جست‌وجوی استاد و پژوهشگر

• Colorectal Cancer Surgery
• Gastrointestinal Endoscopy
• Medical Artificial Intelligence
• +۳ مورد دیگر

Mustafa Bulut serves as Clinical Associate Professor at the University of Copenhagen's Faculty of Health and Medical Sciences within the Department of Clinical Medicine. His clinical and research activities are centered at Regionshospitalet Køge and Rigshospitalet in Copenhagen, with affiliations to both surgical and gastrointestinal research units. His research focuses on innovative approaches in colorectal cancer management, including electroporation therapy, AI-assisted endoscopic diagnostics, and hybrid surgical techniques. Key areas include neoadjuvant immunotherapy trials, mortality prediction models using data-driven approaches, and development of novel endoscopic procedures for high-risk patients. His work bridges clinical practice with technological innovation in gastrointestinal medicine. Recent publications demonstrate strong emphasis on translational research, with multiple studies on calcium electroporation for tumor ablation and AI applications in colonoscopy. His collaborative network includes prominent Danish surgical oncology centers and international research groups focused on improving colorectal cancer outcomes through technological integration. Professor Bulut maintains active clinical practice while contributing to medical literature through high-impact publications in surgical oncology and endoscopy journals. His current research trajectory shows increasing focus on data science applications in surgical decision-making and minimally invasive cancer treatment modalities.

Flemming Jannik Vind Bjerrumمشاهده پروفایل

دانشیار بالینی

• Surgical Simulation
• Artificial Intelligence in Surgery
• Robotic Surgery Training
• +۳ مورد دیگر

Flemming Jannik Vind Bjerrum holds a dual appointment as Clinical Associate Professor at the University of Copenhagen's Department of Clinical Medicine and as a surgeon at the Capital Region of Denmark, primarily affiliated with Amager and Hvidovre Hospital and Copenhagen Academy for Medical Education and Simulation. His work bridges clinical practice and academic research in surgical innovation. His research focuses on simulation-based training and competency assessment for minimally invasive and robotic surgery. Key areas include development of AI algorithms for surgical gesture annotation, virtual reality simulation modules, and evidence-based training protocols. His fingerprint analysis reveals dominant expertise in surgeon training , systematic reviews , laparoscopy , and simulation training , with significant contributions to surgical AI applications. Recent publications (2024-2025) show a strong trend toward artificial intelligence integration in surgical training and assessment, particularly in robotic surgery gesture analysis, bronchoscopy, and bowel preparation evaluation. Over 80 research outputs demonstrate consistent productivity, with 17 publications in 2024 alone indicating active current research. No scientific awards are publicly documented in the provided materials. His advising activities and grant funding are not explicitly detailed in available sources, though his leadership in multicenter trials (e.g., robotic cardiac surgery simulation assessment) suggests collaborative grant involvement. His work with the Copenhagen Academy for Medical Education and Simulation indicates institutional leadership in surgical education infrastructure. Bjerrum co-develops simulation technologies including virtual reality modules for canine surgery and AI-driven assessment tools, operating within interdisciplinary teams at Copenhagen Academy for Medical Education and Simulation. His research network shows extensive international collaboration, particularly in surgical AI standardization efforts.

Lorin Maletskyمشاهده پروفایل

استاد

• Biomechanics
• Machine Design
• Biomedical Engineering
• +۴ مورد دیگر

Lorin Maletsky is a Professor and Associate Department Chair in Mechanical Engineering at the University of Kansas. His work bridges biomechanics and machine design, focusing on experimental testing of cadaveric joints and prosthetic devices. He leads the Experimental Joint Biomechanics Laboratory and collaborates with orthopedic surgeons, companies, and researchers globally. Research Interests : Development of dynamic knee simulators for prosthetic evaluation Biomechanical testing of implants and biological systems Finite element modeling and tribological analysis Kinematic alignment in total knee arthroplasty Medical device design and validation Key Article Trends : His research spans biomechanics, orthopedic engineering, and machine design. Articles emphasize knee joint mechanics, prosthetic wear testing, kinematic modeling, and integration of computational/experimental methods. Topics include ligament constraints, dynamic loading, and aging-related orthopedic challenges. Scientific Awards : Kemper Fellowship for Teaching Excellence Sharp Teaching Fellowship Grants & Collaborations : Funded by the orthopedic industry, National Science Foundation, and the Center for Disease Control. Collaborations include clinical programs, orthopedic companies, and interdisciplinary teams. Laboratory : The Experimental Joint Biomechanics Laboratory at the University of Kansas focuses on physiological loading, wear analysis, and kinematic evaluation of joint prosthetics using cadaveric models and dynamic simulators.

