جست‌وجوی استاد و پژوهشگر

Laura Eloمشاهده پروفایل

استاد

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +۷ مورد دیگر

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

Changhuei Yangمشاهده پروفایل

استاد

• Biophotonics
• Microscopy
• Computational Imaging
• +۳ مورد دیگر

Changhuei Yang is the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at California Institute of Technology, serving as Executive Officer for Electrical Engineering and Investigator at Heritage Medical Research Institute. He holds a Ph.D. and three master's degrees from MIT, with appointments at Caltech since 2003. Research focuses on: Advanced microscopy techniques including Fourier Ptychography Wavefront shaping for biological tissue imaging Optical phase conjugation for deep-tissue applications Compact medical devices for cerebral monitoring Publications demonstrate leadership in computational imaging, with recent advances in stain-free embryo analysis, portable cerebral blood flow monitors, and high-resolution volumetric imaging techniques using neural representations. Honored as National Academy of Inventors member. Research applications span deep-tissue biochemical imaging, incisionless surgery, and optogenetic activation systems.

Raphael Sznitmanمشاهده پروفایل

استاد

• Computer Vision
• Probabilistic Methods
• Statistical Learning
• +۲ مورد دیگر

Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.

David Junckerمشاهده پروفایل

استاد

• Microfluidics
• Nanotechnology
• Lab-on-a-Chip
• +۶ مورد دیگر

David Juncker is a Professor and Department Chair of the Department of Biomedical Engineering at McGill University. He serves as a Principal Investigator at the McGill University & Genome Quebec Innovation Centre and holds associate memberships in the Department of Neurology and Neurosurgery, Department of Electrical and Computer Engineering, Division of Experimental Medicine, Department of Surgery, and Goodman Cancer Research Centre. His research focuses on micro- and nano-bioengineering technologies for bioanalysis, precision medicine, and organs-on-chips. Key areas include microfluidics, lab-on-a-chip devices, biomedical sensors, medical diagnostics, biomaterials, tissue engineering, and cancer biomarker discovery. His lab develops scalable antibody microarrays, self-powered diagnostic platforms, microfluidic probes for brain tissue perfusion, and nanogradients for neuronal navigation, with applications in cancer diagnostics, global health, and neuroscience. Recent publications (2023-2025) reveal strong emphasis on extracellular vesicle analysis, single-cell proteomics, 3D-printed microfluidic/organ-on-a-chip systems, and capillary-driven circuits. Key trends include low-cost point-of-care diagnostics, advanced circulating tumor cell isolation methods, and biomimetic synthetic vesicles for drug delivery, demonstrating translational potential in early disease detection. Dr. Juncker leads a highly interdisciplinary team comprising undergraduate and graduate students, post-doctoral fellows, and staff from diverse scientific, engineering, and cultural backgrounds. His lab actively recruits Canadian/permanent resident graduate students for projects on single extracellular vesicle and protein detection in cancer and infectious diseases, leveraging microfluidics and wearables for biomarker discovery. The Juncker Lab operates from the McGill University & Genome Quebec Innovation Centre (740 Dr. Penfield Avenue, Room 6206). It maintains a collaborative, multicultural environment focused on developing transformative micro- and nano-bioengineering technologies with significant potential impact on human health diagnostics and treatment.

• Molecular Imaging
• Biomedical Imaging
• Optical Coherence Tomography
• +۳ مورد دیگر

Adam de la Zerda is an Associate Professor at Stanford University's Department of Structural Biology (School of Medicine) and Electrical Engineering (by courtesy). He develops advanced optical molecular imaging technologies combining nanoparticle contrast agents and adaptive OCT systems for cancer and ophthalmic disease research. Technion-Israel Institute of Technology (BSc, 2005) Stanford University (PhD, 2011) UC Berkeley (Postdoctoral Fellowship) Research Themes : Virtual biopsy using machine learning-enhanced OCT Gold nanorod-based molecular contrast agents Speckle noise reduction for cellular resolution Needle beam optical coherence tomography angiography His 15 most recent publications demonstrate technical innovations in: Metasurface optics for extended depth-of-field Spectral deconvolution of multiple contrast agents Speckle modulation for improved diagnostic clarity Noninvasive lymphatic system mapping Scientific Honors : Pew-Stewart Scholar for Cancer Research AFOSR Young Investigator NIH Early Independence Award Forbes 30 Under 30 (x2) Chan Zuckerberg BioHub Investigator His lab team has developed clinical prototypes including OcuBell Inc. 's ophthalmic imaging systems and Visby Medical 's diagnostic platforms. Current research spans from in vivo glycoprotein imaging to de novo biosensor development for real-time disease monitoring in awake animal models.

