جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Biomedical Imaging
• +۵ مورد دیگر

Despina Kontos, PhD is the Herbert and Florence Irving Professor of Radiological Sciences at Columbia University Irving Medical Center (CUIMC), with appointments in the Department of Radiology and the Herbert Irving Comprehensive Cancer Center. She serves as the Chief Research Information Officer for CUIMC, Vice Chair of Artificial Intelligence and Data Science Research in the Department of Radiology, and Director of Biomarker Imaging at NewYork-Presbyterian Hospital. Additionally, she holds appointments in the Departments of Biomedical Informatics and Biomedical Engineering. Dr. Kontos received her educational training from prestigious institutions: BS in Engineering from the University of Patras, Greece MSc and PhD in Computer and Information Sciences from Temple University Postdoctoral training in Radiology at the University of Pennsylvania Certificates in Biostatistics and Epidemiology from UPenn, Cancer Biology from Harvard, and AI for Decision Making from Wharton As a computer scientist with expertise in artificial intelligence and machine learning, Dr. Kontos focuses on developing computational methodologies to leverage imaging as quantitative biomarkers for personalized disease prediction, particularly in cancer. Her research program investigates how imaging data can be mined to extract sophisticated phenotypic signatures with diagnostic, prognostic, and predictive value. While her primary focus has been on breast cancer, her lab also pursues related research in lung cancers, evaluating the integration of CT radiomic features with liquid biopsy data to characterize tumor heterogeneity. Dr. Kontos founded and directs Columbia University's Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), a multidisciplinary center dedicated to developing and integrating quantitative imaging and non-imaging biomarkers for personalized disease prediction. Through CIMBID, she has built a vibrant scientific ecosystem that brings together expertise across Columbia's campuses, linking basic science, engineering, clinical medicine, public health, and health services research. Analysis of Dr. Kontos's publication record reveals a strong focus on applying AI and machine learning to biomedical imaging, particularly for cancer risk prediction and personalized treatment. Her work demonstrates a progression from foundational methodological development to clinical translation, with increasing emphasis on multi-modal biomarker integration. Recent publications show expansion into new disease areas including Alzheimer's disease prediction, while maintaining her strong focus on breast and lung cancer applications. Dr. Kontos has received significant recognition for her contributions to the field: Academy for Radiology and Biomedical Imaging Research Distinguished Investigator Award (2020) Eastern Cooperative Oncology Group - American College of Radiology Imaging Network ECOG-ACRIN Young Investigator Award of Distinction for Translational Research (2014) Dr. Kontos has been highly successful in securing research funding, with numerous grants from federal agencies including the National Institutes of Health (NIH) and the Department of Defense (DOD), as well as private foundations such as the American Cancer Society (ACS) and the Radiological Society of North America (RSNA). Her leadership extends to mentoring students and postdoctoral researchers through her roles at CIMBID and the Department of Radiology. As the founding director of CIMBID, Dr. Kontos leads a multidisciplinary team that includes the Computational Imaging Biomarker Group (CBIG), the Laboratory of AI and Biomedical Science (LABS), and several other affiliated research labs. The center leverages Columbia's institutional strengths in engineering, data science, and clinical medicine to advance personalized healthcare through AI and imaging technologies.

Delphine Périé-Curnierمشاهده پروفایل

استاد

• Biomedical Technology
• Biomechanics
• Solid Mechanics
• +۶ مورد دیگر

Delphine Périé-Curnier is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal and Director of Graduate Studies. Her research focuses on developing quantitative MRI techniques for non-invasive characterization of living tissue mechanical properties, particularly in cardiotoxicity detection and musculoskeletal mechanobiology . She leads the Bioperformance Analysis and Innovation Laboratory (LIAB) and contributes to the Institute of Biomedical Engineering. Education: Ph.D. from Paul Sabatier University, Toulouse, France Her work bridges medical imaging , biomechanical modeling , and finite element analysis to predict disease progression through pathomechanism understanding. Key projects include exercise-induced cardiac changes in childhood cancer survivors and spinal biomechanics in scoliosis. Recent publications (2023-2024) emphasize cardiovascular MRI for childhood cancer survivorship and hemodynamic modeling in left ventricle analysis. She supervises 26 graduate students, with completed theses spanning topics like doxorubicin cardiotoxicity , knee replacement stability , and spatial cardiac MRI protocols . Teaching includes graduate courses in biomedical design , advanced biomechanics , and modeling techniques .

