جست‌وجوی استاد و پژوهشگر

Imin Kaoمشاهده پروفایل

استاد

• Robotics
• Manufacturing Automation
• Dextrous Manipulation
• +۵ مورد دیگر

Imin Kao is a Professor in the Department of Mechanical Engineering at Stony Brook University and Executive Director of SUNY Korea. He leads the Manufacturing Automation Laboratory (MAL), focusing on robotics, manufacturing automation, and biomedical engineering applications. His research includes dextrous manipulation with soft fingers, wafer manufacturing processes, and intelligent fault detection systems. Kao has over 110 refereed papers and holds multiple industry-funded grants from NSF, DoE, and corporate partners. Education: Ph.D. in Mechanical Engineering, Stanford University (1991) M.S. in Mechanical Engineering, Stanford University (1986) B.S. in Mechanical Engineering, National Chung Hsing University, Taiwan (1981) Research Interests: His work bridges robotics with biomedical applications, including surgical navigation systems, 3D-printed bone scaffolds, and vibration modeling in manufacturing. He pioneered wiresaw slicing technology for silicon wafer production and developed novel surgical techniques using real-time vision systems. His recent focus includes biomimetic implants and soft-finger robotic manipulation. Awards: SUNY Chancellor’s Award of Excellence in Teaching Student Life Award at Stony Brook University Labs & Teams: Directs the Manufacturing Automation Lab (MAL) and oversees SUNY Korea’s academic operations. Collaborates with multidisciplinary teams on NSF-funded projects involving robotics, medical devices, and advanced manufacturing.

• Visualization
• Computer Graphics
• Human-Computer Interaction
• +۲ مورد دیگر

Dr. Yingcai Xiao is an Associate Professor in the Department of Computer Science at the University of Akron's College of Engineering and Polymer Science. He joined the university in 1995 after working as a Software Scientist at Intergraph. Education: Ph.D. in Computer Science, Ph.D. in Physics, M.S. in Mathematics Dr. Xiao's research focuses on Visualization , Computer Graphics , Human-Computer Interaction , and their applications in Video Game Design and Numerical Simulation . His work bridges theoretical computing with practical solutions in education, healthcare, and engineering design. His publications from 2006–2008 highlight expertise in open-source learning platforms (e.g., Sakai), virtual surgical systems using fuzzy logic, and distributed 3D CAD systems. These contributions reflect interdisciplinary interests in educational technology, medical simulation, and internet-based systems. Scientific Awards: Co-PI on NSF Grant: Interactive Learning to Stimulate the Brain's Visual Center and to Enhance Memory Retention Dr. Xiao has served as Program Chair for the International Conference on Computer Graphics, Visualization, Computer Vision, and Image Processing (2007–2021), demonstrating sustained leadership in his field. He has also co-authored a chapter in Biologically-Inspired Computing for the Arts: Scientific Data through Graphics , expanding his impact into arts-oriented computational methods.

• Diagnostic Imaging
• Breast Imaging
• Mammography
• +۴ مورد دیگر

Gopal R Vijayaraghavan is a Professor in the Department of Radiology at UMass Chan Medical School, specializing in Breast Imaging within the T.H. Chan School of Medicine. His academic career spans multiple institutions across different countries, reflecting a global perspective on medical education and practice. His educational background includes: MPH in Healthcare Management from Harvard University School of Public Health MBBS in Medicine and Surgery from Jawaharlal Institute of Postgraduate Medical Education and Research, India MD in RadioTherapy/Diagnosis from Tata Institute of Social Sciences, India Fellowship in Abdominal Imaging at University of Massachusetts Medical School Fellowship in Women's Imaging at Beth Israel Deaconess Medical Center Residency training at Armed Forces Medical College and Tata Memorial Hospital Dr. Vijayaraghavan's research focuses primarily on breast imaging, cancer detection, and diagnostic radiology techniques. His work examines the intersection of technological advances with clinical outcomes, particularly in breast cancer screening and diagnosis. He has made significant contributions to understanding mammography, ultrasound, and digital breast tomosynthesis in cancer detection, with particular attention to underserved populations. His recent work explores artificial intelligence applications in breast imaging and the impact of the COVID-19 pandemic on cancer diagnosis pathways. His publications demonstrate consistent focus on improving timeliness and accuracy of breast cancer diagnosis through advanced imaging techniques, including evaluation of screening mammograms, wireless localization technology for surgical guidance, and imaging-biology correlations in breast cancers. Dr. Vijayaraghavan's work has received recognition through significant citation counts, particularly his research on AI-based breast cancer detection (72 citations) and COVID-19 vaccination effects on diagnostic imaging (43 citations). He maintains active collaborations with researchers including Vedantham, Srinivasan; Kandil, Dina; and others, bridging clinical practice, technological innovation, and healthcare policy to improve breast cancer outcomes.

