Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.
Rikke Gade is an Associate Professor at the Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design at Aalborg University. Her research focuses on Visual Analysis and Perception, AI for the People, and Mobility and Tracking Technologies. She has established herself as a leading researcher in computer vision applications across multiple domains including sports analytics, animal welfare, and human-computer interaction. Dr. Gade earned her PhD in Computer Vision from Aalborg University in 2015 with her dissertation "Taking the Temperature of Sports Arenas - Automatic Analysis of People." Prior to this, she completed her M.Sc. in Informatics with specialization in Vision, Graphics and Interactive Systems in 2011, and her B.Sc. in Electronic and Electrical Engineering in 2009. Her research interests span Computer Vision, Image Processing, Robot Vision, and Thermal Imaging with applications in diverse fields. She has pioneered work in using thermal imaging for sports analytics, developing systems for tracking athletes and analyzing movement patterns in sports arenas. More recently, her research has expanded into AI applications for animal welfare, particularly developing computer vision systems for detecting pain and stress in horses through facial expression analysis. Dr. Gade's publications reveal a clear progression from foundational work in thermal imaging and sports analytics toward more complex applications in healthcare, animal welfare, and smart building systems. Her most recent work shows increasing focus on ethical AI applications that serve societal needs, as evidenced by projects like "AI for the People" and research on equine pain detection. Best Paper Award at Conference CISBAT 2021 (Lausanne, Switzerland) Dr. Gade actively supervises PhD students, including Alves, J.M. on equine affective state assessment. She has secured significant research funding, including a major grant from the Independent Research Fund Denmark for developing AI systems to detect pain in horses. Her collaborative approach is evident in her involvement in multiple interdisciplinary projects spanning computer science, veterinary medicine, and building science. She leads the Visual Analysis and Perception research group at Aalborg University, focusing on practical applications of computer vision technologies. Her team works closely with industry partners on real-world implementations, particularly in sports analytics and animal welfare applications. Current projects include developing AI systems for road damage detection in collaboration with Faxe Kommune and creating advanced tracking systems for sports performance analysis.
Cino Pertoldi is a Professor at the Department of Chemistry and Bioscience, Aalborg University, affiliated with the Faculty of Engineering and Science. His research focuses on conservation biology, evolutionary biology, genetics, and ecology. He leads and participates in multiple research projects, such as AI-based wildlife monitoring and virtual fencing systems. His work involves interdisciplinary approaches using drones, machine learning, and genomics to address ecological challenges. Education: Not explicitly stated in the provided text. Projects: AI methods for predator identification via thermal drones (2025-2025) eDNA tracking of mink and foxes in waterways (2024-2026) Historical morphometrics of European bison skulls (2023-2026) Research Interests: Wildlife conservation, genomics of endangered species, habitat monitoring, and technological innovations in ecology. Notable contributions include studies on European bison genomics, raccoon dog diets, and thermal imaging for hare monitoring. His work often integrates citizen science and advanced technologies to enhance ecological understanding and conservation strategies. Grants/Projects: Over 13 projects since 2016, including collaborations on virtual fencing and drone-based wildlife surveillance. He has supervised 2 PhD students. Labs/Teams: Involved in environmental biomonitoring initiatives at AAU Arctic and collaborates with institutions like Aalborg Zoo and Vejlerne nature reserve.
Moiz Khan Sherwani serves as a Postdoctoral Researcher at the Department of Veterinary and Animal Sciences within the Faculty of Health and Medical Sciences at the University of Copenhagen. His work bridges computational methods with veterinary clinical microbiology, focusing on data-intensive biological problem-solving through advanced algorithmic approaches. His educational background includes a PhD in Computer Science specializing in Artificial Intelligence in Medicine from the University of Calabria. This foundation enables his interdisciplinary research at the intersection of machine learning and life sciences. Dr. Sherwani's research spans medical image analysis (including MRI/CT-based diagnosis and infection segmentation) and current work on multi-omics/genomics data analysis. He develops predictive ML models for biological applications, with particular emphasis on veterinary clinical contexts. His methodology integrates synthetic data generation with clinical translation, positioning him at the forefront of computational veterinary science. His 2024 publication in Frontiers in Radiology demonstrates his focus on systematic evaluation of deep learning for medical image synthesis, revealing strong trends toward clinically applicable AI frameworks in radiotherapy. This work reflects his dual expertise in technical AI development and domain-specific medical implementation.