• Signal Processing
• Computer Vision
• Machine Learning
• +۶ مورد دیگر

Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.

Stephen Laneمشاهده پروفایل

استاد آموزشی

• Computer Graphics
• Computer Animation
• Virtual Reality
• +۵ مورد دیگر

Stephen H. Lane is a Teaching Professor at the University of Pennsylvania's School of Engineering and Applied Science, Department of Computer and Information Science. He serves as Director of the Computer Graphics and Game Technology (CGGT) Master's Program and teaches courses such as Computer Animation (CIS462/562), Advanced Topics in Computer Graphics and Animation (CIS660), and Game Design and Development (CIS564). He also supervises the Game Design Practicum (CIS568) capstone course. Education: B.S. in Mechanical and Aerospace Engineering from Cornell University (1980) M.S. in Systems Engineering from UCLA (1982) Ph.D. in Mechanical and Aerospace Engineering from Princeton University (1988) Dr. Lane's research focuses on the intersection of robotics, physically-based character animation, embodied intelligent agents, and virtual reality user interfaces. His work integrates control theory, artificial intelligence, and computer animation techniques to develop advanced simulation and training systems. His publications since 1987 cover topics such as inverse kinematics, neural networks for motion control, B-spline receptive fields, robotic skill acquisition, and gesture recognition systems. His recent work (2010-2011) emphasizes sensor fusion for gesture recognition and immersive training interfaces. Scientific Awards: Co-inventor of four US patents related to robotic animation and motion control systems Contributions to hybrid controller hierarchies and neural network training methods As founder of soVoz, Inc., Dr. Lane commercializes behavioral animation technology for virtual environments. His academic-industry collaboration includes contracts with Microsoft, Disney, and the US Army. He has developed tools like ProScena™ to integrate interactive 3D simulation capabilities into gaming and training applications.

• Biometrics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Anthony Clarkمشاهده پروفایل

استادیار

• Autonomous Robotics
• Evolutionary Robotics
• Computer Vision
• +۷ مورد دیگر

Anthony Clark is an Assistant Professor of Computer Science at Pomona College, where he has been teaching since 2020. Previously, he served as an Assistant Professor at Missouri State University from 2016 to 2020. He directs the ARCS (Autonomous Robotics and Complex Systems) Lab, which focuses on improving the robustness and adaptability of autonomous robots, particularly small-scale systems that can navigate unpredictable terrain and adapt to potential damage. Clark earned his Ph.D. in Computer Science from Michigan State University in 2016, where he worked under Dr. Philip K. McKinley, and his B.S. in Computer Engineering from Kansas State University, graduating magna cum laude. His research centers on making autonomous robots more robust and adaptive through optimization algorithms and multimodal systems. He specializes in evolutionary robotics, computer vision, neural networks, and simulation methods for developing control systems that leverage multiple locomotion mechanisms. His recent work demonstrates strong trends across several domains: developing hybrid locomotion systems (wheel/leg transformations), applying deep learning to terrain classification and pathfinding, using simulation environments for training, and exploring pretraining techniques for evolutionary robotics. His research shows a consistent focus on bridging simulation and real-world applications while addressing challenges in robot adaptability and robustness. Faculty Excellence in Teaching, Missouri State University (2018) Best Paper Award, Workshop on Evolutionary and Reinforcement Learning (2013) Best Paper Award, ALIFE Conference, Behavior and Intelligence Track (2012) Outstanding Reviewer, Elsevier (2018) Master Advisor Certification, Missouri State University (2017) Clark has advised numerous undergraduate and graduate students through the ARCS Lab, with current research involving projects like the Adabot (a robot with multiple locomotion mechanisms) and thermal semantic segmentation for aerial field robots. His teaching portfolio includes courses on data structures, algorithms, neural networks, computer systems, and mobile robotics. He has also served as a Visiting Associate at Caltech's ARC Lab from 2023-2024, working with Dr. Soon-Jo Chung. The ARCS Lab develops simulation environments, optimizes control systems, and fabricates physical robots. Current projects include the Adabot with its geared coaxial shaft mechanism for hybrid locomotion, thermal semantic segmentation using satellite data, and creating dynamic simulation environments with Unreal Engine 5. The lab emphasizes practical applications of theoretical research while training students in both hardware and software aspects of robotics.