• Biomedical Engineering
• Medical Image Processing
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Zbisław Tabor is a Professor and Deputy Head of the Department of Biocybernetics and Biomedical Engineering within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in building C-2, room 411 (4th floor), with contact phone +48 12 617 46 60 and email ztabor@agh.edu.pl. He also serves on the Biomedical Engineering Discipline Council, contributing to academic governance in his field. Professor Tabor's research spans the critical intersection of biomedical engineering and artificial intelligence, with emphasis on medical image analysis and clinical decision support systems . His work focuses on: Advanced medical image segmentation using deep learning architectures (e.g., nnU-Net) Uncertainty quantification in AI-driven diagnostic tools Quantitative imaging for radiation therapy planning and monitoring Computer-aided diagnosis systems for oncology and cardiology Improving reliability of medical image annotation through computational methods Analysis of his recent publications (2022-2025) reveals a strong trajectory toward clinically applicable AI solutions. Key trends include the development of automated body composition analysis for cancer prognosis, SPECT/CT imaging protocols for targeted alpha therapy of brain tumors, and robust deep learning frameworks for wound assessment and cardiomegaly screening. His research consistently addresses multi-center validation and real-world implementation challenges in medical AI. No scientific awards are documented in the available information. There is no mention of student advising, research grants, or laboratory facilities in the provided materials.

• Respiratory Medicine
• Pulmonary Diseases
• Chest Diseases
• +۵ مورد دیگر

Professor YILMAZ BÜLBÜL is a distinguished academic at Karadeniz Technical University's Faculty of Medicine, Department of Internal Medicine specializing in Chest Diseases. With over 25 years of academic service, he has progressed through the ranks from Lecturer (1999-2001) to Assistant Professor (2001-2006), Associate Professor (2006-2011), and currently serves as Professor since 2011. His research interests span Respiratory Medicine, Pulmonary Diseases, Chest Diseases, Internal Medicine, Sleep Medicine, Pulmonary Thromboembolism, Lung Cancer , and COPD . Dr. Bülbul has made significant contributions to understanding pulmonary embolism diagnostics, COPD management, lung cancer palliative care, and respiratory complications in sleep disorders. His work often bridges clinical practice with innovative approaches including AI applications in pulmonary diagnostics. An analysis of his recent publications reveals a strong focus on real-world clinical applications, with particular emphasis on pulmonary fibrosis treatments (2025), nocturnal symptoms in COPD utilizing machine learning (2024), and pulmonary complications following thoracic surgery (2022). His research demonstrates consistent translational value with direct clinical applications in respiratory medicine. Professor Bülbul has supervised 6 theses and maintains active research collaborations across Turkey. His work has generated substantial scholarly impact with 795 citations (Scopus) and an h-index of 18. He serves as a peer reviewer for numerous SCI journals including Balkan Medical Journal and Türk Tıp Bilimleri Dergisi, contributing to scientific quality control in his field. His laboratory and clinical research focuses on pulmonary diagnostics, respiratory complications of systemic diseases, and innovative approaches to respiratory symptom management. Current work appears to integrate artificial intelligence with traditional pulmonary diagnostics, reflecting his adaptation to emerging technologies in medical research.

