies on coastal freshwater sequestration and NAGRA collaborations analyzing pressure anomalies in clay formations. Teaching responsibilities include courses such as H510 Quantitative Methods in Hydrology, HYD 547 Hydrological Modeling, and HYD 516 Geofluids. His research group uses advanced modeling tools like PGEOFE and Landlab, and collaborates with institutions like the University of Utah and the University of Dhaka. Notable achievements include the 2021 O.E. Meinzer Award and the 2016 New Mexico Tech Distinguished Research Award. Recent projects include a planned 2025 drilling expedition off Massachusetts to study Pleistocene-aged freshwater deposits, and geophysical surveys in Bangladesh to map deep aquifers. His work integrates magnetotelluric imaging, noble gas dating, and hydrothermal modeling to address subsurface flow dynamics. Advising over 10 graduate students since 2014, his lab has contributed to understanding induced seismicity mechanisms and geothermal system evolution.

Paul E. Handمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۳ مورد دیگر

Paul E. Hand is an Associate Professor of Mathematics and Computer Science at Northeastern University, affiliated with both the College of Science and the Khoury College of Computer Sciences. He holds a Bachelor of Science in Applied and Computational Mathematics from the California Institute of Technology (2004) and a PhD in Mathematics from the Courant Institute at New York University (2009), where he received the Kurt O. Friedrichs Prize for outstanding dissertation. PhD in Mathematics, Courant Institute, NYU (2009) BS in Applied and Computational Mathematics, Caltech (2004) His research focuses on developing theoretical frameworks and algorithms for machine learning and artificial intelligence, particularly in signal recovery, phase retrieval, and vision/imaging. He also explores intersections of deep learning with convex optimization and has contributed to bilinear recovery problems. Recent publications emphasize generative models, inverse problems, and robust optimization techniques. Key themes include deep learning with provable recovery guarantees , convex programming for signal inversion , and manifold-based optimization . Kurt O. Friedrichs Prize for Outstanding Dissertation (2009) NSF CAREER Grant DMS-1848087 He has taught courses on Deep Learning, Machine Learning, and Signal Processing at Northeastern University since 2016, previously holding academic roles at Rice University (2016-2018) and MIT (2009-2016). He directs educational outreach initiatives and developed the educational resource Leading Lesson for multivariable calculus problem-solving.

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

Eunsik Kimمشاهده پروفایل

استاد

• Ergonomics
• Biomechanics
• Human Factors Engineering
• +۵ مورد دیگر

Eunsik Kim is a Professor at the University of Windsor , holding a position in the Department of Mechanical, Automotive, and Materials Engineering within the Faculty of Engineering . His research focuses on ergonomics, occupational safety, and biomechanics, with applications in automotive design, workplace safety, and human-machine interaction. He leads the Occupational Safety and Ergonomics research lab , where students explore real-world solutions for challenges such as driver fatigue, musculoskeletal risks in manual labor, and automation in agriculture. Key research interests include: Ergonomics : Optimizing workstation designs, automotive seating, and anti-vibration tools Biomechanics : Analyzing human posture, musculoskeletal loading, and workplace ergonomics Machine Learning : Developing AI-driven systems for posture recognition, workload prediction, and task automation Autonomous Vehicles : Investigating human factors in driving automation and takeover scenarios Education Innovation : Gamification in engineering labs to enhance student motivation and learning Collaborations include projects with Ewha Womans University (South Korea) on autonomous vehicle comfort and a prototype robotic system for mushroom harvesting. His work often bridges theoretical research with practical applications, addressing both industry and academic challenges. Awards and Recognition: Students Ilfeoma Michael and Elnaz Akhavan Rezaee received accolades at the Association of Canadian Ergonomists conference for their lab research Advising and Grants: Professor Kim mentors students in interdisciplinary projects, focusing on ergonomics, robotics, and AI. His research has led to innovations in workplace safety and automated systems, supported by institutional and collaborative funding. Labs/Teams: The Occupational Safety and Ergonomics Lab and partnerships with South Korean institutions drive cutting-edge studies in automotive safety, robotics, and human factors engineering.

