جستجوی استاد راهنمای دانشگاهی

Dylan Campbellمشاهده پروفایل

مدرس

• Computer Vision
• Optimization
• Machine Learning
• +۳ مورد دیگر

Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.

Benyuan Liuمشاهده پروفایل

استاد

• Data and Computer Communication Networks
• Mobile and Wireless Networks
• Internet Technologies & Applications
• +۳ مورد دیگر

Benyuan Liu is a Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell . He serves as Director and Graduate Coordinator for Ph.D. programs, with expertise in Data and Computer Communication Networks, Mobile and Wireless Networks, and Internet Technologies & Applications. Education: B.S., University of Science and Technology of China M.S., Yale University Ph.D., University of Massachusetts Amherst His research focuses on Artificial Intelligence in Medical Imaging , Deep Learning for Endoscopy , and Edge Computing Systems . Recent work includes automated lesion detection, 3D reconstruction from sensor data, and predictive models for financial and reproductive health domains. The 15 most recent publications highlight applications of deep learning in medical diagnostics (thyroid nodules, gastric lesions, dental caries), computer vision (attention mechanisms, transformers), and financial technology (market psychology analysis). Technical themes include mmwave radar processing, diffusion models for synthetic data, and multi-scale feature extraction. Benyuan Liu leads the Computer Networking Lab and CHORDS initiative at UMass Center for Digital Health. His work bridges network optimization with healthcare AI , emphasizing real-time systems and portable diagnostics.

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +۲ مورد دیگر

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

Mahdi S. Hosseiniمشاهده پروفایل

استادیار

• Deep Learning
• Computer Vision
• Computational Pathology
• +۳ مورد دیگر

Mahdi S. Hosseini is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and a faculty member of the Applied AI Institute. He holds a PhD from the University of Toronto (2016) and completed a postdoctoral fellowship at UofT, supported by MITACS-Elevate and NSERC fellowships. His research focuses on advancing deep learning and computer vision for computational pathology and healthcare technologies, aiming to develop AI tools for clinical diagnosis. He currently supervises graduate students and has published over 30 papers and two patents. Education: PhD in Electrical and Computer Engineering from the University of Toronto (2016), postdoctoral training at UofT collaborating with Huron Digital Pathology Inc. (Waterloo, Ontario). Research interests include deep learning, computer vision, computational pathology, medical imaging, and AI ethics (P4AI project). His work emphasizes developing explainable AI systems for clinical pathology, biomarker discovery, and efficient learning algorithms. Professional service includes serving as Area Chair for NeurIPS 2023, CVPR 2023-2024, and ECCV 2024. He reviews grants for CIHR, NSERC, and serves on program committees for key conferences (ICCV, CVPR, NeurIPS). Teaching includes courses on applied AI, machine learning, and deep learning for computational pathology at both graduate and undergraduate levels. Awards: MITACS-Elevate Fellowship (postdoc), NSERC Research Funding (2016-2017). His work has led to patents in diagnostic systems and has collaborated with hospitals and pathologists to advance clinical applications. Labs/Teams: Active collaborations with the Applied AI Institute at Concordia, Huron Digital Pathology, and healthcare institutions. Research emphasizes interdisciplinary approaches between computer science and clinical medicine.

Ji Hwan Parkمشاهده پروفایل

استادیار

• Data Visualization
• Digital Twins
• Human-AI Teaming
• +۵ مورد دیگر

Ji Hwan Park is an Assistant Professor in the School of Interactive Games and Media at RIT's Golisano College of Computing and Information Sciences (GCCIS). He holds a PhD from Stony Brook University under Prof. Arie Kaufman. His research focuses on accessible data visualization, digital twins, human-AI collaboration, and VR/AR applications. Notable contributions include developing tools for ADHD-friendly visualizations and interactive protein motif identification. He has received funding from the Department of Defense for biomedical research and earned an Honorable Mention at CHI 2024. Current teaching includes courses on game design and advanced algorithms. Research activities span medical imaging analytics (e.g., CMed framework for crowd-sourced diagnostics), climate modeling through Bayesian deep learning, and creative visualization techniques like Graphoto. His work bridges technical innovation with human-centered design principles, particularly in healthcare and neurodivergent accessibility contexts.