• Ophthalmology
• Retinal Surgery
• Medical Imaging Analysis
• +۳ مورد دیگر

Renaud Duval is a Clinical Associate Professor in the Department of Ophthalmology at the University of Montreal's Faculty of Medicine. He directs the Ophthalmology Fellowship Program and leads retinal services at the Centre universitaire d’ophtalmologie (Hôpital Maisonneuve-Rosemont). His clinical practice integrates surgical innovation, medical education, and research supervision for medical students, residents, and fellows. Education: MD CM, McGill University (2006) Residency in Ophthalmology, Université de Montréal (2011) Fellowship in Vitreoretinal Surgery, Rush University/Illinois Retina Associates (2013) His research bridges clinical ophthalmology and technology development. Primary interests include: retinal pathophysiology (age-related macular degeneration, diabetic retinopathy), surgical innovation (new instrument design, vitrectomy techniques), AI integration for automated analysis of OCT/fundus images, and tele-ophthalmology frameworks for remote diagnostics. He co-authored Canadian tele-retina screening guidelines and develops deep learning models for lesion segmentation in retinal imaging. Publications emphasize translational applications: 65% focus on surgical outcomes (vitrectomy mechanics, intraocular pressure dynamics), 25% on AI/imaging diagnostics, and 10% on infectious/inflammatory retinopathies. Recent work demonstrates strong convergence of biomechanical modeling (ocular rigidity) and AI-driven image analysis. Awards & Honors: 6× Rhett Buckler Awards (ASRS Film Festival, 2012-2019) for innovative surgical videos ASRS Junior Achievement Award (2015) Dean’s Honour List (McGill 2006, UdeM 2011) Robert Forsyth/Francis Williams Prizes (McGill, 2006) He leads or co-investigates clinical trials (6+ as PI/co-I) and secured CIHR funding for a 5-year project (2022-2026) developing an OCT-controlled subretinal drug injection system for preclinical research. As director of fellowships, he oversees advanced ophthalmology training programs. Affiliations include the Vision Health Research Network (RRSV), emphasizing collaborative retinal research.

• Surgical Robotics
• Medical Robotics
• Computer-Aided Surgery
• +۱۲ مورد دیگر

José María Sabater-Navarro is a Full Professor and Vice-Dean of the Polytechnic School at Miguel Hernández University in Elche, Spain. He serves as Director of the Master of Industrial Engineering and represents the School of Engineering of Elche on the university's Executive Board within the Department of Systems Engineering and Automation. His academic credentials include a PhD in Robotics (Summa Cum Laude, 2003) from Miguel Hernández University and an MSc in Mechanical Engineering (1998) from Universitat Politècnica València. Professor Sabater-Navarro specializes in surgical robotics with emphasis on computer-aided surgery, surgery simulation, and augmented reality applications. His research extends to medical image processing for neurosurgery and orthopedics, diabetes control systems, rehabilitation robotics, soft robotics, and haptic interfaces for telerobotic systems. He actively investigates kinematic analysis and control of parallel service robots for medical and industrial applications. His scientific recognition includes: research grant from ITQ-CSIC (1998) research grant from IMPIVA-UMH (1999) Festo award for the best project (2004) Fundeum award to the best informatics initiative (2004) «Importantes» award for Instead Technologies Ltd. (2014) He currently supervises PhD candidates Juan Rodriguez, Luis Daniel Lledó, and Alvaro García, following previous guidance of Fco. Javier Badesa Clemente, Ricardo Morales Vidal (2014), and Gema Romero Moraleda (2009). His funded projects include the diabetes control application (2014, Coordinator), MARCUS robotized platform for NOTES/SILS surgery (2011-2014, Coordinator), and the Iberoamerican network for ICT applications for people with disabilities (2012-2015, Researcher). He founded Instead Technologies Ltd. (2009) and contributes to the Neuroengineering research group at the Institute of Bioengineering.

