جست‌وجوی استاد و پژوهشگر

• Mechanobiology
• Computational Biology
• Spatial Genomics
• +۷ مورد دیگر

Adrien Hallou is a Group Leader in Tissue Biology (Innovation Investigator Track) and Principal Investigator at the Kennedy Institute, University of Oxford. He leads the Laboratory of Cell and Tissue Dynamics, funded by the Kennedy Trust for Rheumatology Research, Royal Society, Clarendon Fund, EPA Cephalosporin Fund, and Innovate UK. He holds an MSc from École Normale Supérieure Paris and an MPhil/PhD from the University of Cambridge. His research integrates mechanobiology , spatial genomics , advanced imaging , machine learning , and mathematical modelling to investigate mechanical/biochemical signaling in cell fate decisions and tissue dynamics during development, homeostasis, and disease. Key themes include tissue fluidity regulation, computational pipelines for mechano-transcriptomics, and biomechanical switches in epithelial tissues. Recent publications emphasize interdisciplinary approaches, with trends in computational biology (55%), tissue mechanics (30%), and machine learning applications (15%). Awards include: Oliver Gatty Studentship in Biophysical Sciences Junior Research Fellowship, Darwin College Cambridge Herchel Smith Research Fellowship He actively recruits PhDs/postdocs and supports fellowship applications (EMBO, Wellcome, HFSP). His lab focuses on collaborative, interdisciplinary work in cell/tissue dynamics.

Shilin Zhaoمشاهده پروفایل

استادیار

• Artificial Intelligence in Digital Pathology
• Spatial Transcriptomics Analysis
• Kidney Disease Morphomics
• +۴ مورد دیگر

Shilin Zhao is an Assistant Professor of Biostatistics at Vanderbilt University Medical Center, specializing in artificial intelligence applications for digital pathology and multi-omics integration. His work bridges computational methodology development with clinical pathology to address challenges in kidney disease, gut microbiome research, and metabolic disorders. Education PhD in Bioinformatics, Shanghai Institutes for Biological Sciences Research Focus Dr. Zhao's research centers on AI-driven pathology and spatial omics , with primary emphasis on: Glomerular and kidney layer segmentation using cross-species data integration Foundation model assessment for cell nuclei analysis in renal histopathology Multi-omics approaches to colitis, atherosclerosis, and metabolic diseases Development of graph networks for spatial transcriptomics prediction His methodologies frequently combine deep learning with biostatistical rigor to translate computational insights into clinical pathology applications. Publication Trends Dr. Zhao's 2025 publications demonstrate intense focus on AI pathology tools (14/15 articles), particularly glomerular analysis and spatial transcriptomics. Key themes include cross-species model adaptation (GLAM), multi-level attention networks (MagNet), and clinical validation of AI foundation models in kidney pathology. His work consistently targets diagnostic precision through computational innovation. Scientific Recognition No specific awards or honors were documented in the provided materials. Academic Contributions While student advising details are unavailable, Dr. Zhao actively contributes to methodological advances in biostatistics through high-impact publications. His involvement in the KPIS 2024 challenge indicates leadership in establishing glomerular segmentation benchmarks for the pathology AI community. Research Environment As primary faculty in Vanderbilt's Department of Biostatistics, Dr. Zhao likely collaborates with institutional resources including the Vanderbilt Biostatistics Data Coordinating Center (VBDCC) and Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD), though specific affiliations aren't explicitly stated.

• Signal Processing
• Communication Systems
• Wireless Communications
• +۵ مورد دیگر

Assoc. Professor Jun Tong is affiliated with the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong. He holds a PhD from City University of Hong Kong and specializes in signal processing for communication systems and instrumentation, focusing on robust, low-complexity algorithms for high-dimensional signals and imperfect models. His research enhances spectrum/energy efficiency in challenging environments. Research Interests: Signal processing applications in communication systems, MIMO and 5G/6G technologies, optical systems, machine learning for signal processing, and sensor networks. He has published extensively in top-tier journals like IEEE Transactions and conferences such as ICC and MWP. Teaching & Supervision: Teaches Digital Signal Processing, Engineering Electromagnetics, and Digital Hardware. Supervises PhD/Master’s projects on topics like 6G wireless communication, MIMO systems, and light-field imaging. Current students are researching areas such as ODDM modulation and sub-THz communications. Funding & Grants: Secured grants including the Advancement and Equity Grants Scheme for Research (AEGiS) for multi-modal defect detection in manufacturing. Previously led projects on UOW-UESTC collaborations. Service & Leadership: Member of the IEEE. Actively involved in academic leadership and curriculum development within his school.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +۴ مورد دیگر

