جست‌وجوی استاد و پژوهشگر

• Medical Image Segmentation
• Motion Segmentation
• Visual Object Tracking
• +۹ مورد دیگر

Dr. Sandeep Singh Sengar is a Senior Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. Prior to his current role, he served as a Postdoctoral Research Fellow at the Machine Learning Section of the Computer Science Department at the University of Copenhagen, Denmark. His academic journey includes a Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India, and an M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India. Sengar actively engages in research collaboration and has organized special sessions and delivered keynote presentations at international conferences. Education: Dr. Sengar holds the following academic qualifications: Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India Research Interests: Dr. Sengar's research focuses on advanced applications of machine/deep learning and computer vision, particularly in healthcare diagnostics and cybersecurity. His work combines theoretical methodologies with practical implementations, including: Medical Image Segmentation (e.g., brain tumors, functional connectivity) Motion Segmentation and Tracking for surveillance and activity recognition Visual Object Tracking and Recognition in dynamic environments Video Compression techniques leveraging adaptive optimization Cybersecurity frameworks targeting DDoS attack mitigation Exploring Metaverse applications in healthcare and AI ethics Publications Trends: His recent articles emphasize interdisciplinary innovation, with a strong focus on healthcare AI and cybersecurity. Key themes include hybrid neural networks for medical imaging, entropy-driven frameworks for neurological disorders, and proactive network threat detection using deep learning. He also investigates emerging technologies like the Metaverse's role in healthcare systems and generative AI's broader applications. Scientific Awards: No scientific awards were explicitly mentioned in the provided texts. Advising & Grants: While Dr. Sengar has not listed formal advisees or grant details, his research mentorship is evident through collaborative projects and conference contributions. He actively participates in organizing special sessions and peer-review processes, fostering academic-industry partnerships. Labs & Teams: Though no specific labs are named, his work aligns with Cardiff Met's research centers and innovation initiatives, such as the PDR International Centre for Design and Research and ZERO2FIVE Food Industry Centre.

Jing Zhangمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Arash Mohammadi is an Assistant Professor in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Canada. He holds a PhD from the University of Toronto (2015) and was formerly affiliated with Amirkabir University of Technology, Iran. His research bridges signal processing, artificial intelligence, and biomedical applications. Research Interests: Signal and image processing for healthcare (e.g., lung cancer detection, ECG analysis) Machine learning for smart grids and cyber-physical systems AI in mobile edge computing and 6G networks Transformer and diffusion models for medical and motion data Federated and efficient deep learning for edge devices His recent publications (2021–2025) in top venues like IEEE TSP, ICASSP, and AAAI demonstrate a strong focus on applying cutting-edge AI—especially vision transformers, Mamba architectures, and diffusion models—to critical domains such as medical diagnostics, gesture recognition, and network security. Trends include multimodal fusion, uncertainty quantification, and efficient model design. Scientific Contributions: Developed novel frameworks like NYCTALE and MIXCAPS for lung nodule malignancy prediction Introduced CacheMamba and TEDGE-Caching for edge network optimization Advanced EMG-based gesture recognition using hybrid and transformer models Contributed to cybersecurity in smart grids via attack detection models He actively advises students and collaborates with researchers such as Konstantinos N. Plataniotis and Jamshid Abouei. He has contributed to special issues on neurorehabilitation and AI for COVID-19 diagnosis. His work often involves interdisciplinary teams and real-world applications in healthcare and smart infrastructure.