Fiona Gilbertمشاهده پروفایل

استاد

• Breast Imaging
• Medical Imaging
• Artificial Intelligence in Radiology
• +۳ مورد دیگر

Fiona Gilbert holds the Chair of Radiology at the University of Cambridge since 2011 and serves as an Honorary Consultant Radiologist at Addenbrooke's Hospital. She currently leads as President of the European Society of Breast Imaging (EUSOBI) and chairs the breast subcommittee of the Radiological Society of North America. Her extensive leadership history includes Chair of the Academic Committee of the Royal College of Radiologists, Chair of the NCRI Imaging Advisory Group, and Chair of the Royal College of Radiologists Breast Group, with significant contributions to NICE and SIGN breast imaging guidelines. Her research centers on advancing breast imaging for early cancer detection, with core interests in breast Tomosynthesis, non-FDG radiotracers, and breast MRI for tumor physiology analysis. She pioneers risk-adapted screening protocols for women with dense breasts using multimodal imaging and variable mammography frequency. A major focus involves integrating Artificial Intelligence into radiology workflows to enhance diagnostic accuracy, reduce errors, and optimize resource allocation in cancer screening programs. Analysis of her recent publications reveals dominant themes in AI-driven screening optimization, supplemental imaging for dense breasts (BRAID trial), and preoperative MRI impact on surgical outcomes (MIPA study). She leads international initiatives like MyPeBS for personalized risk-stratified screening while addressing critical ethical and equity challenges in AI implementation. Her work consistently bridges technical innovation with clinical guideline development across European radiology practice. Professor Gilbert is embedded in the Cambridge Mathematics of Information in Healthcare (CMIH) Hub at the Centre for Mathematical Sciences, fostering interdisciplinary collaboration between mathematicians, computer scientists, and clinicians to develop computational solutions for healthcare challenges, particularly in oncology imaging and AI validation frameworks.

Jeroen van der Laakمشاهده پروفایل

استاد مهمان

• Computational Pathology
• Deep Learning
• Medical Image Analysis
• +۵ مورد دیگر

Jeroen van der Laak is a Visiting Professor at Linköping University's Faculty of Medicine, affiliated with the Department of Health, Medicine and Care (HMV) and the Department of Diagnostics and Specialist Medicine (DISP). He is a key member of the Computational Pathology Group and contributes to the Wallenberg Centre for Molecular Medicine (WCMM), focusing on advancing digital pathology through artificial intelligence and deep learning methodologies. Dr. van der Laak's research centers on computational pathology, with specific expertise in developing deep learning algorithms to improve cancer diagnostics and prognosis. His work spans multiple domains including histopathological image analysis, tumor detection, biomarker identification, and computer-aided diagnosis systems. He investigates how machine learning techniques can enhance pathologist efficiency while reducing observer bias and identifying novel prognostic indicators for personalized cancer treatment. His research addresses critical challenges in computational pathology, including the need for high-quality, large-scale histopathological datasets and clinical validation of AI models. Analysis of Dr. van der Laak's recent publications reveals a strong focus on applying deep learning to diverse pathology challenges across multiple organ systems. His work demonstrates expertise in whole-slide image analysis, tumor segmentation, cancer grading, and biomarker discovery. Notably, his research spans breast cancer, prostate cancer, colorectal cancer, kidney transplant pathology, and gynecological pathology, showing both depth in specific applications and breadth across medical specialties. Key themes include attention-based image compression, human-in-the-loop refinement of AI models, and automated lesion scoring systems. Dr. van der Laak actively collaborates with international pathologists and participates in major European initiatives like the BIGPICTURE project, which aims to build an EU-scale digital pathology repository. His research emphasizes clinical validation of AI models to ensure safety and utility in routine practice, with contributions to consensus studies including recommendations for diagnosing serous tubal intraepithelial carcinoma (STIC) and standardization of molecular pathology education. As part of the Computational Pathology Group, Dr. van der Laak contributes to developing, validating, and deploying novel medical image analysis methods based on deep learning technology, with a focus on computer-aided diagnosis systems that directly support pathologists in their daily work while identifying potential new biomarkers for individualized treatment approaches.

