جست‌وجوی استاد و پژوهشگر

Robert D. Ambrosiniمشاهده پروفایل

دانشیار

• Medical Imaging
• Radiology
• Cardiothoracic Imaging
• +۲ مورد دیگر

Dr. Robert D. Ambrosini is an Associate Professor in the Department of Imaging Sciences at the University of Rochester School of Medicine and Dentistry. His clinical focus lies in Cardiothoracic Imaging, where he applies expertise from his dual training in Internal Medicine and Radiology. MD and PhD from University of Rochester (2011) Completed residency in Radiology (2012-2016) and fellowships in Magnetic Resonance Imaging (2016) and Cardiothoracic Imaging/Informatics (2017) at University of Rochester Medical Center His research integrates advanced imaging technologies with informatics systems, particularly in diagnostic applications. He has contributed to publications on computer-aided detection in medical imaging, including work on brain tumor analysis. Dr. Ambrosini has received numerous accolades, including the Best Oral Presentation by Junior Faculty (2018), Prutton Medal in Chemical Engineering (2002), and multiple scholarships and honor society memberships. He has also been recognized for teaching excellence, receiving the Teacher of the Year award (2004).

• Nuclear Medicine
• Radiology
• Cardiac Imaging
• +۳ مورد دیگر

Ties Mulders is a Researcher in the Department of Radiology & Nuclear Medicine at Erasmus University Medical Center (Erasmus MC), specializing in advanced imaging techniques with a focus on PET-CT applications in cardiac and thoracic pathologies. His work bridges nuclear medicine, radiology, and clinical cardiology to improve diagnostic accuracy for complex conditions. His core research spans: Cardiac PET-CT Imaging : Investigating FDG-PET/CT variations in prosthetic heart valves and post-sternotomy complications to differentiate infection from inflammation Lung Cancer Diagnostics : Validating AI-driven CAD systems for lung nodule characterization and optimizing radiation-safe follow-up protocols for pulmonary carcinoids Thoracic Lesion Management : Leading international Delphi initiatives to standardize diagnostics for mediastinal cystic lesions through multidisciplinary consensus Analysis of his 2023-2025 publications reveals a strong trend toward clinical translation of imaging biomarkers, particularly in reducing false positives in cardiac prostheses evaluation and minimizing radiation exposure in lung cancer surveillance. His work consistently integrates quantitative imaging with real-world clinical decision-making across nuclear medicine and radiology domains. No scientific awards were documented in the provided profile. While student mentoring details are absent, his collaborative research involves extensive cross-institutional partnerships with cardiologists, thoracic surgeons, and oncology teams. Current projects include the DETECTION initiative for mediastinal lesions and validation studies for AI-based nodule detection systems, though specific grant information isn't disclosed. Dr. Mulders operates within Erasmus MC's Radiology & Nuclear Medicine department, contributing to multidisciplinary teams focused on cardiac imaging innovation and thoracic oncology diagnostics through active participation in prospective cohort studies and international consensus projects.

• Geometric Algebra
• Clifford Algebra
• Medical Imaging
• +۵ مورد دیگر

Silvia Giuseppina Franchini serves as a Contract Teacher in the Department of Biomedicine, Neuroscience and Advanced Diagnostics at the University of Palermo's School of Medicine and Surgery. Her academic career demonstrates a strong interdisciplinary focus bridging advanced mathematical frameworks with practical medical applications. Dr. Franchini's research interests include: Geometric and Clifford Algebra applications in medical imaging Hardware acceleration for geometric algebra operations Machine learning approaches for medical diagnosis, particularly for Crohn's disease Embedded systems design for real-time image processing Robotics control systems using conformal geometric algebra Medical image analysis and 3D reconstruction Her publication record spanning fifteen years reveals an evolution from foundational hardware implementations to sophisticated medical applications. She has developed specialized architectures including the GAPPCO system, ConformalALU coprocessor, and GAPP compiler, demonstrating innovative approaches to implementing geometric algebra in hardware for medical imaging. Her recent work emphasizes machine learning applications for Crohn's disease classification, showing how mathematical frameworks can translate to clinical diagnostic tools. Dr. Franchini maintains active research engagement with publications continuing through 2022, indicating ongoing contributions to the field of medical imaging technology development. Her work consistently addresses computational challenges while maintaining clinical relevance, particularly in gastroenterology through her Crohn's disease research.