Franceschiello Benedetta is an Associate Professor at HES-SO Valais-Wallis School of Engineering, specializing in Technical and IT disciplines. She holds a PhD in Mathematical Neuroscience from Université Pierre et Marie Curie (Paris). Her work bridges applied mathematics, computational neuroscience, and neuroimaging, with a focus on visual perception modeling, MRI techniques, and neural dynamics. Teaching: Linear Algebra courses across multiple engineering bachelor programs Expertise: Combines mathematical modeling with neuroscientific applications to study optical illusions, brain connectivity, and ophthalmic diagnostics Key Affiliations: ISMRM, Organization for Human Brain Mapping (OHBM), Association for Research in Vision and Ophthalmology (ARVO) Research Interests: Computational modeling of visual cortex mechanisms underlying geometric optical illusions Development of MRI-based methods for eye structure segmentation and axial length estimation Analysis of brain network reliability through standardized MRI protocols Optimization techniques for medical imaging reconstruction (e.g., weighted LASSO problems) Recent Work Trends: Focus on integrating psychophysical experiments with computational models to elucidate perceptual mechanisms, emphasizing synergistic interactions between physical stimulus parameters. Active in advancing MRI applications for both clinical diagnostics and fundamental neuroscience research.

Michael W. Jenkinsمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Cardiac Imaging
• +۳ مورد دیگر

Michael W. Jenkins is a Professor of Biomedical Engineering at the Case Western Reserve University School of Medicine and a member of the Cancer Imaging Program at the Case Comprehensive Cancer Center. His research focuses on developing biomedical optics tools for studying congenital heart disease and peripheral nervous system disorders. Key techniques include optical coherence tomography (OCT), light-sheet microscopy, and infrared neuromodulation. The Jenkins Lab specializes in advancing 3D imaging modalities for real-time tissue analysis, aiming to reduce surgical delays and improve diagnostic accuracy. Research interests include rapid 3D tissue visualization to replace traditional frozen-section pathology, optical pacing of cardiac tissues, and corneal nerve imaging. His work bridges engineering and medicine, with applications in ophthalmology, cardiology, and neurology. Recent innovations include label-free microscopy techniques (e.g., MUSE imaging) and AI-driven segmentation tools for neural anatomy analysis. Publications highlight advancements in corneal crosslinking assessment, vagus nerve microanatomy characterization, and cardiac tissue imaging. The lab collaborates with industry to translate optical tools into clinical settings, emphasizing precision and real-time diagnostics. Teaching and mentorship are integral to his role, fostering interdisciplinary training in biomedical optics. Ongoing projects address unmet clinical needs in neural modulation therapies and regenerative medicine through optical platforms.

Ron Yurkoمشاهده پروفایل

عضو هیئت علمی

• Sports Analytics
• Natural Language Processing & Large Language Models
• Biostatistics & Epidemiology
• +۴ مورد دیگر

Ron Yurko is an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University , where he focuses on methods at the intersection of statistical inference and machine learning. He serves as Director of the Carnegie Mellon Sports Analytics Center (CMSAC) , overseeing initiatives like the sports analytics research lab, CMSACamp , and the annual Sports Analytics Conference. Research Interests His work bridges Sports Analytics and Natural Language Processing , with applications in Biostatistics , Public Policy , and Statistical Pedagogy . Recent projects include evaluating NFL player positioning ( NFL Ghosts ), analyzing language patterns in LLM outputs, and developing fractional tackle metrics for defensive evaluation. Google Scholar articles reveal deeper methodological contributions to Bayesian modeling for directional movement analysis, multilevel models for snap timing, and spatio-temporal frameworks in football event modeling. Academic Background PhD in Statistics (2022) from Carnegie Mellon University BS in Statistics with University Honors (2015) from Carnegie Mellon Additional Contributions He is developing a textbook Statistical Methods in Sports Analytics (2027), maintains the Statistical Thinking in Sports Analytics newsletter, and teaches course 36-460/660 at CMU.