• Physics-based statistical signal processing
• Subsurface sensing
• Auditory prostheses
• +۶ مورد دیگر

Leslie M. Collins is a distinguished Professor of Electrical and Computer Engineering, Professor of Biomedical Engineering, and Professor in the Department of Head and Neck Surgery & Communication Sciences at Duke University's Pratt School of Engineering. She is also a Faculty Network Member of the Duke Institute for Brain Sciences, demonstrating her interdisciplinary reach across engineering, medicine, and neuroscience. Dr. Collins earned her BSEE from the University of Kentucky, followed by MSEE and PhD degrees from the University of Michigan, Ann Arbor. After working as a Senior Engineer at Westinghouse Research and Development Center from 1986 to 1990, she joined Duke University in 1995 as an Assistant Professor, was promoted to Associate Professor in 2002, and to full Professor in 2007. Her research spans physics-based statistical signal processing, subsurface sensing, auditory prostheses, and pattern recognition. Dr. Collins has made significant contributions to landmine detection systems, having been part of the team that transitioned MURI-developed algorithms to the Army HSTAMIDS and GSTAMIDS systems. Her work on cochlear implants and brain-computer interfaces represents groundbreaking advances in auditory prosthetics and neural engineering. She has secured substantial research funding from agencies including ARO, NVESD, SERDP, ESTCP, NSF, and NIH. Analysis of her recent publications reveals a strong trajectory toward integrating deep learning with traditional signal processing approaches across multiple domains. Her work bridges military applications (landmine detection), medical technology (cochlear implants, heart monitoring), and infrastructure analysis (power grid mapping), demonstrating remarkable versatility in applying core signal processing principles to diverse real-world problems. DoD UXO Cleanup Project of the Year (2000) Member of Tau Beta Pi honor society Member of Sigma Xi honor society Member of Eta Kappa Nu honor society Dr. Collins has been a dedicated mentor, graduating 15 PhD students as of 2015. Her research has been supported by numerous grants from major federal agencies including ARO, NVESD, SERDP, ESTCP, NSF, and NIH, reflecting the significance and impact of her work across defense, environmental, and medical applications. She has played a key role in translating research into practical systems, particularly in landmine detection where her work has directly impacted military operations. Her research group operates at the intersection of multiple laboratories and teams, including collaborations with the Duke Institute for Brain Sciences and various medical departments at Duke. This interdisciplinary approach enables her to tackle complex problems that span traditional academic boundaries, from developing algorithms for landmine detection to creating improved speech processing for cochlear implant users.

• Medical Image Processing
• Computer-Aided Diagnosis
• Organ Segmentation
• +۳ مورد دیگر

Özgür Dandin is a researcher at Arel University specializing in medical image processing and computer-aided diagnosis systems. His work primarily focuses on developing automated segmentation algorithms for abdominal organs, particularly in trauma cases, with publications spanning from 2015 to 2020. His research interests include: Medical image segmentation and analysis Computer-aided detection systems for trauma diagnosis Organ volume calculation methods Application of deep learning in medical imaging Development of software tools like ManSeg 2.6b for radiological assessment Dandin's publication record demonstrates consistent focus on improving diagnostic accuracy through computational methods across various abdominal organs including kidneys, liver, spleen, and intestines. His work bridges engineering and clinical applications, collaborating with medical professionals to ensure clinical relevance. Key contributions include automated segmentation methods for injured organs, novel approaches for hematoma volume calculation, validation studies of medical image analysis tools, and applications of convolutional neural networks in polyp classification. His scientific collaborations are primarily with Gökalp Tulum, Onur Osman, and Tuncer Ergin, resulting in numerous publications in IEEE conferences and journals. The consistent publication record from 2015 through 2020 indicates active engagement in the field of medical image analysis with practical applications in emergency medicine, radiology, and surgical specialties.