Lihi Zelnik-Manorمشاهده پروفایل

استاد

• Haptics
• Robotics
• Computer Vision
• +۴ مورد دیگر

Lihi Zelnik-Manor is a Professor at the Faculty of Electrical and Computer Engineering at the Technion - Israel Institute of Technology . Her research focuses on digitizing the sense of touch, integrating Haptics , Robotics , and Computer Vision to create digital representations of physical properties and develop haptic feedback devices for virtual interactions. Executive Vice President for Innovation and Industry Relations (2023-2026) Vice Dean for Graduate Studies (2022-2023) General Chair: CVPR’21, ECCV’22 Her work spans Neural Architecture Search (NAS) , 3D Reconstruction , and Image Processing , with recent publications on haptic devices (2025), diffusion models (2025), and soft-tissue simulation (2024). She actively contributes to academic leadership through roles in top conferences and community initiatives like the Schmidt Postdoctoral Award steering committee.

Danijel Karolyiمشاهده پروفایل

استاد

• Animal Science
• Meat Quality Assessment
• Pig Breeds
• +۷ مورد دیگر

Prof. Danijel Karolyi is a Full Professor at the Department of Animal Science, Faculty of Agriculture, University of Zagreb, with a focus on meat quality, genetic diversity in pig breeds, and animal welfare. He has held academic roles since 1998, advancing from Assistant to Professor in 2018. His expertise spans traditional meat product technology, stress physiology, and conservation of local livestock breeds. PhD in Animal Science, University of Zagreb (2003-2007) Master’s in Animal Science, University of Zagreb (1998-2002) Graduate thesis, University of Zagreb (1990-1998) His research centers on the genetic and nutritional factors influencing carcass and meat properties, with emphasis on local breeds like the Turopolje and Black Slavonian pigs. He investigates stress adaptation, proteolysis/lipolysis during curing, and sustainable production systems. Recent work includes EU-funded genomic studies and optimizing traditional processing techniques. Article trends reflect interdisciplinary approaches to meat science, combining genetics, food chemistry, and socio-economic analysis. Topics include fatty acid profiling, geographical indications, cross-fostering impacts, and disease effects on biodiversity. Collaborative EU projects dominate his recent publications. Editorial board member, Meso journal (2008-2021) Animal Science Section Editor, Agriculturae Conspectus Scientificus (2016-2020) Commission member for EU PGI label specifications for Drniš ham and Slavonian kulen Prof. Karolyi has coordinated university projects on animal genetics and quality assessment. He serves as a consultant for Croatian agricultural agencies and has provided expert training in carcass evaluation and animal welfare. His professional activities include workshops across Europe on meat processing and conservation of animal genetic resources.

Curtis Bakerمشاهده پروفایل

استاد

• Visual Perception
• Neurophysiology
• Psychophysics
• +۱۲ مورد دیگر

Curtis Baker is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC), Montreal General Hospital site, and a Professor in the Department of Ophthalmology and Visual Sciences at McGill University. His research focuses on understanding human visual perception , particularly low-level neural mechanisms that underpin everyday visual processing of figure-ground and local depth relationships through cues like contrast, texture, and motion . Institutional Affiliation: RI-MUHC, McGill University Academic Rank: Professor His lab employs human psychophysics , electrophysiology , optical imaging , and computational modeling to study how early visual processing detects and utilizes complex cues. Key research areas include receptive field dynamics , second-order boundary perception , and machine learning applications in visual neuroscience. Recent publications highlight his work on convolutional neural networks for receptive field estimation, Y-like neuronal responses in human vision, and texture regularity models using wavelet analysis. Collaborations span computational neuroscience , optical imaging , and depth perception mechanisms. Students and researchers in his lab include graduate students in Integrated Program in Neuroscience , Physiology , and Biomedical Engineering , alongside alumni contributing to neurophysiology and computational biology projects.