• Retinal Disease
• Age-Related Macular Degeneration
• Retinal Detachment
• +۱۲ مورد دیگر

Luciano V. Del Priore, MD, PhD serves as the Robert R. Young Professor of Ophthalmology and Visual Science at Yale School of Medicine, where he also holds the position of Chair of the Department of Ophthalmology and Visual Science and Chief of Ophthalmology at Yale New Haven Hospital. Previously, he was the Pierre G. Jenkins Professor and Chair of Ophthalmology and Visual Science at the Medical University of South Carolina, and held faculty positions at Columbia University and Washington University School of Medicine. Dr. Del Priore specializes in the surgical and medical treatment of retinal diseases, with particular expertise in age-related macular degeneration, retinal detachment, diabetic retinopathy, and macular holes. His research focuses on the cell biology of retinal degenerations and cell-based therapies for conditions including retinitis pigmentosa, Stargardt's disease, and atrophic age-related macular degeneration. He has made significant contributions to understanding the role of the retinal pigment epithelium in maintaining the choriocapillaris and how its dysfunction affects visual recovery after surgery. His recent publications demonstrate a strong emphasis on applying advanced computational techniques to understand retinal disease progression, including deep learning frameworks for medical image segmentation and statistical methods for analyzing longitudinal disease trajectories. His work bridges clinical ophthalmology with cutting-edge computational approaches to improve diagnosis and treatment of retinal conditions. America's Top Doctors (2019) Life Fellow, American Academy of Ophthalmology (2018) Senior Achievement Award, American Academy of Ophthalmology (2016) Distinguished Alumnus Award, Wilmer Eye Institute (2012) Lew R. Wasserman Award, Research to Prevent Blindness (1999) Senior Honor Award, American Society of Retina Specialists Dr. Del Priore maintains an active research laboratory focused on the biology of the retina in health and disease. He has been involved in clinical trials of stem cell-derived therapies for retinal degenerations and has published extensively in peer-reviewed literature. His work has led to better understanding of disease progression in geographic atrophy, Stargardt's disease, and choroideremia, with the goal of improving clinical trial design and drug efficacy assessment.

Milica Balabanمشاهده پروفایل

دانشیار

• Organic Chemistry
• Polymer Chemistry
• Materials Science
• +۶ مورد دیگر

Milica Balaban is an Associate Professor at the Faculty of Natural Sciences and Mathematics , University of Banja Luka, serving as Head of the Chemistry Department since 2014. She holds a Ph.D. in Polymer Chemistry from the University of Belgrade (2013) and has dedicated her career to polymer synthesis, structural characterization, and environmental chemistry. Focus on thermoplastic elastomers, self-healing polymers, and green chemistry principles Active in international collaborations (EIT RawMaterials, Erasmus+) Coordinator for education innovation projects and STEM outreach Her research spans polymer additives, food packaging safety, mechanochemistry, and sustainable materials. She has secured grants for projects like MechSusInd and RM@Schools-4 , and serves as an ad-hoc reviewer for journals including Journal of the Serbian Chemical Society . Awards include Matsumae International Foundation and Erasmus+ Key Action 1 grants.

• Biomedical Image Processing
• Mathematical Analysis
• Computational Geometry
• +۳ مورد دیگر