• Speech Perception
• Cognitive Effort
• Acoustic Phonetics
• +۱۱ مورد دیگر

Dr. Alexander Francis is an Associate Professor in the Department of Speech, Language, and Hearing Sciences at Purdue University's Auditory Perceptual Learning Laboratory , within the College of Health and Human Sciences. His work bridges linguistics, cognitive psychology, and auditory neuroscience , focusing on phonetic learning and the interplay between perceptual experience and speech processing. Education: Ph.D. in Linguistics from the University of Chicago (1999). His research investigates how native and non-native listeners adapt to speech sounds across diverse conditions, including: Speech perception in noisy environments Lexical tone acquisition in tone and non-tone language speakers Impact of hearing impairment on auditory processing Psychoacoustic mechanisms in selective attention Development of native-like speech perception in bilinguals Applications of speech technology for hearing aids and cochlear implants Recent publications highlight his interdisciplinary approach, spanning speech processing (2025) , autonomic arousal analysis (2024) , and neurodegenerative speech patterns (2023) . His work intersects with cognitive science, biomedical engineering, and environmental psychology. Lab Affiliation: Auditory Perceptual Learning Laboratory at Purdue University.

• Brain-Computer Interface
• Brain Imaging
• Machine Learning
• +۲ مورد دیگر

Amad Zafar is a Research Professor at Sejong University's Department of Artificial Intelligence and Robotics. He holds a PhD in Intelligent Control & Automation from Pusan National University (2019) and has previously held academic positions at the University of Wah and University of Lahore Islamabad Campus. Education: B.Sc., University of Wah, Pakistan (2009) MS., University of Wah, Pakistan (2013) Ph.D., Pusan National University, South Korea (2019) His research focuses on Brain-Computer Interfaces , Brain Imaging , Machine Learning , Intelligent Systems , and Battery Technology . His notable achievements include developing a Fast fNIRS-based BCI , Battery SOC/RUL Prediction , and Hotspot Detection in Solar Panels . Recent publications highlight trends in medical imaging analysis (diabetic retinopathy, skin lesions, breast cancer), deep learning frameworks for healthcare, reinforcement learning for traffic optimization, and hybrid neural networks for brain signal processing. All works emphasize optimization techniques and intelligent system design. Professional experience spans from Lab Engineer (2010-2013) to Research Professor (2022-Present) . Collaborations with institutions in South Korea, Pakistan, and international researchers are evident through co-authorships. Research outputs (87 total) demonstrate sustained contributions across biomedical engineering, energy systems, and AI since 2016.

• Medical Informatics
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

David Bani-Harouni is a researcher at the Chair of Computer Aided Medical Procedures at Technische Universität München (TUM). His work focuses on Medical Informatics , Artificial Intelligence , and Deep Learning , with an emphasis on Clinical Decision Support and Medical Image Analysis . Research Interests : Large Language Models (LLMs), Vision Language Models (VLMs), interpretability in deep learning, multimodal clinical decision support, and medical image analysis. Teaching : He contributes to lectures and practical courses such as Computer Aided Medical Procedures I , Medical Augmented Reality , and Deep Learning for Medical Applications . Publications : His research spans reinforcement learning for clinical decision-making, multimodal operating room datasets, toxin prediction systems (e.g., ToxNet), and graph convolutional networks for intoxication prediction. Contact : david.bani-harouni@tum.de

• Medical applications of vision-language models
• Multimodal deep learning for medical applications
• Holistic clinical decision support systems
• +۲ مورد دیگر