Neda Haj Hosseiniمشاهده پروفایل

مدرس ارشد

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

Neda Haj Hosseini is a Senior Lecturer and Associate Professor in Biomedical Engineering at Linköping University's Department of Biomedical Engineering (IMT) . She contributes to teaching courses like TBMT56 - Medical Technology and TBME08 - Biomedical Modeling and Simulation , while leading research initiatives in AI-driven cancer diagnostics and biomedical optics. Research Focus: Development of AI methods for cancer diagnostics, optical coherence tomography (OCT) applications, and fluorescence spectroscopy in surgical guidance Affiliations: Center for Medical Image Science and Visualization (CMIV) , Analytic Imaging Diagnostic Arena (AIDA) , Swedish Medical Technology Association Recent Research Trends demonstrate expertise in applying deep learning to: Pediatric brain tumor classification using multimodal imaging Optical biopsy techniques for intraoperative decision support Automated biomarker quantification in histopathology Medical imaging data integrity and algorithm validation Scientific Awards include grants from: Joanna Cocozza Foundation (2022) Swedish Childhood Cancer Foundation (2024) Academic Leadership involves mentoring students in projects such as: "Multiple Instance Attention-based Learning for Brain Tumor Classification" "Vision Transformers for Multiclass Brain Tumor Tissue Classification" "Reaction-diffusion Models for Image-driven Tumor Simulation"

• Serious Games
• 3D Visualization
• Medical Education Technology
• +۴ مورد دیگر

Daniel Livingstone is a researcher at The Glasgow School of Art (GSA) specializing in the application of games and 3D technologies to enhance learning and public engagement. His work spans medical visualization, heritage interpretation, and broader educational technology domains. Current PGR supervisee: Shaojie Ni (AR & Gamification in Museums) Email: D.Livingstone@gsa.ac.uk Research Themes : Serious games, virtual reality, 3D anatomical modeling, disease education, digital heritage preservation, and AI-driven simulations. Highlights include AR tools for rheumatology engagement, VR applications in diabetes management, and digital reconstructions of historical surgical instruments. Article Trends : Focus on merging immersive technologies with healthcare education, heritage storytelling, and interdisciplinary applications of game engines. Recurring keywords: Augmented Reality , 3D Visualization , Medical Education , Public Health , Virtual Environments .

Azhar Zamمشاهده پروفایل

دانشیار

• Biomedical Optics
• Optical Coherence Tomography (OCT)
• Photoacoustics
• +۵ مورد دیگر

Azhar Zam is an Associate Professor of Bioengineering at NYU Abu Dhabi (NYUAD) and associated faculty at NYU Tandon School of Engineering's Biomedical and Electrical Engineering departments. He holds a B.Sc. from University of Indonesia, M.Sc. from University of Luebeck (Germany), and Ph.D. from Friedrich-Alexander-University Erlangen-Nuremberg (Germany). His research focuses on developing smart optical devices for medical imaging/diagnostics, including laser surgery, OCT, photoacoustics, and AI-driven imaging systems. He leads the Laboratory for Advanced Bio-Photonics and Imaging (LAB-π) at NYUAD and has authored 85+ publications/patents. Education: Bachelor of Science, University of Indonesia M.Sc. Biomedical Engineering, University of Luebeck Ph.D. Engineering, Friedrich-Alexander-University Erlangen-Nuremberg Research Interests: Innovations in biomedical optics, optical-based smart sensors, AI-enhanced diagnostics, and miniaturized medical imaging systems. His work integrates advanced optical technologies with surgical robotics and clinical applications. Professional Contributions: Associate Editor for Frontiers in Photonics Biophotonics section; Reviews Editor for Frontiers in Ophthalmology Retina section. Previously held positions at University of Basel (Assistant Professor), University of Waterloo, and other institutions globally. Labs & Teams: Directs NYUAD's LAB-π lab focusing on bio-photonics innovations. Collaborates across NYU's global network and international partners.