Giacomo Nardi is an Associate Professor (Maître de Conférences) at INSA Rouen starting September 2025, holding a PhD in Mathematics from Sorbonne University. His research bridges mathematical analysis and biomedical image processing through extensive collaborations with the Institut Pasteur in Paris, focusing on developing advanced mathematical frameworks for biological imaging applications. His educational background includes: PhD in Mathematics, Sorbonne University (Pierre and Marie Curie), thesis: "On a characterization of the relaxation of a generalized Willmore functional" under supervisors S. Masnou and E. Mascolo. Nardi's work spans Biomedical Image Processing and Mathematical Analysis , evolving from foundational contributions in calculus of variations (Willmore functionals, BV functions) to cutting-edge biomedical applications. His current research employs topological data analysis and geometric methods to solve complex biological imaging problems including 3D segmentation of hematopoietic stem cells, receptor dynamics analysis, and retinal degeneration quantification. This interdisciplinary approach integrates deep mathematical theory with practical biological challenges through close partnerships with biologists at the Institut Pasteur. Analysis of his publication trajectory reveals a decisive shift toward biomedical applications since 2016, with recent work (2022-2025) dominating in high-impact imaging journals and conferences. His research consistently applies geometric measure theory and functional analysis to innovate in cellular dynamics modeling and 3D image segmentation, demonstrating exceptional versatility across pure mathematics and translational biomedical research. Nardi actively mentors the next generation of researchers: Postdoctoral supervision: Somoballi Ghoshal (2024-present, Institut Pasteur) on persistent homology-based 3D cell segmentation in cardiac tissue. PhD supervision: Benjamin Dorra (defense March 5, 2025, Medetia/Institut Pasteur) on AI-driven histopathology for ciliopathy models, resulting in ISBI 2024 award-winning publications. He co-develops the open-source Fshapestk (Functional shapes toolkit) and collaborates with J.-C. Olivo-Marin's Quantitative Image Analysis Unit at the Institut Pasteur, maintaining strong ties between theoretical mathematics and experimental biology through ongoing joint projects in cellular dynamics and tissue imaging.

Ihor Smalمشاهده پروفایل

استادیار

• Image Analysis
• Smart Microscopy
• Artificial Intelligence
• +۷ مورد دیگر

Ihor Smal is an Assistant Professor at the Department of Biology, Faculty of Science, Utrecht University. He has held previous positions at Erasmus MC (2009-2018) and TU Delft (2019-2020). His research bridges statistical image processing, smart microscopy, and explainable AI for biological motion analysis, with applications in automated real-time imaging systems. Education: M.Sc. in Physics and Electrical Engineering, Ivan Franko National University of Lviv (1999, cum laude) Professional Doctorate in Engineering (PDEng), Eindhoven University of Technology (2005) PhD in Biomedical Imaging, Erasmus University/MC (2009) His work focuses on developing AI-driven solutions for motion registration, particle tracking, and image understanding in biological systems. He leads the NWO IMAGINE! program's Tech Work Package 2, aiming to automate imaging experiments using explainable AI. The 15 most recent publications highlight his contributions to deep learning for CT segmentation, particle tracking, diffusive state modeling, and real-time imaging optimization. Key subfields include neural networks, biomedical signal processing, and automated microscopy. Scientific Awards: VENI grant Erasmus MC Fellowship NWO BBoL grant Students: Vincent Hellebrekers Daan te Rietmole His lab at Utrecht University, accessible via www.smal.ws, collaborates with institutions like Erasmus MC and TU Delft, leveraging facilities such as the Utrecht Biology Imaging Center.

Jon Sporringمشاهده پروفایل

استاد

• Computer Science
• Mathematical Image Processing
• Medical Image Analysis
• +۶ مورد دیگر

Jon Sporring is a Professor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied image processing, stochastic geometry, and biomedical imaging. He leads research in mathematical and medical image analysis, computer graphics, and pattern recognition. Education: Ph.D. in Computer Science (1998), Master in Computer Science (1995), both from University of Copenhagen Affiliations: Pioneer AI section, Applied Geometry Lab, and Faculty of Science External Roles: Visiting professor at McGill University (2012-13), co-founder of DigiCorpus Aps (2012-16) His research integrates scale-space theory, statistical shape analysis, and advanced imaging techniques for applications in medical diagnostics, materials science, and neuroscience. Recent work focuses on 3D reconstruction, persistent homology for bias correction, and AI-driven biomedical analysis. Jon teaches computer science at all academic levels, currently offering courses in bioimaging, signal processing, and deep learning. He emphasizes collaborative projects with external partners and has held administrative roles including Vice-Chair for Research at DIKU. His 15 most recent publications reflect expertise in medical imaging, 3D modeling, and AI applications, with subfields spanning neurodegenerative disease analysis, mitochondrial ultrastructure, and multi-scale image processing. Articles demonstrate interdisciplinary impact across medicine, biology, and materials science.