• Metrology
• Optical Engineering
• Manufacturing Engineering
• +۴ مورد دیگر

Dr. Jon Petzing is a Senior Lecturer in Metrology and Director of Undergraduate Admissions at Loughborough University's Wolfson School of Mechanical, Electrical & Manufacturing Engineering. He holds a BEng (Hons) in Mechanical Engineering, a Diploma of Industrial Studies (DIS), and a PhD in Optical Metrology, all from Loughborough University. His career spans roles in aerospace, confectionery manufacturing, and academia, with a focus on optical metrology and precision engineering. Research interests include flow cytometry data analysis, low-coherence interferometry, surface texture parameters, and manufacturing process optimization. Notable contributions include advancements in automated data analysis for cell therapy manufacturing and innovations in in-situ measurement techniques. Key awards include Chartered Engineer status (CEng), Fellow of the Higher Education Academy (FHEA), and the Helmut N Friedlaender Award for Leadership. Publications highlight trends in synthetic data applications, operator variability reduction, and interdisciplinary metrology solutions for biomedical and industrial challenges. His work bridges academic research with practical manufacturing and clinical applications, emphasizing precision and automation.

• Medical Imaging
• Computer Science
• Medical Diagnostics
• +۳ مورد دیگر

Dr. Nadia Smaoui Zghal is a distinguished researcher in medical imaging and computer science, focusing on healthcare applications. Her work integrates advanced computational techniques to enhance diagnostic processes, including AI-driven solutions for diseases like COVID-19, breast cancer, and melanoma. Supported by the British Council, her research emphasizes early detection and precision medicine. Research Interests: Medical Imaging & AI Applications Data Mining for Healthcare Analytics Image Processing in Dermatology Pandemic Response Technologies Advising & Grants: Her research has been sponsored by the British Council, reflecting its academic significance. No formal advisees are listed in available records. Labs/Teams: No specific lab or team affiliations mentioned.

Sangam Ankmnal Veerannaمشاهده پروفایل

استادیار

• Psychoacoustics
• Electrophysiology
• Auditory Processing Disorder
• +۳ مورد دیگر

Dr. Sangam Ankmnal Veeranna is an Assistant Professor in Audiology at the University of Southern Mississippi. He holds a Ph.D. in Hearing Science from Western University, Canada (2017), following a postdoctoral fellowship at the National Center for Audiology there. His research focuses on auditory processing in individuals with and without listening difficulties, employing psychoacoustic and electrophysiological methods. He teaches courses such as SHS-302 Anatomy and Physiology of Hearing and SHS-701 Psychophysics . Dr. Veeranna’s work integrates clinical audiology with cutting-edge technologies, including machine learning applications for auditory diagnostics. His recent studies explore the impact of maternal COVID-19 during pregnancy on infant hearing, mobile psychoacoustic testing systems, and neural mechanisms behind auditory processing disorders. His expertise spans psychoacoustics, electrophysiology, and APD assessment. Notable contributions include developing novel methods to simultaneously assess subcortical and cortical auditory sensitivity and advancing cluster analysis techniques for auditory brainstem responses. He actively publishes in journals like American Journal of Otorhinolaryngology and Frontiers in Audiology and Otology .