Matthias Keicher is a Postdoc and Research Manager at the Chair for Computer Aided Medical Procedures at the Technical University of Munich , affiliated with the IFL Lab at Klinikum Rechts der Isar. His work focuses on deploying AI for clinical applications, particularly vision-language models and large language models for structured report generation and decision support systems. Education: Dipl.-Ing. in Mechanical Engineering and Management from TUM (2006-2013) Industry Experience: Former CTO and Managing Director at SurgicEye GmbH (2016-2018) Research Interests: Medical Vision-Language Models (VQA, structured reporting) Multimodal Deep Learning for diagnostics Interpretable AI with generative models Decision support systems integrating patient data Article Trends: Over 2024-2014, his publications span surgical phase recognition (TeCNO), vertebral fracture grading (iMIMIC best paper), chest X-ray classification (FlexR), radiology report generation (RaDialog), and toxin prediction (ToxNet). Keywords include Medical Imaging, Graph Networks, Language Models , with subfields like 3D Computer Vision, Federated Learning, Clinical Reasoning . Scientific Awards: MICCAI iMIMIC 2023 Best Paper Teaching: He organizes two lectures ( Computer Science for Medical Students , Innovation Generation in Healthcare ) and tutors courses such as Deep Learning for Medical Applications and Machine Learning in Medical Imaging . Labs & Teams: Works at the IFL Lab (Intelligent Future Lab) in Munich, leading a research team funded by the DIVA project focused on vision-language models in clinical settings.

• Computer Graphics
• Deep Learning
• Virtual Reality
• +۲ مورد دیگر

Michael Sommersperger is a Researcher at the Department of Computer Science, Technical University of Munich (TUM), affiliated with the Chair of Computer Aided Medical Procedures under Prof. Nassir Navab. Based at the Garching campus, he contributes to labs including IFL Lab and NARVIS Lab within TUM's medical technology research ecosystem. He earned a Bachelor in Informatics (2014-2018) and Master in Biomedical Computing (2018-2021) from TUM, followed by a Visiting Scholar position at Johns Hopkins University (2020-2021). His research integrates computer vision and graphics with medical applications, focusing on surgical procedures and diagnostic systems. His work spans Computer Graphics, Deep Learning, and Virtual/Augmented Reality with clinical applications in ophthalmology. Key research areas include Medical Image Analysis (MEDIA), Robotics and Ultrasound (RobUSt), and Surgical Data Science, emphasizing real-time visualization for medical interventions. Michael actively teaches advanced courses including Computer Aided Medical Procedures, Medical Augmented Reality, and Introduction to Surgical Robotics through 2025/26. He leads practical courses on 3D Computer Vision and Deep Learning for Medical Applications, training students in cutting-edge medical technology development. His lab collaborations at Garching campus drive innovation in medical AR/VR systems, particularly for ophthalmic surgery simulation and intraoperative guidance. Current projects leverage generative AI and 3D vision techniques to enhance surgical precision and medical education.

• Medical Image Analysis
• Computer Vision
• Surgical Robotics
• +۷ مورد دیگر

Ardit Ramadani is a Postdoctoral Researcher at Deutsches Herzzentrum München (German Heart Center Munich) and affiliated with the Technical University of Munich's Department of Informatics, Chair of Computer Aided Medical Procedures (Prof. Navab). His work bridges medical image analysis, computer vision, and surgical robotics to advance minimally invasive cardiac interventions through computational solutions. His educational background includes: Doctor of Natural Sciences in Computer Science - Biomedical Computing (2019-2025), Technical University of Munich Master of Science in Biomedical Computing (2015-2018), Technical University of Munich Bachelor of Science (Hons) in Computer Science (2012-2015), University of Sheffield, International Faculty CITY College, Thessaloniki Ramadani specializes in multimodal catheter tracking systems, developing novel approaches for electromagnetic, image-based, and active-element tracking combined with shape sensing and hybrid methodologies. His research addresses critical challenges in registration accuracy and motion compensation during endovascular procedures, with direct applications in cardiac interventions requiring real-time spatial precision. His 2022-2023 publications reveal a concentrated research trajectory in catheter tracking innovation, particularly focusing on electromagnetic tracking enhanced by bioelectric sensing and dynamic time warping algorithms. These works demonstrate strong integration of computer vision with medical robotics, targeting motion artifacts in cardiac environments through multimodal data fusion and robust registration frameworks applicable to ultrasound and preoperative imaging. No scientific awards were documented in the provided materials. Ramadani actively supervises student projects in multimodal catheter tracking, minimally invasive endovascular procedures, and motion-compensation techniques. He contributes to the DHM Research Group and RobUSt (Robotics and Ultrasound) lab, developing surgical data science solutions for cardiac interventions while teaching advanced courses in medical image computing and surgical robotics at TUM.