• Medical Image Reconstruction
• Computed Tomography
• Positron Emission Tomography
• +۶ مورد دیگر

Professor Jerome Liang is a distinguished faculty member at Stony Brook University's Renaissance School of Medicine, holding professorships in Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science. He serves as Co-Director of Radiology Research and has established himself as a leading expert in medical imaging reconstruction techniques. Dr. Liang's educational background includes a Ph.D. in Physics from City University of New York, postdoctoral training at Duke University, and fellowship at Albert Einstein College of Medicine. His undergraduate degree in Modern Physics was obtained from Lanzhou University in China. His primary research interests focus on advanced medical imaging techniques, particularly low-dose computed tomography image reconstruction, quantitative SPECT reconstruction, high-resolution PET imaging, tissue segmentation from multi-spectral images, computer-aided diagnosis systems, and virtual colonoscopy development. His work bridges engineering principles with clinical applications to improve diagnostic imaging capabilities while reducing radiation exposure. Analysis of his recent publications reveals a strong focus on machine learning applications in medical imaging, particularly in polyp classification, dual-energy CT spectral analysis, and virtual endoscopy. His research consistently aims to enhance diagnostic accuracy while optimizing radiation dose and improving visualization techniques for various medical conditions. 1981 China-US Physics Examination and Application Program (CUSPEA) Winner (Top 25 among 250,000 candidates) 1990 NIH First Investigator Award 1996 American Heart Association Established Investigator Award 1996 Radiological Society of North America Certificate of Merit Award 2002 SUNY Chancellor's Entrepreneur Award 2007 IEEE Society Fellow 2011-2013 SBU, BNL and CSHL Certificates of Excellence in Research and Invention 2013 Stony Brook School of Medicine Award for Excellence in Translational Research Dr. Liang has secured significant research funding including NIH/NCI R01 grants for "Advanced Virtual Colonoscopy for Early Cancer Screening" and "Radiogenomics of Colorectal Polyps." He currently leads active protocols including IRB 93995-MODCR005 focused on integrating virtual and optical colonoscopies with pathological analysis. His laboratory (IRIS - Imaging Research and Informatics) continues to advance medical imaging technology while mentoring the next generation of researchers in this critical field.

• Environmental Epidemiology
• Children's Environmental Health
• Molecular Epidemiology
• +۳ مورد دیگر

Margaret Karagas, PhD is the inaugural Department Chair and James W. Squires Professor of Epidemiology at the Geisel School of Medicine at Dartmouth College, with a secondary appointment as Professor of Community and Family Medicine. She also serves as Director of both the Children's Environmental Health and Disease Prevention Research Center and the Center for Molecular Epidemiology at Dartmouth. Her leadership extends to the NIH-funded Environmental Influences on Child Health Outcomes (ECHO) Program, which encompasses over 90,000 participants across the United States. Dr. Karagas earned her PhD from the University of Washington in 1990 and has established herself as a leading researcher in environmental epidemiology. She collaboratively developed an innovative cross-disciplinary postdoctoral and graduate program in Quantitative Biomedical Sciences (QBS) that integrates epidemiology, bioinformatics, and bistatistics. Her research focuses on interdisciplinary studies investigating environmental exposures, host factors, and biological mechanisms affecting health from infancy through adulthood, with particular emphasis on under-studied rural populations. Dr. Karagas employs high-dimensional analytic tools along with biomarkers and sensors to study the exposome, genetic susceptibility, and biological response. Her work has uncovered adverse cardiometabolic, neurodevelopmental, and immune-related health outcomes associated with drinking water contaminants, food-borne toxicants (including in infant first foods), and exposures from woodstoves. Analysis of her recent publications reveals a strong focus on environmental health impacts across the lifespan, with significant contributions to understanding how environmental exposures affect child development, chronic disease risk, and health disparities. Her work frequently employs advanced molecular and analytical techniques to assess environmental exposures and their biological consequences. Dr. Karagas serves on numerous prestigious international consensus panels and committees, including those for the United Nations, World Health Organization, International Agency for Research on Cancer, European Food Safety Authority, US National Institutes of Health, and National Academies of Science, Engineering and Medicine. As an academic leader, she has mentored diverse investigators at all career stages and established innovative training programs. Her research has directly contributed to practice and policy changes regarding environmental health threats. She maintains active collaborations across multiple centers at Dartmouth including the Dartmouth Toxic Metals Superfund Research Program and Dartmouth SYNERGY.