Aoife Gowenمشاهده پروفایل

استاد

• Hyperspectral Imaging
• Sustainable Transport Policy
• Critical Thinking in Education
• +۴ مورد دیگر

Professor Aoife Gowen is a leading academic at the UCD School of Biosystems & Food Engineering , specializing in hyperspectral imaging and its applications across medicine, food safety, and engineering. Her research, supported by prestigious European Research Council (ERC) funding, investigates water molecule interactions with surfaces to improve bone graft materials and develop innovative diagnostic tools for prostate cancer. She also leads Science Foundation Ireland (SFI)-funded projects on hyperspectral monitoring of bacterial growth for food safety. Beyond technical research, Professor Gowen has developed computational tools now integrated into commercial chemical analysis software. Her work spans interdisciplinary domains, including sustainable transport policy, critical thinking education, and promoting gender diversity in engineering. As a key figure in the Women on Walls initiative, she has enhanced visibility for women in STEM fields. Her recent publications focus on spectral technologies for food quality, microplastics characterization, and medical diagnostics, reflecting her commitment to addressing global challenges in health and sustainability. Scientific Awards: ERC Grant for water-surface interaction research Professor Gowen actively collaborates with European networks and industry partners, driving advancements in hyperspectral imaging applications. Her lab’s efforts to bridge computational science with real-world chemical analysis have positioned her as a pioneer in invisible chemistry visualization, impacting medicine, food, and environmental engineering.

• Hyperspectral Imaging
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Suhad Al-Khafaji is a Research Fellow in Hyperspectral Spectroscopy at Griffith University's School of Environment and Science (Chemistry and Forensic Science). Their research focuses on hyperspectral imaging, machine learning, and computer vision applications in agriculture, environmental monitoring, and material analysis. Al-Khafaji is affiliated with the Australian Rivers Institute and previously the Institute for Integrated and Intelligent Systems (2015-2020). They hold an ORCID identifier (0000-0002-7986-4308) and are located at N44 1.26, Nathan Campus. Research Interests: Hyperspectral imaging for agricultural quality assessment (e.g., macadamia moisture analysis) Spectral-spatial boundary detection algorithms in multispectral datasets Machine learning integration with computer vision techniques Nanotechnology applications in imaging systems Cognitive psychology aspects of pattern recognition Recent Work Trends: Recent articles emphasize hyperspectral image processing innovations, particularly boundary detection and feature extraction for agricultural and environmental applications. Their 2024 work applies machine vision to macadamia quality prediction, while earlier contributions (e.g., 2022) refine spectral-spatial analysis methodologies. Labs/Teams: Active member of Griffith's Australian Rivers Institute and former affiliate of the Institute for Integrated and Intelligent Systems.

• Geographic Information Systems
• Spatial Epidemiology
• Natural Disaster Management
• +۵ مورد دیگر

Samsung Lim serves as an Associate Professor of geographic information systems (GIS) in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW) Sydney. With expertise spanning data science, artificial intelligence, and machine learning, Lim applies geospatial technologies to critical real-world challenges in natural disaster management and public health research. Lim's interdisciplinary work bridges engineering, computer science, and public health domains to develop practical decision-making tools for emergency response and disease surveillance. Ph.D. in Aerospace Engineering and Engineering Mechanics, University of Texas, Austin, TX, USA M.A. in Mathematics, Seoul National University, Seoul, South Korea B.A. in Mathematics, Seoul National University, Seoul, South Korea Lim's research focuses on applying GIS to natural disaster management and public health challenges. Key areas include machine learning methods for bushfire susceptibility mapping, spatial clustering for landslide susceptibility analysis, city-scale evacuation management in flood scenarios, and social media-based natural disaster assessment. In public health, Lim investigates geo-correlations between environmental factors and asthma occurrence, computational approaches to avian influenza outbreaks, emerging hot spot analysis of COVID-19, and early detection systems for emerging infectious diseases. This work combines advanced spatial analytics with machine learning to address complex environmental and health challenges. The recent publication record demonstrates a clear interdisciplinary trajectory where geospatial science intersects with public health emergency response and natural hazard management. Lim's work consistently applies machine learning techniques to geospatial data, with particular emphasis on disaster susceptibility mapping, disease outbreak detection, and infrastructure monitoring. The research spans multiple continents and addresses both immediate emergency response needs and long-term environmental health challenges, reflecting a commitment to practical applications of geospatial science. Associate Editor of Geospatial Information Science National Delegate of Commission 3 of International Federation of Surveyors (FIG) National Representative of the International Cartographic Association (ICA) Commission on Sensor-driven Mapping Senior Member of Institute of Electrical and Electronics Engineers (IEEE) Lim actively contributes to the development of early warning systems for emerging infectious diseases through collaborations with public health researchers. The work on EPIWATCH demonstrates how AI can enhance surveillance capabilities for outbreak detection. Lim's research on cruise ship transmission of diseases and the spread of avian influenza through bird migration patterns and poultry trade networks shows strong engagement with real-world public health challenges. These projects often involve multidisciplinary teams spanning engineering, computer science, epidemiology, and veterinary medicine. Lim's work integrates multiple geospatial data sources and analytical techniques to address complex environmental and public health challenges. This includes developing frameworks for performance analysis of OpenStreetMap data, creating specialized road datasets for pedestrian navigation, and applying Persistent Scatterer Interferometry for land motion monitoring. The research combines traditional geospatial methods with cutting-edge machine learning approaches to extract meaningful insights from complex spatial datasets.