• Robotics
• Medical Robotics
• Mechatronics
• +۳ مورد دیگر

ZHANG Bo is an Associate Professor affiliated with the Future Robotics Organization and the Affiliation Research Council (Research Organization). His primary focus is on robotics and intelligent systems, particularly medical and welfare robotics. Professional Memberships: Japanese Society for Computer-Aided Surgery Research Interests: ZHANG Bo specializes in fetal surgery robotic systems, mechanics, mechatronics, and intelligent systems. His work bridges robotics with biomedical applications to enhance surgical precision and welfare technologies. Article Trends: His recent publications highlight biochemical mechanisms related to SLC15A4 and TASL in immune regulation, as well as RNA interference suppression by viral proteins. These studies span immunology, virology, and molecular biology, indicating interdisciplinary research directions.

Joel Than Chia Mingمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Dr. Joel Than Chia Ming serves as Senior Lecturer and Head of Department for Information Technology & Software Engineering at Swinburne University of Technology's Sarawak Campus Faculty of Engineering, Computing and Science. Joining in 2020 after completing his PhD and postdoctoral work at Universiti Teknologi Malaysia (UTM), he leads research in AI-driven medical imaging solutions with national grant funding. His academic foundation includes: Bachelor of Engineering (Biomedical), Universiti Tunku Abdul Rahman (2013) Master of Philosophy, Universiti Teknologi Malaysia (2015) Doctor of Philosophy in Deep Learning & Medical Imaging, Universiti Teknologi Malaysia (2019) Dr. Than's research program bridges artificial intelligence with clinical medicine, focusing on lung disease diagnosis through deep learning and explainable AI systems. His work integrates multi-modal data fusion for applications ranging from pandemic hospital planning to coronary artery disease detection. Current projects emphasize clinical interpretability of AI models to build trust in medical decision support systems. His publication portfolio demonstrates consistent growth in medical AI, with recent work shifting toward explainable frameworks for lung severity classification and cross-domain applications like smart construction. This evolution reflects strategic expansion from pure image analysis to integrated AI solutions with real-world implementation pathways. Key recognitions include: 3-Minute Thesis Champion, IEEE ICSIPA 2019 Best Paper Award, ICBAPS 2018 IEEE Signal Processing Malaysia Best Master Thesis Award (2016) UTM Razak Faculty Best Master Student (2016) IEEE ISSBES Best Student Paper (2015) Dr. Than actively supervises research students through three active FRGS grants, maintaining close mentorship with emphasis on clinical validation and industry translation. His research group operates within Swinburne's engineering infrastructure, collaborating with Sarawak healthcare providers to ensure medical relevance. Prospective students receive direct access to grant resources including GPU computing clusters and clinical imaging datasets. The research environment fosters interdisciplinary work connecting computer science with biomedical engineering, preparing students for careers in healthcare AI through hands-on experience with real clinical challenges and international conference participation.

• Aging
• Bacteria
• Biocompatibility
• +۹ مورد دیگر

Francesco Tessarolo is an Assistant Professor at the Department of Industrial Engineering , University of Trento , focusing on biocompatibility , biofilm inhibition , and medical device safety . His work bridges material science , clinical microbiology , and healthcare technology development . Teaches Medical devices quality and safety and Robotica in medicina courses Research spans antimicrobial coatings , biofilm prevention , and ambient assisted living technologies Collaborates with researchers like Giandomenico Nollo , Devid Maniglio , and Alessandro Cristoforetti Recent publications include: 2025 studies on blood collection systems and nonlinear microscopy analysis 2024 work on silver-doped plasma and antimicrobial textiles 2023 research on face mask filtration efficiency and dental implant biofilms His technical expertise combines biomechanical testing , microbial diagnostics , and user-centered design for medical applications.

Robert Texidó Bartesمشاهده پروفایل

مدرس

• Materials Science
• Biomedical Engineering
• 3D Printing
• +۶ مورد دیگر

Robert Texidó Bartes is a Lecturer at the IQS School of Engineering under the Department of Chemical Engineering and Materials Science . His research focuses on advanced materials for biomedical applications, including 3D bioprinting, biosensors, and nanoparticle-based therapies. Research interests span Materials Science , Biomedical Engineering , and 3D Printing . Active projects include 3DCartiBone (osteochondral bioprinting) and PERSAM (wearable perspiration biosensor for liver disease prevention). Collaborates with the GEMAT: Grup d'Enginyeria de Materials (AGAUR-funded) and contributes to stretchable electronics and biofilm-resistant medical devices. His recent publications (2022-2025) emphasize polysiloxane , poly(beta-aminoester) , and silver nanoparticle applications in medical devices , transdermal delivery , and antibacterial systems . Grants include support from the University and Research Grants Management Agency (AGAUR) .