• Marine Sciences
• Coral Reef Conservation
• Biogeochemistry
• +۴ مورد دیگر

Liza M. Roger is an Assistant Professor in the School of Molecular Sciences at Arizona State University (ASU) and a Senior Global Futures Scientist at the Global Futures Scientists and Scholars Program. She is affiliated with the School of Ocean Futures and works at the Walton Center for Planetary Health. Education: Ph.D. in Marine Biology and Geochemistry (University of Western Australia, 2017) B.Sc. Hon. in Marine Biology and Natural Resources Management (University of Western Australia, 2011) Associate’s Degree (Université du Littoral Côte d’Opale, France, 2006) Her research explores how environmental change affects marine organisms in symbiotic relationships with microscopic algae, such as corals, mollusks, anemones, and jellyfish. She pioneers coral in vitro methodologies to advance understanding of symbiosis, biomineralization, and stress adaptation. Recent publications highlight her work on nanotechnology for coral reef conservation, thermal stress mitigation using engineered nanoceria, and interdisciplinary collaborations merging art with coral research. Her studies also address trace metal roles in coral nutrition, insulin signaling pathways, and innovative imaging techniques to monitor coral health. Scientific Awards: NSF’s 2021 Coral Bleaching Research Coordination Network Early Career Training Program Award VCU’s 2021 Postdoctoral Independent Research Award Liza’s multidisciplinary approach integrates expertise from oceanography, biochemistry, nanoscience, and sustainability. She previously worked at the Australian Institute of Marine Science and has field experience as a cetacean naturalist in Iceland and a scuba diving instructor in the Mediterranean Sea and Southeast Asia.

Craig Hackingمشاهده پروفایل

دانشیار

• Trauma Radiology
• Emergency Radiology
• Forensic Radiology
• +۳ مورد دیگر

Associate Professor Craig Hacking is an Academic Lead for Clinical Radiology at the Academy for Medical Education, University of Queensland (UQ). As a consultant radiologist at Princess Alexandra Hospital and forensic radiologist at Qld Health, his expertise spans trauma, emergency, and forensic radiology, alongside radiology and anatomy education. He graduated from UQ with a MBBS in 2005 and attained FRANZCR in 2013. He is renowned for contributions to Radiopaedia.org, including editorial roles and online anatomy courses. His teaching spans over two decades, including roles with ANZERG and RANZCR examinations. He leads the UQ MD Year 2 Radiology fundamentals curriculum and developed the Radiology resources website for students. Research focuses on trauma imaging, spectral CT, and radiology education. Over 15 publications include studies on electric scooter injury patterns, spectral CT applications, and fracture classification using AI. His book Imaging for Students (2023) is a key educational resource. Awards and honors are not explicitly listed, but his leadership roles and contributions indicate significant recognition. Supervision is available for research students.

Peter H.N. de Withمشاهده پروفایل

استاد

• Computer Science
• Biomedical Engineering
• Artificial Intelligence
• +۷ مورد دیگر

Peter H.N. de With is a Full Professor at the Video Coding & Architectures group within the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He is an international expert in video compression and image analysis for health, surveillance, and automotive applications, with over 35 years of R&D experience. He leads the Video Coding & Architectures Group (SPS-VCA) and contributes to initiatives like the Center for Care & Cure Technology Eindhoven and Eindhoven MedTech Innovation Center. De With's research focuses on video/image signal processing, machine learning, and their applications in healthcare (e.g., esophageal cancer detection), security, and automotive systems. His work includes collaborations with hospitals, EU projects, and industry leaders like Bosch Security Systems and ASML. His recent publications emphasize real-time 3D processing, assembly state recognition, driver action analysis, and medical imaging advancements, reflecting his expertise in computer vision and AI. Notable scientific awards include IEEE Fellowship and multiple paper awards (CE Chester Sall, SPIE, Elsevier). Scientific Awards IEEE Fellow CE Chester Sall Award SPIE Paper Award Elsevier Journal Award Best Paper Award (2017) Second Place in CAMELYON17 Challenge De With has supervised numerous research projects and contributed to datasets in noise reduction, augmented reality, and medical imaging. He actively collaborates on AI-driven innovations for healthcare and industrial applications.