Fintan McEvoy is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, affiliated with the Department of Veterinary Clinical Sciences. His research focuses on advancing veterinary imaging through computational methods, particularly in diagnostic radiology for companion animals. Education: Holds multiple advanced qualifications including: MVB (Veterinary Medicine) PhD DVSc (Doctor of Veterinary Science) DVR (Diploma in Veterinary Radiology) DipECVDI (Diplomate of the European College of Veterinary Diagnostic Imaging) Research Focus: Leads innovation in veterinary diagnostics through: Development of AI-powered tools for canine hip dysplasia assessment Quantitative CT analysis of pulmonary and adipose tissues Machine learning applications for radiographic feature classification Computer vision solutions for automated disease screening Validation of diagnostic protocols in veterinary dentistry Publication Trends: McEvoy's recent work (2024-2025) demonstrates concentrated focus on artificial intelligence applications in veterinary orthopedics, particularly deep learning systems for automated assessment of canine hip dysplasia. His research trajectory shows consistent integration of computational methods with diagnostic imaging throughout his career. Professional Leadership: President of the European College of Veterinary Diagnostic Imaging (2024-2026) Regular presenter at academic conferences including talks on AI in disease screening
Anna V Müller serves as Associate Professor in Veterinary Diagnostic Imaging within the Department of Veterinary Clinical Sciences at the University of Copenhagen's Faculty of Health and Medical Sciences. Her dual roles as Didactic Coordinator for the Master of Companion Animal Clinical Sciences program and Social Media Community Manager at the University Hospital for Companion Animals demonstrate her integrated approach to clinical service, education, and research. She teaches Veterinary Imaging and supervises Bachelor's and Master's theses while maintaining active research in diagnostic imaging and educational methodologies. Education: Doctor of Veterinary Medicine (DVM) PhD Research Focus: Dr. Müller's expertise centers on quantitative analysis of veterinary diagnostic imaging, with specialized applications in ultrasound, CT, MRI, and radiology for companion animals and exotic species. Her innovative work integrates machine learning algorithms to enhance image interpretation accuracy while simultaneously advancing veterinary education through neurodiversity research (particularly ADHD) and communication strategies. This dual-track approach bridges technical imaging advancements with pedagogical innovation to improve both diagnostic outcomes and educational experiences. Publication Trends: Recent publications (2021-2025) reveal three dominant research trajectories: (1) species-specific imaging challenges in exotic animals (guinea pigs, Tasmanian wombats), (2) radiation safety and image optimization in canine diagnostics, and (3) social media applications for clinical communication. Her work consistently connects quantitative image analysis with practical clinical solutions, while the neurodiversity in education strand represents an emerging cross-disciplinary contribution to academic veterinary medicine. Scientific Recognition: ECVDI Travel Grant (2015) Academic Leadership: As thesis supervisor for undergraduate and graduate students in the Master of Companion Animal Clinical Sciences program, Dr. Müller guides research in diagnostic imaging applications. Her PhD thesis on image feature extraction established foundational work for current machine learning applications. The University Hospital for Companion Animals serves as her primary research environment where she leads social media initiatives that simultaneously support client communication and research recruitment. Clinical Integration: Working within the Section for Diagnostic Imaging & Companion Animal Surgery and Clinical Specialties, she collaborates with surgical and clinical teams to translate imaging research into patient care protocols. Her social media management role creates unique synergy between public engagement and data collection for ongoing research projects in companion animal health.
Matin Afshar is a Senior Postdoctoral Researcher (Research Fellow) in the Department of Materials and Production at Aalborg University's Faculty of Engineering and Science. His research focuses on orthopedic biomechanics, particularly bone-implant interactions and orthopedic screw fixation. He leads projects within the SPARK initiative investigating knee prosthetics and collaborates internationally with institutions including Harvard Medical School and KU Leuven. Education includes a PhD in Biomechanics from Amirkabir University of Technology focused on orthopedic bone screw fixation through modal analysis. His doctoral research established correlations between mechanical properties and implant stability. He completed research visits at KU Leuven focusing on in-silico bone screw fixation models. Research integrates computational modeling with experimental validation, utilizing finite element analysis, in vitro testing, and advanced biomechanical characterization. Recent investigations focus on 3D-printed implants, patient-specific surgical instruments, and machine learning applications in orthopedic outcomes prediction. Authored over 30 peer-reviewed publications addressing biomechanical stability assessment, drilling mechanics, and implant design optimization. Research demonstrates consistent focus on translating computational models into clinical applications for improved surgical outcomes. Awarded Best Poster Presentation at the International Iranian Conference on Biomedical Engineering. Active member of the European Society of Biomechanics and peer reviewer for Journal of Biomechanics and Spine. Currently leading development of patient-specific implant solutions through international collaborations and research visits in Leuven, Belgium.