• Experimental astroparticle physics
• Dark matter detection
• WIMP detection
• +۴ مورد دیگر

Matthew Szydagis is an Associate Professor in the Department of Physics at the University at Albany, State University of New York, where he conducts cutting-edge research in experimental astroparticle physics with a focus on dark matter detection. Education: PhD, University of Chicago, 2010 Postdoctoral Associate, University of California Davis, 2010-2014 Dr. Szydagis leads research efforts centered around the LZ (LUX-ZEPLIN) Dark Matter Experiment, the world's largest direct dark matter search project operating at the Sanford Underground Research Facility. His expertise lies in the physics of two-phase Xenon time-projection chambers and the development of sophisticated Monte Carlo simulation techniques to understand detector responses. In 2011, he created the NEST (Noble Element Simulation Technique) software package, which has become an essential tool for the broader scientific community working with noble element detectors. His research spans multiple disciplines including particle physics, astrophysics, and computational physics, with applications extending beyond dark matter research into neutrino physics and medical physics. Dr. Szydagis is an active member of the international LZ collaboration and leads the Dark Matter Research Group at the University at Albany. His work contributes significantly to establishing the world's most sensitive limits on dark matter interactions across a wide range of particle masses.

Sharmin Sultanaمشاهده پروفایل

استادیار

• Multimodal Image Processing
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Sharmin Sultana, PhD, is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's College of Engineering and Computing. Her research focuses on developing advanced computational methods for medical imaging, particularly in artificial intelligence, computer vision, and deep learning applications to improve healthcare diagnostics and treatment. PhD, Computational Modeling and Simulation, Old Dominion University (2017) Postdoctoral Research Fellow, Radiation and Oncology, Johns Hopkins University School of Medicine (2017–2020) MS and BS, Computer Science and Engineering, University of Dhaka, Bangladesh Dr. Sultana specializes in Multimodal Image Processing , Artificial Intelligence , and Medical Imaging , with a focus on creating AI-driven solutions for biomedical image analysis. Her work includes state-of-the-art techniques for image-guided cancer treatment and multimodal image registration. The 15 most recent publications highlight her contributions to prostate cancer imaging , cranial nerve segmentation , and environmentally sustainable materials . Key methodologies include convolutional neural networks, deformable models, and dual contouring for precise surgical planning and simulation.

Mahdi Jampourمشاهده پروفایل

پژوهشگر

• Computer Vision
• Artificial Intelligence
• Digital Humanities
• +۳ مورد دیگر

Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.

Mujdat Cetinمشاهده پروفایل

استاد

• Computational Imaging
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Mujdat Cetin is a Professor of Electrical and Computer Engineering at the University of Rochester, serving as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science. Previously, he held faculty positions at Sabanci University, Istanbul, Turkey, and MIT. He earned his BS from Bogazici University (1993), MS from the University of Salford (1995), and PhD from Boston University (2001). His research focuses on computational imaging, signal processing, and machine learning applications in biomedical imaging, radar systems, and neuroscience. Key areas include uncertainty quantification in imaging, probabilistic methods for image analysis, and brain-computer interfaces. His work bridges electrical engineering, computer science, and biomedical applications. Notable awards include the IEEE Signal Processing Society Best Paper Award and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is an IEEE Fellow and has held editorial roles for journals like IEEE Transactions on Computational Imaging and SIAM Journal on Imaging Sciences. His contributions span conference leadership, including Technical Program Co-chair roles for major signal processing and fusion events. Cetin’s research group has advanced computational sensing, medical imaging algorithms, and EEG-based emotion/motor imagery classification. Ongoing work emphasizes integrating physics-based models with deep learning for robust imaging and signal analysis.