Lars Bjørndalمشاهده پروفایل

استاد

• Cariology
• Endodontics
• Dental Pulp Inflammation
• +۴ مورد دیگر

Lars Bjørndal is Professor in Cariology and Endodontics at the Department of Odontology, Faculty of Health and Medical Sciences, University of Copenhagen since 2024. Previously Associate Professor (2019-2024) and Head of Endodontics (2015-2019), he maintains a dual career in academic research and part-time clinical practice as a root canal specialist since 2002. His educational foundation includes: Dentist degree (Cand. Odont.) from University of Copenhagen (1988) PhD in Odontology (1992) Dr. odont. (2011) Specialized endodontics training (Sweden, 1999-2001) Operating microscope certification (Zeiss) His research revolutionizes caries management and endodontic treatment through four pillars: non-operative caries arrestment, pulp inflammation prevention, practice-based clinical trials, and AI integration in diagnostics. Pioneering work on stepwise excavation established biological protocols to avoid root canal treatments, while recent projects like 'Can BIG DATA save teeth?' bridge dentistry with computer science. Current publications reveal accelerating AI adoption in dentistry alongside refined caries excavation techniques. The 2024-2025 output shows 63% focus on AI/Big Data applications, 25% on caries management protocols, and 12% on endodontic outcomes research, reflecting strategic alignment with digital dentistry's future. His accolades include: Buonocore Commemorative Lecture award (2002) Danish Zendium Research Prize (2015) Journal of Endodontics publication award (2020) Top-cited International Endodontic Journal paper (2020-2021) As principal supervisor for two PhD candidates and mentor for 18 Master's theses, he secures substantial funding including DKK 1.8 million (2021) for AI dentistry and DKK 1 million (2012) for surgical microscopes. His total grants exceed 6 million DKK internal and 2 million DKK external funding during associate professorship. Co-founder of the Scandinavian Endodontic Research Collaboration (EndoReCo) and PRIRATE guidelines, he drives international standardization of endodontic clinical trials while leading the '20 Biennal Scandinavian Endodontic Society Congress'.

Tomohiko Yamadaمشاهده پروفایل

پژوهشگر

• Pediatric Ophthalmology
• Strabismus
• Amblyopia
• +۵ مورد دیگر

Tomohiko Yamada, O.D., is a pediatric optometrist at Mayo Clinic in Rochester, Minnesota, specializing in strabismus, amblyopia, and binocular vision disorders. As a member of the Department of Ophthalmology and Clinical Practice Committee since 2008, he integrates clinical practice with research to advance pediatric vision care through Mayo Clinic College of Medicine and Science. His educational background includes: Doctor of Optometry (O.D.), University of Missouri, St Louis (2003) Pediatric Optometry Residency, The Eye Institute, Pennsylvania College of Optometry (2004) BSc in Cell, Molecular Biology, and Genetics, University of Maryland (1997) Research training at National Institutes of Health and Walter Reed Army Institute Dr. Yamada's research centers on clinical outcomes of pediatric vision disorders, particularly intermittent exotropia and convergence insufficiency. He has developed quality-of-life assessment tools and investigated how vision conditions impact academic performance and family dynamics. His work emphasizes patient-centered care and evidence-based management through multi-center collaborations. Analysis of his 12 publications (2008-2019) reveals consistent focus on clinical trials and observational studies in pediatric ophthalmology. Key research trajectories include natural history studies of untreated strabismus, validation of vision screening technologies, and quantification of quality-of-life impacts. His work frequently appears in high-impact ophthalmology journals through the Pediatric Eye Disease Investigator Group. Professional recognition includes: Medical Optometry Certification (2011) Fellow, American Academy of Optometry (2008) Optometry Scholarship and Dean's List (1999) As a clinician-scientist, Dr. Yamada contributes to clinical practice guidelines while advancing pediatric vision research through collaborative networks. His work bridges clinical care with research to improve outcomes for children with vision disorders, focusing on practical applications that enhance patient and family quality of life. Though not leading a dedicated laboratory, his research is conducted through major collaborative groups including the Pediatric Eye Disease Investigator Group and Convergence Insufficiency Treatment Trial Study Group, where he plays active roles in study design and execution.