Yi Panمشاهده پروفایل

استاد

• Parallel and Cloud Computing
• Wireless Networks
• Bioinformatics
• +۳ مورد دیگر

Dr. Yi Pan is a Distinguished University Professor and Chair of the Department of Computer Science at Georgia State University. He holds a B.Eng. and M.Eng. from Tsinghua University (China) and a Ph.D. from the University of Pittsburgh (USA). His research focuses on parallel computing, wireless networks, bioinformatics, and machine learning, with over 330 publications and 39 authored/edited books. Dr. Pan has organized international conferences and delivered over 40 keynote speeches globally. Education: B.Eng., Computer Engineering, Tsinghua University, 1982 M.Eng., Computer Engineering, Tsinghua University, 1984 Ph.D., Computer Science, University of Pittsburgh, 1991 Research Interests: Dr. Pan’s work spans parallel and cloud computing, wireless networks, and bioinformatics. His contributions include scalable algorithms for functional module discovery in protein networks, self-defense mechanisms for cyber threats, and efficient routing protocols for sensor networks. Recent efforts integrate AI into healthcare, such as lesion localization in OCT imaging and autism spectrum disorder diagnosis via EEG analysis. Awards & Recognition: IEEE Distinguished Speaker (2000–2002) Yamacraw Distinguished Speaker (2002) Listed in Who’s Who in America and Who’s Who in Computational Science Awards from NSF, AFOSR, IBM, and Mellon Foundation Advising & Leadership: As department chair, Dr. Pan oversees interdisciplinary collaborations between Computer Science and Computer Information Systems. He has advised numerous students and holds editorial roles in 15 journals, including several IEEE Transactions. His grants focus on advancing AI-driven healthcare, cybersecurity, and computational biology. Labs & Teams: Leads research initiatives in bioinformatics, medical AI, and parallel computing, collaborating with industry partners like IBM and Shell Oil. Active in organizing conferences such as the IEEE International Conference on Bioinformatics and Biomedicine.

Umar Razaمشاهده پروفایل

مدرس ارشد

• Internet of Things
• Machine Learning
• Wireless Sensor Networks
• +۹ مورد دیگر

Dr. Umar Raza serves as a Senior Lecturer in Networking, IoT and Smart Systems at Manchester Metropolitan University's Department of Engineering, where he concurrently manages the Cisco Network Academy. His academic career includes prior lecturing roles in Robotics and Computing at Staffordshire University, with research spanning industrial applications of emerging technologies. His educational credentials include a PhD in Wireless Sensor Networks from the University of Bradford (2014), Postgraduate Certificates in Research Methods and Professional Higher Education from Staffordshire University (2005, 2007), an MSc in Electronics and Computer Systems from Huddersfield University (1995), and a BSc in Engineering Electronics from DeMontfort University Leicester. Dr. Raza's research centers on Internet of Things applications integrated with Machine Learning across industrial, agricultural, and healthcare domains. Current projects address IoT/ML solutions for domicile care monitoring, cognitive impairment assistance, natural disaster early warning systems, and smart farming in Pakistan, with emphasis on practical implementations in real-world environments. His recent publications demonstrate a pronounced interdisciplinary trajectory, particularly at the IoT-Machine Learning nexus for healthcare innovation (cardiac diagnostics, autism support wearables) and agricultural technology. Significant contributions also appear in blockchain scalability, vehicular network security, and 5G propagation modeling, reflecting both theoretical rigor and industry-relevant problem solving. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Raza actively supervises two MSc and two PhD candidates while leading multiple funded projects: Principal Investigator for KTP projects with Kindus Solutions (completed) and Trumeter Ltd Project Advisor for KTP with RAIT Ltd Lead researcher on NATO Science for Peace Security grant for natural disaster warning systems Principal Investigator for Ignite National Technology Fund smart farming initiative in Pakistan As Cisco Network Academy Manager, he drives industry-academia collaboration in networking education, developing curricula that integrate cutting-edge IoT and cybersecurity concepts while maintaining strong professional engagement through IEEE, IET, and BCS memberships.