• Machine Learning
• Deep Learning
• Biomedical Signal Processing
• +۷ مورد دیگر

Yue Zhang is a Professor at Westlake University's School of Engineering with an exceptionally active research program spanning multiple disciplines. With dozens of publications in high-impact venues during 2025-2026 alone, Dr. Zhang demonstrates leadership in interdisciplinary research connecting computer science with practical applications in healthcare, environmental science, and education. Affiliation: Westlake University, School of Engineering Research Areas: Machine Learning, Biomedical Engineering, Computer Vision, Environmental Monitoring Publication Output: 90+ papers indexed in dblp (1996-2026), with remarkable productivity in recent years Dr. Zhang's research interests center on applying advanced machine learning techniques to solve real-world problems across multiple domains. The work demonstrates particular strength in biomedical signal processing (EEG, ECG, HRV analysis), environmental monitoring (mangrove forests, crop yield estimation, nitrogen content), and educational technology (AI integration in classrooms). The research approach typically combines deep learning architectures with domain-specific knowledge to create interpretable and effective solutions. The publication record reveals a strong trend toward multimodal and interdisciplinary approaches, with increasing focus on practical applications of AI in healthcare diagnostics, environmental conservation, and educational transformation. Recent work shows particular innovation in attention mechanisms, tensor-based optimization, and knowledge distillation techniques adapted for specialized domains. Dr. Zhang's collaborative network spans multiple institutions and disciplines, with frequent co-authorship patterns suggesting established research groups in both computer science and application domains. The work appears well-funded given the range of sophisticated applications and high publication output across top venues.

Harshal Sanghviمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Harshal Sanghvi, Ph.D., currently serves as a postdoctoral fellow in the Charles E. Schmidt College of Medicine and holds an adjunct professor role in the College of Business at Florida Atlantic University. He earned his doctorate in computer engineering from FAU. His professional roles include program chair for the Institute for Certified Computing Professionals and vice chair for the Health-Tech and Life Sciences committee at South Florida Tech Hub. Sanghvi chairs the department of technology and clinical trials at Advanced Research. His research focuses on medical imaging, computer vision, augmented reality, artificial intelligence, business intelligence, and embedded systems. He has contributed extensively to peer-reviewed journals and conferences, with recent work emphasizing AI-driven healthcare solutions, cybersecurity, and interdisciplinary applications of technology. Key themes in his publications include AI in ophthalmic diagnostics, IoT in healthcare, digital twin models for ophthalmology, and novel algorithms for medical imaging analysis. His cybersecurity research addresses threats in smart cities and IT systems, while his engineering work explores actuator design and material reinforcement. No scientific awards or grants are explicitly mentioned in the provided texts. His advisory and collaborative efforts span clinical trials, medical device development, and tech policy initiatives in South Florida's innovation ecosystem. Sanghvi’s work bridges clinical medicine and business strategy through technology, exemplified by his roles in healthcare tech hubs and academic leadership in interdisciplinary research areas.

McKay Sohlbergمشاهده پروفایل

استاد

• Cognitive Rehabilitation
• Traumatic Brain Injury (TBI)
• Neuroscience
• +۴ مورد دیگر

Professor McKay Sohlberg is a renowned academic and clinician in the field of cognitive rehabilitation, affiliated with the University of Oregon's College of Education and its Department of Communication Disorders and Sciences. She has held the HEDCO Endowed Professorship since 2010 and is a Fellow of the American Speech-Language-Hearing Association (ASHA). Her work focuses on developing interventions for cognitive impairments post-acquired brain injury, with a particular emphasis on assistive technology and client-centered goal setting. Education : Ph.D. in Educational Psychology, University of Washington (1990) M.S. in Speech-Language-Hearing Sciences, University of Washington (1984) B.A. in Human Biology, Stanford University (1982) Research Interests : Professor Sohlberg’s research spans cognitive rehabilitation strategies for TBI, concussion recovery, and postsecondary education support for individuals with brain injuries. She co-directs the Brain Injury and Concussion Clinic (BrICC) and has pioneered tools like the Attention Process Training-3 (APT-3) and the Read, Understand, Learn & Excel (RULE) framework. Awards : 2020 Mitchell Rosenthal Award for Scientific and Clinical Achievement 2018 Veteran’s Administration Champion 2013 Oregon Pioneer Award for Clinical Research 2008 Senior Distinguished Teaching Award (University of Oregon) Advising & Grants : She leads federally funded grants on assistive technology and has advised numerous graduate students. Her clinical contributions include evidence-based practice guidelines for TBI and social communication rehabilitation. Labs/Teams : Co-directs the Brain Injury and Concussion Clinic (BrICC) within the HEDCO clinic, integrating clinical care and research.