• Contemporary Art
• Art History
• Visual Culture
• +۶ مورد دیگر

Giorgio Bacci is an Associate Professor of Contemporary Art History at the Department of History, Archaeology, Geography, Arts and Entertainment (SAGAS) at the University of Florence. He currently serves as National Coordinator of the PRIN 2022 project BorderArt(E)Scapes. Contemporary Art, Anthropology and Borderscapes and is a member of the UNIFI unit within the CHANGES Partnership funded by PNRR-PE5. His academic career includes positions at the Scuola Normale Superiore (2008-2012, 2017-2018), fixed-term researcher (2012-2017), and teaching appointments at the University of Turin, Academy of Fine Arts in Bologna, IED in Turin, and Alma Artis Academy in Pisa. His research focuses on contemporary art, with particular emphasis on border studies, migration, the relationship between art and literature, and illustration from the 19th to 21st centuries. Bacci has curated numerous exhibitions including Manuele Fior. Viaggio a colori (2024), Simone Bianchi. L'arte dei supereroi (2023), Igort. Attraversare le forme (2023), and Hyper-modern Dante. Illustrations of Dante throughout the world 1983-2021 (2021-2022), among many others. His scholarly work demonstrates how visual culture engages with contemporary social and political issues, particularly through the lens of borders and migration. Bacci serves on the scientific committees of several peer-reviewed journals including Ceramica e Arti Decorative del Novecento , L'Illustrazione. Rivista del libro a stampa illustrato , and Visual History. Rivista internazionale di storia e critica dell'immagine . His recent publications reveal a consistent scholarly trajectory examining how contemporary art engages with historical narratives, cultural identity, and social transformation, particularly through the lens of borders and migration. As an academic advisor, Bacci has supervised award-winning theses at the University of Turin, including Martina Furno's Infanzie e Nazioni. Immagini e testi per ragazzi tra Francia e Italia (1930-1940) (2017) and Giulia Scrocchi's La pubblicità tra arte e immagini a larga circolazione: il caso Fiat nel secondo dopoguerra (2016), both of which won the Premio Piemonte Letteratura. His current research projects include the PRIN 2022 project examining border landscapes through interdisciplinary methodologies combining art history and anthropology.

• Microwave Imaging
• Dielectric Properties
• Antenna Engineering
• +۵ مورد دیگر

Mehmet Çayören is a Professor at Istanbul Technical University in the Department of Electronics and Communication Engineering. His research focuses on applying microwave imaging techniques to medical diagnostics, particularly for early detection of breast cancer. He leads the development of the SAFE (Screening and Early Detection) microwave imaging system, which has shown promising results in clinical investigations. His work bridges electrical engineering, biomedical applications, and machine learning for improved cancer screening. Dr. Çayören's research interests span multiple domains within electromagnetic applications for medical imaging: Microwave imaging systems for breast cancer detection and screening Determination of dielectric properties of biological tissues Development of open-ended coaxial probe techniques for material characterization Application of machine learning algorithms (XGBoost, SVM) to enhance medical imaging Monitoring of intracerebral hemorrhage using microwave imaging Development of tissue-mimicking phantoms for medical device validation His recent publications demonstrate a strong trend toward integrating advanced machine learning techniques with microwave imaging systems to improve diagnostic accuracy. The SAFE platform represents a significant advancement in non-invasive breast cancer screening, particularly for dense breast tissue where traditional mammography has limitations. His work also extends to neurological applications, with research on microwave-based monitoring of brain hemorrhages. Scientific awards received by Dr. Çayören include: Teknoloji Ödülü (Technology Award) in 2014 Dr. Çayören has supervised 24 students and leads multiple research projects funded by various sources including TUBITAK. His current projects focus on microwave imaging systems for breast cancer screening, monitoring of intracerebral hemorrhage, and hardware design for microwave imaging systems. He collaborates extensively with medical professionals to validate his imaging systems in clinical settings. Dr. Çayören leads a research group focused on microwave imaging applications in medicine. His team develops both hardware systems (like the SAFE platform) and advanced signal processing algorithms to improve medical diagnostics. The group maintains close collaborations with hospitals for clinical validation of their imaging systems.