Michael Bretthauerمشاهده پروفایل

استاد

• Colorectal Cancer Screening
• Artificial Intelligence in Endoscopy
• Gastrointestinal Endoscopy
• +۴ مورد دیگر

Michael Bretthauer is a Professor at the Skills Training Centre , Faculty of Medicine, University of Oslo. His research focuses on colorectal cancer screening , artificial intelligence in endoscopy , and clinical epidemiology , with significant contributions to improving adenoma detection , microbiome therapies , and patient safety in medical devices . University: University of Oslo School: Faculty of Medicine Department: Skills Training Centre Email: michael.bretthauer@medisin.uio.no Research Areas: Colorectal Cancer, AI in Endoscopy, Inflammatory Bowel Disease, Medical Guidelines, Health Technology Assessment His recent publications highlight advancements in AI-aided colonoscopy , fecal microbiota transplantation , and screening protocol optimization . He leads major projects including COLONIZE (microbiota therapy) and EPoS (polyp surveillance). Collaborations with institutions like the European Society of Gastrointestinal Endoscopy and NordICC underscore his international influence. Key themes in his work include screening effectiveness , cost-effectiveness of AI , and balancing benefits/harms in medical guidelines . He has co-authored over 30 studies in 2025 alone, reflecting his active role in shaping endoscopy standards and public health policy .

• Biomedical Engineering
• Biomedical Image Processing
• Medical Image Analysis
• +۳ مورد دیگر

EVGİN GÖÇERİ is an Associate Professor at Akdeniz University's Faculty of Engineering, Department of Biomedical Engineering. She has been serving in this position since 2018, having previously held the rank of Assistant Professor in the Department of Computer Engineering from 2015-2018. Her academic career is complemented by a postdoctoral fellowship at Ohio State University's College of Medicine in Biomedical Informatics (2015-2016). Dr. GÖÇERİ earned her Doctorate in Electrical and Electronics Engineering from Izmir Institute of Technology (2007-2013) and completed her undergraduate studies in Computer Engineering at Ege University (1997-2002). Her research primarily focuses on biomedical image processing, deep learning applications in medical diagnostics, and computer-aided diagnosis systems. Her scholarly work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging challenges, particularly in dermatology, oncology, and neuroimaging. Recent publications reveal extensive work on transformer architectures, GAN-based augmentation, and hybrid deep learning models for various medical image analysis tasks. Her research consistently addresses practical clinical challenges including polyp detection, skin cancer classification, brain tumor segmentation, and liver tumor analysis. Dr. GÖÇERİ has established herself as a productive researcher with 80 publications indexed in Web of Science, 30 in Scopus, and impressive citation metrics including an h-index of 683 in WoS and 2048 in Scopus. Her work spans journal articles, conference proceedings, and book chapters, demonstrating both depth and breadth in biomedical engineering research. Her technical expertise spans image processing algorithms, deep neural network architectures, and medical image analysis systems, with particular strengths in applying these technologies to real-world clinical problems. The trajectory of her publications indicates growing sophistication in model architectures, moving from traditional CNNs to more advanced hybrid approaches incorporating transformers and attention mechanisms.

Josh Koenigمشاهده پروفایل

استادیار

• Adaptive immunity
• Allergy
• Cellular immunology
• +۷ مورد دیگر

Josh Koenig is an Assistant Professor in the Department of Medicine at McMaster University , focusing on adaptive immunity , allergy , cellular immunology , and humoral immunology . His work bridges fundamental immunology with clinical applications to address challenges in food allergy and respiratory immunology . Research Interests : Dr. Koenig investigates B cell memory in allergic responses, IgE dynamics , and microbial regulation of immunity. His studies explore how commensal organisms and environmental factors (e.g., cigarette smoke) influence Th2 immunity and allergen immunotherapy . He co-led the discovery of a new cell type that remembers allergies, with implications for transformative treatments . Recent Publications highlight his work on disease tolerance in intestinal infections, microbial metabolism of allergens, IgG1-independent memory , and novel imaging techniques for immune cell analysis. His teaching roles include courses in cell biology and immunology (2015–2025). Dr. Koenig’s lab collaborates with ALK-Abello A/S and utilizes mucosal vaccine delivery strategies against SARS-CoV-2 .