• Massive MIMO
• Cognitive Radio Networks
• Smart Grids
• +۶ مورد دیگر

Professor Sangarapillai Lambotharan is a Professor of Digital Communications and Head of the Signal Processing and Networks Research Group at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. He holds qualifications including BEng, PhD, DIC, PGCAP, and is a Senior Member of IEEE, Fellow of IET, and Fellow of the Higher Education Academy. His research focuses on advanced wireless communication technologies such as Massive MIMO, Cognitive Radio Networks, and Smart Grids, alongside Machine Learning, Data Mining, and Cyber Security. He has authored over 200 publications in top-tier journals and conferences. His work integrates machine learning with signal processing to address challenges in adversarial attacks, federated learning, and network optimization. Recent research emphasizes intelligent reflecting surfaces (RIS), OTFS satellite communications, and federated learning defenses against model poisoning. Prof. Lambotharan's contributions span theory development, algorithm design, and practical implementations in 5G/6G systems, UAV networks, and edge computing environments. He leads collaborative projects on secure communications, distributed intelligence, and blockchain-based network security mechanisms. Prof. Lambotharan's academic leadership includes editorial roles at IET Signal Processing and teaching Digital Signal Processing and Information Theory. His lab explores cutting-edge topics like RIS-aided systems, anomaly detection in IoT, and context-aware caching strategies for heterogeneous networks. Key achievements include pioneering work on Kalman filtering for channel estimation and game-theoretic solutions for resource allocation in cognitive radio networks.

Carlo Russoمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Deep Learning
• Neuroscience
• +۳ مورد دیگر

Dr. Carlo Russo is an Honorary Senior Lecturer at the Macquarie Medical School, Macquarie University, and a Research Associate at the Computational NeuroSurgery (CNS) Lab. His research focuses on applying non-linear geometry, fractal analysis, and deep learning to image analysis in neurosciences. He previously spent over 15 years at Humanitas Research Hospital in Milan, Italy, developing software/hardware for computer-aided diagnosis in clinical workflows. His work includes fractal geometry applications in histopathology and radiology images. Research Interests: Medical Imaging, Deep Learning, Fractal Geometry, Neuro-Oncology, and Biomedical Engineering. He has authored/co-authored 18 peer-reviewed articles and 5 patents. Collaborations & Projects: Active in the 'Computational Neurosurgery Lab' project (2019–present), collaborating on AI-driven approaches for glioma diagnosis and neurosurgical applications. Recent work includes MRI-deep learning fusion for tumor segmentation and synthetic PET generation. Lab Affiliation: Computational NeuroSurgery (CNS) Lab, Macquarie University.

Kosmia Loizidouمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Medical Image Processing
• Machine Learning
• +۲ مورد دیگر