Nick Greenمشاهده پروفایل

پژوهشگر

• Biomedical Engineering
• 3D Printing
• Orthopaedic Surgery
• +۴ مورد دیگر

Nick Green is a Biomedical Engineer affiliated with the Royal Brisbane and Women’s Hospital (RBWH) and Queensland University of Technology (QUT) , specializing in orthopaedic surgery and 3D medical printing. He holds a Masters in Engineering (Research) and collaborates with A/Prof Kevin Tetsworth on 3D printed surgical models for over 100 cases. Role: Biomedical Engineer in Orthopaedic Surgery Institutions: RBWH (clinical), QUT (academic) Focus: 3D printing, patient-specific implants, surgical navigation Research Interests: His work bridges orthopaedics and biomedical engineering , with a focus on 3D printing for preoperative planning, segmentation techniques , and implant design . Recent trends in his publications highlight augmented reality in shoulder surgery, biomechanical modeling , and surgical tool innovation . Labs & Collaborations: Nick collaborates with multidisciplinary teams across neurology , maxillofacial surgery , and vascular surgery , with a strong emphasis on shoulder surgery and bio-printing . He also explores commercialization pathways for medical devices.

Jared Wohlgemutمشاهده پروفایل

پژوهشگر ارشد

• Trauma Sciences
• Emergency General Surgery
• Clinical Decision Support Systems
• +۳ مورد دیگر

Jared Wohlgemut is a Research Fellow at the Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London. He studied Medicine at the University of Aberdeen and holds an MSc in Trauma Sciences (with Distinction) from Queen Mary University of London. His career includes the Academic Foundation Programme (Mersey), Core Surgical Training (East of Scotland), and Specialist Training in General Surgery (West of Scotland). His research focuses on developing clinical decision support tools using Bayesian Network prognostic models for trauma care in forward healthcare settings. He is actively involved in the COMBAT-AID project, a collaboration between the Centre for Trauma Science and the Risk and Information Management groups at Queen Mary University of London. His work bridges academic trauma research with computational methods to improve decision-making in emergency surgical contexts.

Kimon Divarisمشاهده پروفایل

استاد

• Pediatric Dentistry
• Dental Caries
• Public Health
• +۷ مورد دیگر

Kimon Divaris is a Professor and James Bawden Distinguished Professor at the Adams School of Dentistry, University of North Carolina at Chapel Hill , where he serves as Chair of the Department of Pediatric Dentistry and Dental Public Health. His work bridges clinical practice, public health, and biological research, focusing on early childhood caries, oral microbiome, and genomics. Postdoctoral Fellowship: Oral and Genetic Epidemiology, UNC-Chapel Hill (2012) Certificate: Pediatric Dentistry, UNC-Chapel Hill (2011) PhD: Epidemiology, UNC-Chapel Hill (2011) DDS: National and Kapodistrian University of Athens (2005) His research explores the intersection of clinical, public health, and biological determinants of oral health, with NIH-supported studies including genome-wide association analyses and microbiome investigations. He leads the ZOE 2.0 study (U01-DE025046) and contributes to the Pediatric HIV/AIDS Cohort Study (U01-HD052102). Recent publications emphasize machine learning applications, microbial biomarkers, and precision oral health frameworks. Scientific awards include the Burton C. Borgelt Faculty Advisory Award (2022), IADR Distinguished Scientist Young Investigator Award (2020), and IADR Centennial Emerging Leader Award (2020). He has authored over 130 publications, including groundbreaking work on fibrin's role in oral inflammation in Science (2021). His mentorship philosophy emphasizes creating intellectually stimulating environments for training the next generation of oral health professionals.

• Orthopedics
• Sports Medicine
• 3D Biomechanical Modeling
• +۷ مورد دیگر

Eline van Es is a Researcher at Erasmus MC in the Department of Orthopedics and Sports Medicine . Her work focuses on 3D biomechanical modeling, corrective osteotomy techniques, and bone morphology analysis. Research interests include: 3D surgical planning for orthopedic procedures Bone growth dynamics in pediatric patients Biomechanical analysis of upper limb deformities Computational modeling for osteotomy corrections Publication trends : Recent articles emphasize 3D technology applications in orthopedic surgery, with specific focus on forearm malunion correction (2025), bone growth modeling (2025), and automated landmark identification (2024). Research spans both adult and pediatric populations, covering diagnostic imaging, surgical simulation, and biomechanical analysis.