Dorte Hald Nielsen serves as a Teaching Professor in the Department of Veterinary Clinical Sciences at the University of Copenhagen's Faculty of Health and Medical Sciences. Her expertise centers on veterinary diagnostic imaging with specialization in companion animal surgery and clinical specialties. She is actively engaged in teaching and research at the university's veterinary hospital in Frederiksberg, Denmark. Dr. Nielsen's research focuses on veterinary radiology, particularly in hip and elbow dysplasia screening, radiation safety protocols, and digital image analysis techniques. Her work integrates advanced technologies including deep learning algorithms to improve diagnostic accuracy in veterinary orthopedics. She has made significant contributions to image quality assessment methodologies and radiation protection standards in veterinary diagnostic procedures. Analysis of her recent publications reveals a strong emphasis on applying innovative approaches to veterinary radiology, with particular focus on AI-assisted diagnostics for hip dysplasia detection, optimization of imaging parameters, and development of standardized screening protocols. Her research spans both technical aspects of imaging and clinical applications in companion animal medicine. Dr. Nielsen is actively involved in veterinary medical education, having developed innovative teaching methodologies including online radiology reporting systems with integrated peer review components to enhance student learning and assessment in diagnostic imaging disciplines.
Lilah Margaret Moorman serves as a Senior Veterinary Surgeon and Guest Researcher at the University of Copenhagen's Department of Veterinary Clinical Sciences, affiliated with both the Section for Diagnostic Imaging & Companion Animal Surgery and Clinical Specialties and the Section for Veterinary Imaging. Her clinical and research work centers on advancing diagnostic imaging techniques in veterinary medicine. Her research focuses on veterinary radiology applications, particularly hip dysplasia screening in companion animals through machine learning integration. Key interests include developing AI-driven solutions for radiographic analysis, creating soft tissue markers for image-guided therapy, and establishing quality standards for digital radiographs. This work bridges computational methods with clinical veterinary practice to enhance diagnostic precision in orthopedics and surgery. Analysis of her 2019-2021 publications reveals a consistent trend toward AI-enhanced veterinary imaging, with emphasis on deep transfer learning for joint detection/classification and multimodal markers connecting diagnostics to therapeutics. These studies demonstrate growing integration of computational intelligence in clinical veterinary workflows, particularly for improving hip dysplasia assessment accuracy and developing precision imaging tools.
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.
Thomas Hartig Braunstein serves as a Senior consultant at the Department of Biomedical Sciences within the Faculty of Health and Medical Sciences at the University of Copenhagen. His research spans multiple disciplines in biomedical science with particular emphasis on advanced imaging techniques and physiological investigations across diverse biological systems. Dr. Braunstein's research interests encompass biomedical imaging, hematology (particularly sickle cell disease), neuroscience (with focus on neurodegenerative diseases like ALS), cardiovascular physiology, molecular biology, and veterinary medicine. His work demonstrates a strong interdisciplinary approach, frequently combining sophisticated imaging methodologies with physiological investigations to address complex biomedical questions. A recurring theme in his research is the development and application of novel analytical techniques to improve disease diagnosis and understanding of pathological mechanisms. Analysis of Dr. Braunstein's publication record reveals a consistent focus on translational research that bridges basic science with clinical applications. His work spans from molecular-level investigations using fluorescent probes to clinical applications in hematology and cardiology, with particular attention to developing innovative imaging and analytical methods. The collaborative nature of his research is evident through co-authorship with specialists across veterinary medicine, neuroscience, hematology, and cardiovascular physiology. Dr. Braunstein maintains active research collaborations across multiple disciplines, as evidenced by his 47 documented research outputs including 41 journal articles, 3 conference abstracts, 2 posters, and 1 comment/debate. His work has been published in reputable journals across various biomedical fields and has garnered attention through social media sharing, Mendeley readership, and citations in patent applications.