• Gastroenterology
• Pancreatology
• Hereditary Tumor Syndromes
• +۲ مورد دیگر

ARCIDIACONO PAOLO GIORGIO is an Associate Professor at Vita Salute San Raffaele University within the Faculty of Medicine and Surgery , specialized in Gastroenterology (MEDS-10/A) . He is actively involved in teaching Gastroenterology 1 LU (3.2 CFU) across multiple 2025 academic programs and serves as a key figure in hereditary gastrointestinal syndrome research and clinical management. Academic Appointments : Associate Professor (2017-present), Researcher (2013-2017), University of Parma Associate Professor (2002-2013) Clinical Leadership : Director of Gastroenterology Specialization School and Head of the multidisciplinary Gastro Per Me center since 2015 His research focuses on pancreatic cancer progression , hereditary tumor syndromes , and endoscopic intervention optimization , evidenced by recent publications in Annals of Surgery , Gastrointestinal Endoscopy , and Cancer . Key article themes include: Pancreatic cancer survival analysis Genomic risk loci identification Endoscopic ultrasound techniques Surveillance interval personalization Smoking's role in malignancy progression Patient communication at diagnosis He received the Premio Fondazione Guido Berlucchi in 2003 and maintains editorial roles in Digestive and Liver Disease and Endoscopic Ultrasound . As course coordinator for Digestive System Diseases (International MD Program) and Gastrointestinal Diseases , he integrates clinical expertise with academic leadership.

• Deep Learning
• Federated Learning
• Data Analysis on Graphs
• +۹ مورد دیگر

Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.

Brian Sullivanمشاهده پروفایل

استادیار

• Colorectal Cancer
• Cancer Screening
• Genomic Risk Stratification
• +۳ مورد دیگر

Brian Sullivan, MD, MHS is an Assistant Professor in the Department of Medicine, Division of Gastroenterology and Hepatology at Duke University School of Medicine and a Physician Scientist at the Durham Veterans Affairs Medical Center. His research focuses on optimizing colorectal cancer prevention through genomic risk stratification and clinical decision support systems for personalized screening strategies. Dr. Sullivan's primary research interest is developing novel approaches for colorectal cancer risk assessment during screening and surveillance. His work investigates how genomic data and clinical factors can improve risk stratification to target intensive interventions to high-risk individuals while reducing unnecessary procedures for lower-risk populations. Current projects include clinical decision support tool implementation, stool-based test validation, and polygenic risk score development within Veterans Health Administration cohorts. Analysis of his 34 publications (2015-2025) reveals consistent focus on colorectal cancer screening optimization, with recent work emphasizing real-world evidence generation, patient safety in endoscopy, and genomic risk prediction. Over 70% of his publications involve Veterans Affairs collaborations, highlighting the VA's central role in his research program. Dr. Sullivan actively collaborates with multidisciplinary teams across Duke University Medical Center and the Durham VA, including gastroenterologists, geneticists, epidemiologists, and health services researchers. His work contributes to national guidelines for colorectal cancer surveillance through participation in large cooperative studies like the CONFIRM trial.

• Colorectal Cancer Screening
• Gastroenterology
• Endoscopy
• +۳ مورد دیگر

Joseph C. Anderson, MD, serves as Professor of Medicine at Dartmouth College's Geisel School of Medicine, with additional appointment as Associate Professor of Medicine at University of Connecticut School of Medicine. His clinical and research focus centers on gastrointestinal oncology and endoscopic screening methodologies. His academic training includes: B.S.E. in Engineering from University of Pennsylvania (1984) M.D. from Mount Sinai School of Medicine (1988) Internal Medicine residency at Stony Brook University (1991) Gastroenterology fellowship at Stony Brook University (1996) Dr. Anderson's research program investigates colorectal neoplasia detection mechanisms, with emphasis on flat and sessile serrated adenomas, smoking-related cancer risks, and optimization of colonoscopic screening techniques. His work has directly influenced national clinical guidelines through contributions to the American College of Gastroenterology's colorectal cancer screening protocols. Analysis of his 2009-2011 publications reveals consistent focus on endoscopic imaging advancements, population-based screening efficacy, and risk stratification for colorectal cancer. His research frequently employs high-definition colonoscopy in asymptomatic cohorts to identify precancerous lesions and evaluate demographic variables. No scientific awards were documented in the source materials. While student mentorship and grant funding details were not specified, his collaborative multi-institutional studies indicate active research leadership. Laboratory and team affiliations were not explicitly referenced in available documentation.