Kevin Schneiderمشاهده پروفایل

استاد

• Software Architecture and Design
• Software Evolution and Maintenance
• Software Analysis and Navigation
• +۶ مورد دیگر

Dr. Kevin Schneider is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on software architecture, evolution, analysis, and visualization, with notable work in quantum computing applications and machine learning. He also explores collaborative software teams and domain-specific languages to enhance development processes. Education: Ph.D., Computing and Information Science, Queen’s University (2000) Research Associate, Computing and Information Science, Queen’s University (1991–94) M.Sc., Computing and Information Science, Queen’s University (1990) B.Sc.(Hon), Computational Science, University of Saskatchewan (1980) Research interests include software design principles, maintenance strategies, and the integration of AI/quantum computing into software engineering. He emphasizes reproducibility in scientific workflows and tools like VizSciFlow. His work often bridges theory and practice, addressing challenges in code clone stability, user feedback management, and healthcare-related machine learning frameworks. Scientific Awards: Most Influential Paper at SCAM 2001 for his work on software engineering via source transformation. Advising and grants: While no specific advisees are listed, his research involves large-scale projects such as the Nutrient App and automated polyp segmentation tools. He collaborates on grants related to quantum computing applications and cloud-based software systems. Labs and teams: His work centers on collaborative scientific data analysis groups and developing tools for real-time groupware systems in complex workflows. He contributes to projects like CloneCognition and FSECAM, aiming to improve software design and maintenance through advanced analytics.

• Biomedical Optics
• Optical Coherence Tomography (OCT)
• Quantitative Phase Microscopy
• +۵ مورد دیگر

Adam Wax is a Professor of Biomedical Engineering and Professor of Physics at Duke University , where he leads cutting-edge research in optical spectroscopy for cancer detection , novel microscopy techniques , and low-coherence interferometry . As a member of both the Duke Cancer Institute Faculty Network and Duke Institute for Brain Sciences , he bridges engineering and biomedical applications. Education: B.S. from Rensselaer Polytechnic Institute (1993), M.A. (1996) and Ph.D. (1999) from Duke University Honors: Fellow, American Institute for Medical and Biological Engineering (2014); Fellow, SPIE (2010); Fellow, OSA (2010); NSF CAREER Award (2004) His research focuses on biomedical optics , particularly optical coherence tomography (OCT) and quantitative phase microscopy , with applications in early cancer detection , subcellular imaging , and clinical diagnostics . Recent work emphasizes low-cost, portable OCT systems for point-of-care applications and multimodal imaging combining spectroscopy with phase analysis. Scientific awards include: Fellow, American Institute for Medical and Biological Engineering (2014) Fellow, International Society for Optics and Photonics (2010) Fellow, Optical Society of America (2010) National Science Foundation CAREER Award (2004)

• Marine Biology
• Ocean Acidification
• Plankton Ecology
• +۳ مورد دیگر

Maria Alguero-Muniz is a Researcher at the University of Glasgow , affiliated with the School of Biodiversity, One Health & Veterinary Medicine . Her work focuses on ocean acidification impacts , plankton community dynamics , and marine ecosystem responses to environmental stressors. Her research spans plankton physiology , trophic interactions , and climate change adaptation in marine systems. Recent projects include eDNA metabarcoding for aquaculture monitoring and upwelling simulations to study carbon cycling. Maria's publications (2012–2025) reveal a thematic focus on marine biogeochemistry , zooplankton ecology , and novel species taxonomy . Key collaborations include institutions studying pelagic food webs and CO2-driven ecological shifts . Scientific awards include funding from The Royal Society of Edinburgh (2021–2022) for planktonic threat monitoring in salmon aquaculture. Her work has been published in Nature Climate Change , Global Biogeochemical Cycles , and Frontiers in Marine Science .

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.

Yuichi Moriمشاهده پروفایل

استاد

• Gastroenterology
• Artificial Intelligence in Medicine
• Colorectal Cancer Detection
• +۳ مورد دیگر

Professor Yuichi Mori is a leading academic at the University of Oslo , affiliated with the Faculty of Medicine and the Department of Health Management and Health Economics . He serves as a Consultant Gastroenterologist at Oslo University Hospital and a Visiting Lecturer at Showa University Northern Yokohama Hospital in Japan. His work focuses on the implementation of artificial intelligence in clinical gastroenterology , particularly in colonoscopy and colorectal cancer detection.