Juan Andrade Cettoمشاهده پروفایل

پژوهشگر

• Mobile Robotics
• Computer Vision
• Neuromorphic Systems
• +۳ مورد دیگر

Juan Andrade Cetto is a Researcher at the Institute of Robotics and Industrial Informatics (IRI), part of the Spanish National Research Council (CSIC). He previously served as Director of IRI from 2014 to 2020. His expertise spans mobile robotics, computer vision, and neuromorphic systems. He holds a BSEE from CETYS University (1993), an MSEE from Purdue University (1995), and a PhD in Robotics from the Universitat Politècnica de Catalunya (UPC, 2003), recognized with the EURON Georges Giralt Best PhD Award in 2005. Research Interests: - Mobile Robotics and Autonomous Systems - Event-based Vision and Neuromorphic Sensors - Real-time Sensor Fusion and SLAM - Control Systems for Energy Conversion (e.g., PEM Fuel Cells) - Probabilistic Methods for Robotics and Computer Vision Recent Research Trends: His work emphasizes neuromorphic computing and event-based systems for robotics applications, including optical flow estimation, ego-motion tracking, and real-time SLAM. Collaborations include EU projects like ALLIES (AI for Sustainable Development Goals) and RAMON LLULL (AIRA Postdoctoral Programme). He leads the BOMBUS project (Perception and Navigation for Agile Robotics, 2024–present). Scientific Awards: EURON Georges Giralt Best PhD Award 2005 Grants & Leadership: Principal Investigator in multiple national/EU projects including BOMBUS (CSIC) and ALLIES (European). Advising: - Current Students: José Agustín Aguilar Plazaola (Fuel Cell Control), Raquel De Frutos Arranz (Event Camera-based Collision Avoidance) - Past Students: Not explicitly listed in provided text. Labs & Teams: Active in IRI’s Robotics and Industrial Informatics research groups, focusing on applied robotics and sensor systems.

Steve W. Leungمشاهده پروفایل

استاد

• Cardiovascular Medicine
• Cardiac Imaging
• Cardiac MRI
• +۷ مورد دیگر

Steve W. Leung, MD , is a Professor in the Department of Internal Medicine at the University of Kentucky College of Medicine , where he serves as Program Director for both the Cardiovascular Medicine Fellowship and the Advanced Cardiovascular Imaging Fellowship , as well as Medical Director of Cardiology IT. He is a distinguished cardiologist with fellowships in major professional societies: FACC, FASE, FSCCT, and FSCMR. Education: BA in Biology, Johns Hopkins University (1996–2000) MD, Vanderbilt University School of Medicine (2000–2004) Internal Medicine Residency, Duke University (2004–2007) Cardiovascular Medicine Fellowship, University of Kentucky (2007–2010) Advanced Cardiovascular Imaging Fellowship, National Institutes of Health (2010–2012) Graduate Certificate in Clinical and Translational Science, University of Kentucky (2014) Dr. Leung’s research is centered on cardiovascular imaging , particularly stress cardiac magnetic resonance (CMR), myocardial strain analysis, and coronary artery disease. His work explores the prognostic value of imaging biomarkers, ethnic differences in cardiac structure, and outcomes in conditions like sepsis and infective endocarditis. He frequently collaborates with large research consortia such as the Society for Cardiovascular Magnetic Resonance (SCMR) and contributes to clinical guidelines, including the ACR Appropriateness Criteria for syncope. His recent publications show a strong focus on stress CMR perfusion imaging , risk stratification in diverse populations (including Medicare-eligible and sex-specific cohorts), and translational models of vascular disease. He has no listed advisees in the provided data, but his leadership in fellowship programs suggests active mentorship. Scientific Awards and Recognitions: Fellow of the American College of Cardiology (FACC) Fellow of the American Society of Echocardiography (FASE) Fellow of the Society of Cardiovascular Computed Tomography (FSCCT) Fellow of the Society for Cardiovascular Magnetic Resonance (FSCMR) Dr. Leung is actively involved in clinical research, grant-funded studies, and national registries such as the SPINS study. He leads initiatives in cardiology informatics and contributes to improving diagnostic accuracy and patient outcomes through advanced imaging. His work bridges clinical practice, education, and research innovation.