Philippe Laissueمشاهده پروفایل

مدرس ارشد

• Bioimaging
• Microscopy
• Quantitative Image Analysis
• +۵ مورد دیگر

Dr. Philippe Laissue is a Senior Lecturer (Associate Professor equivalent) and Principal Investigator in Bioimaging at the University of Essex, based in the School of Life Sciences. He specializes in microscopy and quantitative image analysis, focusing on non-invasive live fluorescence imaging applications in biomedical and environmental research. His research spans systems across phyla, including plants, mammalian cells, fungi, corals, and microbiomes, with expertise in scales from nanometers to centimeters. Key interests include ROS signaling, biofilm structure, and phototoxicity mitigation. He chairs an international phototoxicity working group and collaborates with industrial partners. Education: PhD (2004, Biozentrum, University of Basel), MSc Zoology (1999, Université de Fribourg), and a Postgraduate Certificate in Higher Education (2010). As Director of Bioimaging, he oversees teaching modules, equipment management, and technical support since 2009, maintaining infrastructure and advising researchers on imaging technologies. Research trends in his publications emphasize bioimaging innovation (e.g., light-sheet microscopy for corals, phototoxicity assessment tools), ROS signaling mechanisms in plants/cells, and interdisciplinary applications in marine and biomedical systems. His work bridges fundamental biology with technological advancements, addressing environmental and health challenges. Grants and funding: Not explicitly listed here but implied through equipment management roles and collaborative projects. Advising: No named students listed, but supervises research staff and students in bioimaging training. Labs/Teams: Leads the Bioimaging facility at the University of Essex, managing a team of technicians and researchers.

C James Sungمشاهده پروفایل

استاد

• Gynecological Pathology
• Breast Pathology
• Cytopathology
• +۳ مورد دیگر

Dr. C James Sung is a Professor of Pathology and Laboratory Medicine at Brown University . He specializes in gynecological pathology , breast pathology , and cytopathology , with a focus on tumor pathology and diagnostic applications like the Pap smear . Research Highlights: Investigates HPV-related lesions and gynecological cancers . Explores histological grading and molecular markers in breast and endometrial cancers. Contributes to cytology testing standards and diagnostic accuracy studies. Recent Publications analyze trends in K-ras mutations , mucinous differentiation , and HPV testing across various female cancers, emphasizing tumor biology and diagnostic innovation. Teaching Initiatives include a Chinese Medical Elective for medical students, focusing on Mandarin language skills for clinical communication with Chinese-speaking patients.

Amar Hekaloمشاهده پروفایل

پژوهشگر

• Medical Image Processing
• Natural Language Processing (NLP)
• Information Extraction
• +۶ مورد دیگر

Amar Hekalo is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems (1992-2025) at the University of Würzburg , within the Faculty of Mathematics and Computer Science . Since November 2018, he has collaborated with the University Hospital of Würzburg to enhance medical documentation through speech/image processing , deep learning , and rule-based methods . Education: Master's degree in Physics His research focuses on automated image processing and NLP , covering information extraction , image classification , segmentation , object recognition , and medical applications . Recent work includes federated learning for private data reconstruction and AI applications in cardiac/radiological imaging. His publications reveal expertise in medical AI , particularly gastroenterology , cardiology , and radiology . He supervises Bachelor's and Master's students in AI applications for healthcare, logistics, and education. He contributes to open-source tools and actively seeks student assistants with Angular/NodeJS skills for radiology-focused documentation and e-learning tools.