• Artificial Intelligence
• Machine Learning
• Medical Education Technology
• +۷ مورد دیگر

Andrew R. Jamieson is an Assistant Professor in the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center, where he leads a research team focused on developing advanced AI systems for medical education and clinical performance assessment. He was appointed in 2019 and serves as Principal Investigator of the Jamieson Group. Institution: UT Southwestern Medical Center School: School of Health Professions Department: Lyda Hill Department of Bioinformatics Academic Rank: Assistant Professor Dr. Jamieson earned his B.A. in Physics with honors (2006) and Ph.D. in Medical Physics (2012) from the University of Chicago. His early work in computer-aided diagnosis laid the foundation for his career in AI and machine learning. Education: University of Chicago (B.A., Ph.D.) Prior Experience: GE Healthcare, Big Data Analytics Startup (First Data Scientist) Dr. Jamieson's research lies at the intersection of artificial intelligence, medical education, and bioinformatics. His team leverages multimodal data—including video, audio, and text—from the UTSW Simulation Center to train frontier AI models for automated assessment of medical student performance. His work in computational image analysis spans label-free live-cell imaging, spatial biology, and highly multiplexed immunofluorescence, with applications in cancer biology and diagnostics. He has also made significant contributions to public health through the development of the UTSW COVID-19 forecast model. The most recent publications reflect a strong trend toward AI-driven medical education tools, particularly using large language models and multimodal AI for OSCE assessment. Earlier works focus on deep learning in medical imaging, dimensionality reduction, and computer-aided diagnosis in mammography. The research consistently emphasizes interpretability, automation, and clinical translation. Scientific recognition includes being featured on the cover of Cell Systems (July 2021) for work on melanoma cell analysis. His team's development of the first automatic AI grading system for medical student OSCE notes in 2023 marks a major innovation in educational assessment. Featured on cover of Cell Systems (2021) Developed UTSW COVID-19 forecast model Pioneered AI grading system for OSCE notes (2023) Dr. Jamieson is actively involved in mentoring and graduate education. He serves as Course Director for the Master’s in Health Informatics program and contributes to nanocourses at the Clinical Informatics Center. His team includes multiple advisees and collaborators working on NLP, LLMs, and AI/ML in healthcare. He is expanding his group and seeking researchers in AI, data science, and software development. His leadership in the Bioinformatics Core Facility (2018–2021) and ongoing collaborations with pathologists and radiation oncologists demonstrate strong interdisciplinary grant and project engagement. Course Director: Master’s in Health Informatics Mentor to multiple graduate students and researchers Collaborations: Pathology, Radiation Oncology, Surgery, Clinical Informatics The Jamieson Group is a dynamic, interdisciplinary research team at the forefront of applying cutting-edge AI to medical education and clinical data analysis. The lab focuses on natural language processing, multimodal learning, and computer vision, with strong ties to the UTSW Simulation Center and Clinical Informatics Center. The team develops custom pipelines for spatial biology and imaging data and is actively expanding to meet growing research demands.

Sharib Aliمشاهده پروفایل

استادیار

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +۱۲ مورد دیگر

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.

• Vascular Biology
• Atherosclerosis
• Arterial Stiffness
• +۲ مورد دیگر

Dr. Stella Daskalopoulou is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) and a Professor in the Department of Medicine at McGill University's Faculty of Medicine and Health Sciences. She specializes in translational cardiovascular research with a focus on vascular health, atherosclerosis, and hypertension management. Senior Scientist, RI-MUHC Glen site Professor, Department of Medicine, McGill University Member, Cardiovascular Health Across the Lifespan Program Research Focus: Her work integrates biomedical technology with clinical investigation to identify early vascular impairment markers. Key areas include: Arterial stiffness and hemodynamic assessment Adiponectin signaling pathways in atherosclerosis Sex-specific cardiovascular risk stratification Biomarker discovery for plaque instability Pregnancy-related vascular complications Machine learning applications in plaque classification Scientific Engagement: Dr. Daskalopoulou contributes to hypertension guideline development (2024 ESC Guidelines) and explores environmental impacts on vascular health (household pollution studies). Her recent publications emphasize: Deep learning for plaque characterization Sex hormone receptor pathways in atherosclerosis Adipokine interactions with HDL metabolism Clinical decision tools for preeclampsia prediction Laboratory: Leads the Vascular Health Unit at RI-MUHC, combining histopathology, immunophenotyping, and advanced imaging for translational research.