Lluis Casas Duocastellaمشاهده پروفایل

استاد پژوهشی

• Archaeometry
• Archaeological Materials Analysis
• Historical Geomagnetism
• +۳ مورد دیگر

Lluis Casas Duocastella is a Research Professor in the Department of Geology at Universitat Autònoma de Barcelona (UAB). His research focuses on archaeometry, geological analysis of archaeological materials, and historical geomagnetism. He has led major projects like Arqueomagnetismo y otras técnicas analíticas aplicadas a la valorización del patrimonio arqueológico e histórico (2014-2018) and contributed to studies on Roman Hispania landscapes and Catalan megalithic sites. Education: Ph.D. in Materials Science (UAB, 2003), M.Sc. in Translation/Interpretation (UAB, 2010), B.Sc. in Geology (UAB, 1997) Research interests include provenance analysis of archaeological materials, application of machine learning in material discrimination, and technological characterization of historical artifacts. Recent work emphasizes Roman glass technology and volcanic rock studies in Tunisia and Spain. He collaborates internationally with institutions like the University of Naples and University of Liverpool.

Dana Cobzasمشاهده پروفایل

دانشیار

• Computer Vision
• Medical Imaging
• Image Segmentation
• +۷ مورد دیگر

Dana Cobzas is an Associate Professor at the MacEwan University in the Department of Computer Science , with adjunct appointments at the University of Alberta. Her academic journey includes a PhD in Computer Science (University of Alberta, 2004) MSc in Mathematics (Babes-Bolyai University, 1998) BSc in Mathematics (Babes-Bolyai University, 1997) . Her research focuses on imaging and computer vision , particularly mathematical models for medical image processing . Key areas include Medical image segmentation and registration 3D modeling from uncalibrated images Sparse classification for population studies Dynamic vision (tracking and modeling) Medical applications in neuroimaging and oncology . She has developed advanced techniques like deep learning-integrated level set methods and FEM-based segmentation. Scientific recognition includes: NSERC Discovery Grant (2015, 2010) Best Vision Paper at IEEE ICRA 2005 Best Student Paper at Vision Interface 2003 . She is actively involved in teaching and mentoring , with experience supervising senior students’ independent studies and contributing to collaborative projects in robotics and biomedical engineering .

Dimitris Maroulisمشاهده پروفایل

استاد

• Data Acquisition
• Real-Time Systems
• Pattern Recognition
• +۶ مورد دیگر

Dimitris Maroulis is a Professor at the Department of Informatics and Telecommunications, University of Athens, leading the Real-Time Systems and Image Analysis Lab (RTS-image). With over 20 years of experience in data acquisition and real-time systems, and 15 years in image/signal analysis, he collaborates extensively with Greek and European hospitals in biomedical informatics. He has led 5 R&D projects and authored 150+ papers with 1400+ citations. University of Athens: Professor (2000–present) Meudon Observatory: Research Fellow (3 years) & Long-term Collaborator (10+ years) Research Interests focus on real-time systems , image/signal processing , and biomedical applications . Key areas include automated segmentation of proteomic images, noise removal methods, and stereo image coding. His 15 most recent publications (2003–2012) span 3D imaging , medical image analysis , and biomedical informatics , with sub-fields like autostereoscopic displays, wavelet-based coding, and computer-aided diagnosis. Awards : Best Paper Award (2012: Integral Image Analysis) Projects include European and national R&D initiatives in image analysis and real-time systems. Labs : RTS-image Lab develops methodologies for biomedical and proteomic applications.