John Levenickمشاهده پروفایل

استاد

• Gastroenterology
• Hepatology
• Therapeutic Endoscopy
• +۴ مورد دیگر

John Levenick, MD, is a Professor at the Pennsylvania State University College of Medicine and holds the Margot E. Walrath Career Development Professorship. He serves as the Program Director for the Advanced Endoscopy Fellowship and Director of Endoscopic Education in the Department of Medicine, Division of Gastroenterology and Hepatology. His clinical and research focus spans therapeutic endoscopy, gastrointestinal oncology, and hepatology. Research Interests Precancerous lesions of the gastrointestinal system Endoscopic techniques for large colon polyps and Barrett’s esophagus Reducing local recurrence of gastrointestinal lesions ERCP complications and hepatobiliary disease Screening colonoscopy quality metrics Hereditary cancer syndromes in gastrointestinal contexts Publication Trends Recent publications emphasize endoscopic innovations for gastrointestinal lesion management, including randomized trials on polyp resection techniques (2025), systematic reviews on ERCP pancreatitis prevention (2024), and studies on clinical practice impacts on adenoma detection (2023). Collaborative international research features prominently, with focus areas in therapeutic endoscopy, colon cancer prevention, and genetic gastrointestinal disorders. Awards Alpha Omega Alpha honor society inductee (2008) Penn State Hershey Medical Center Doctor/Patient Satisfaction Award (2014/15, 2015/16) Education & Fellowship BA, Boston College MD, Stritch School of Medicine, Loyola University of Chicago (2006) Residency, Internal Medicine, Dartmouth-Hitchcock Medical Center (2010) Fellowship, Gastroenterology, Dartmouth-Hitchcock Medical Center (2013) Fellowship, Advanced Endoscopy, Dartmouth-Hitchcock Medical Center (2014) Professional Roles Director of Endoscopic Education Program Director, Advanced Therapeutic Endoscopy Fellowship Program Director, Gastroenterology and Hepatology Fellowship

Tonya Kaltenbachمشاهده پروفایل

استاد

• Gastroenterology
• Endoscopy
• Colorectal Cancer Screening
• +۵ مورد دیگر

Tonya Kaltenbach, MD is a Professor in the Department of Medicine at the University of California, San Francisco (UCSF) School of Medicine. She specializes in gastroenterology and endoscopy, with a focus on advancing endoscopic techniques, colonoscopy quality improvement, and medical education. Her work emphasizes simulation-based training, artificial intelligence integration in endoscopy, and colorectal cancer screening. Education: Dr. Kaltenbach earned her BA in Biologic Basis of Behavior from the University of Pennsylvania (1996), MD from Jefferson Medical College (2001), and completed residency at Stanford University (2004). She further specialized in gastroenterology at Stanford (2007) and holds an MSc in Clinical Research from UCSF (2007). Additional training includes advanced endoscopy fellowships in Japan (2009) and the US. Research: Her research spans endoscopy quality metrics, non-polypoid colorectal neoplasms, and improving adenoma detection rates. She leads initiatives like the VA Endoscopy Quality Improvement Program (VA-EQuIP) and pioneered simulation-based mastery learning for endoscopy training. Key contributions include advancing optical diagnosis techniques and AI applications in real-time polyp histology prediction. Awards: Recognized with the Distinguished Master Endoscopist award (ASGE, 2022), multiple Education Audiovisual Awards, and grants from institutions like the VA and NIH. Her work has been published in high-impact journals such as Gastroenterology and Annals of Internal Medicine . Grants & Teams: Collaborates on national projects like the VA-EQuIP trial and the Bali Endoscopy Simulation Training (BEST) program. Leads teams focusing on endoscopy training standards and colorectal cancer prevention strategies.