• Bioengineering
• Biomaterials
• Tissue Engineering
• +۱۰ مورد دیگر

Devid Maniglio is an Associate Professor at the Department of Industrial Engineering, University of Trento. His research focuses on bioengineering, biomaterials, and tissue engineering, with a particular emphasis on bioprinting, surface modification, and functional materials. He has contributed to advancements in silk fibroin and hydrogel-based systems for medical applications. Research Interests Bioengineering for personalized medicine Biomaterials and surface engineering 3D bioprinting and tissue regeneration Molecular imprinting and biosensors Drug delivery and cell encapsulation Teaching Diagnostic and therapeutic technologies for personalized medicine Engineered materials for precision medicine Fundamentals of biomedical technologies Functional surfaces laboratory Labs & Collaborations Devid Maniglio is affiliated with the Functional Surfaces Laboratory at the University of Trento, collaborating with researchers such as Stefano Rossi and Flavio Deflorian. His work integrates interdisciplinary approaches in biomedical engineering and sustainable medical technologies.

Yuxiao Zhouمشاهده پروفایل

استادیار

• Implantable device design
• Surgery planning
• Bone disease diagnosis
• +۴ مورد دیگر

Dr. Yuxiao Zhou is an Assistant Professor in the J. Mike Walker '66 Department of Mechanical Engineering at Texas A&M University, with an affiliation to the School of Engineering Medicine. His research focuses on multi-scale musculoskeletal biomechanics, addressing challenges in aging, bone disease, and injury through implantable device design, computational modeling, and medical imaging. He holds a Ph.D. in Mechanical Engineering from Pennsylvania State University (2020), an M.S. from Rutgers University (2015), and a B.S. from Harbin Institute of Technology (2012). Research interests include biomechanical analysis of bone-implant interactions, bone regeneration mechanisms, and mechanobiology. Dr. Zhou's lab develops patient-specific biomedical devices and computational tools for surgery planning and disease diagnosis. His work integrates advanced imaging techniques like micro-CT and atomic force microscopy (AFM) to study bone mechanics across scales. Key awards include the Maryland Stem Cell Research Fund Postdoctoral Fellowship (2021) and the C. Norwood Wherry Memorial Graduate Fellowship (2020). His research has led to innovations in bone implant design, osteoporosis treatment strategies, and tissue engineering scaffolds. Recent publications explore topics like mineralization processes in tooth development and wireless microfluidic systems for dental implants. Dr. Zhou collaborates on grants related to biomechanical modeling and medical device development. His lab emphasizes translational research, aiming to bridge engineering and clinical applications. Ongoing projects include 3D-printed oxygen-releasing scaffolds for bone regeneration and aerogel-based solutions for bone cement interfaces.

Marinko Sarunicمشاهده پروفایل

استاد مدعو

• Biomedical Imaging
• Optical Coherence Tomography
• Microscopy
• +۲ مورد دیگر

Marinko Sarunic is an Adjunct Professor at the School of Engineering Science , Simon Fraser University . He holds a PhD in Biomedical Engineering from Duke University and has been recognized as a Michael Smith Foundation for Health Research Scholar . His research focuses on biomedical imaging , particularly optical coherence tomography (OCT) , microscopy , and low-coherence interferometry , with applications in diabetic retinopathy , Alzheimer’s disease , and age-related macular degeneration . Dr. Sarunic's work spans adaptive optics , deep learning , and sensorless OCT systems , emphasizing clinical translation and open-source software development (e.g., OCTAVA ). His Google Scholar publications highlight multimodal imaging , vascular heterogeneity analysis , and AI-driven diagnostics for retinal diseases. His contributions include the Michael Smith Foundation for Health Research Scholar award. Though not currently teaching courses, his collaborations and leadership in retinal imaging and medical device innovation are pivotal for advancing non-invasive diagnostics in neurodegenerative and diabetic conditions .

Sophie Rapagnaمشاهده پروفایل

پژوهشگر

• knee osteoarthritis
• microCT imaging
• bone microarchitecture
• +۲ مورد دیگر

Sophie Rapagna is a Casual Academic and Micro-CT Facility Manager at the College of Science and Engineering, Flinders University . Her research focuses on knee osteoarthritis , microCT imaging , and bone microarchitecture with applications in implant biomechanics and digital volume correlation (DVC) . Active in Flinders Institute for Nanoscale Science and Technology Full Member of the Medical Device Research Institute PhD candidate in biomechanics since 2017 Her publications (17 total) span topics like: Micro-CT-based mechanical analysis of implants Porosity in 3D-printed titanium components Failure mechanisms in dissimilar welds Bone-cartilage interactions in osteoarthritis Time-elapsed imaging of implant degradation High-resolution imaging for biomechanical strain mapping She collaborates with researchers such as Dr. Egon Perilli and Michael Taylor on projects involving digital volume correlation and additive manufacturing .