Kosmia Loizidou serves as a Post-Doctoral Researcher at the KIOS Research and Innovation Center of Excellence, University of Cyprus, following the completion of her Ph.D. in Electrical Engineering from the same institution in June 2023. Her doctoral research focused on Detection, Classification, and Prediction of Breast Abnormalities using Sequential Mammograms. Her academic credentials include: BSc in Mechanical and Manufacturing Engineering with a minor in Biomedical Engineering from the University of Cyprus (2016) MSc in Electrical Engineering, specializing in Biomedical Engineering, from the University of Cyprus (2018) PhD in Electrical Engineering from the Department of Electrical and Computer Engineering, University of Cyprus (2023) Dr. Loizidou's research centers on biomedical engineering with a specialized focus on medical image processing and machine learning applications for cancer diagnosis. She pioneers methodologies for breast cancer detection through temporal subtraction of sequential mammograms, developing advanced computer-aided diagnosis systems that classify masses and micro-calcifications while predicting near-term cancer occurrence. Her work bridges engineering innovation with clinical oncology to enhance early detection accuracy. Analysis of her publication trajectory reveals a concentrated research program leveraging machine learning and image processing on sequential mammograms. Key themes include temporal subtraction techniques for improving cancer localization, classification of benign versus malignant abnormalities, and predictive modeling of near-term breast cancer development. Her contributions span algorithm development, comprehensive reviews, and specialized applications for micro-calcifications and mass detection. Scientific awards: No awards or fellowships are documented in the provided information. Regarding academic mentoring and funding, no details are available about supervised students or secured research grants. Her professional activities appear centered on independent research within the KIOS framework. She operates within the KIOS Research and Innovation Center of Excellence, a premier hub for intelligent systems research at the University of Cyprus focused on critical infrastructure networks. This environment supports her interdisciplinary work at the intersection of biomedical engineering, computer science, and oncology.

• Medical Image Analysis
• Video Surveillance Systems
• Pattern Recognition
• +۳ مورد دیگر

Lorenzo Putzu is a Researcher (Assistant Professor) at the University of Cagliari's Department of Computer Science. He holds a PhD in Computer Science from the same institution (2016). His work focuses on pattern recognition techniques applied to medical imaging and video surveillance, including automated diagnosis support systems, security applications, and synthetic data generation for AI training. Since 2017, he has been part of the PRA Lab, contributing to projects like the IMMAGINA initiative for trustworthy AI in surveillance. Education: Bachelor's Degree in Computer Science (2010) Master's Degree in Computer Science (2012) PhD in Computer Science (2016) Research Interests: Medical image analysis for automated diagnosis (e.g., blood cell classification, histopathology) Video surveillance systems using AI (e.g., person re-identification, crowd counting) Synthetic data generation for improving model robustness Federated learning in healthcare applications Adversarial defense mechanisms in computer vision Key Projects: Development of MIRIAMS for multiple sclerosis segmentation Advancing leukocyte classification methods for hematological analysis Pioneering work on federated learning for cell nuclei segmentation Grant & Advising Activities: No explicit grants or advising roles listed Labs/Teams: Active contributor to the PRA Lab, collaborating on interdisciplinary AI projects in healthcare and security.

Dario Siegenمشاهده پروفایل

پژوهشگر

• Science and environmental communication
• Political communication
• Social change
• +۱ مورد دیگر

Dario Siegen is a Researcher at the Institute of Communication Science and Media Research (IKMZ) at the University of Zurich, affiliated with the Department of Public Sphere and Society under Prof. Mark Eisenegger. He also held roles as Student Conference Coordinator for the International Sustainable Campus Network at ETH Lausanne and Research/Teaching Assistant during 2016-2018 under Prof. Mike Schäfer. His academic background includes a Master’s in Development Studies (Graduate Institute Geneva, 2018-2020), and a dual Bachelor’s in Journalism & Communication Science and Business Administration from the University of Zurich (2014-2017). Research focuses on intersections between science/environmental communication, political dynamics, societal transformation, and computational methods. Notable projects include analyzing climate change media coverage during the pandemic, academic-industry engagement patterns, and South African climate perception studies. His work bridges qualitative and quantitative methods, often using automated content analysis. Key awards include the Swiss Study Foundation’s recognition (2019) and the University of Zurich’s Outstanding Academic Achievement Award (2018). His contributions span peer-reviewed journals like Environmental Communication and Plos One, with recent conference presentations at ECREA (2022) and ICA (2022) on topics ranging from vaccination debates to media quality in conflict reporting. Active in interdisciplinary teams such as the fög Research Center, his research emphasizes societal communication challenges in contemporary issues.

Cristina Lopesمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Distributed Virtual Environments
• +۵ مورد دیگر

Cristina Lopes is a Professor of Informatics at the Donald Bren School of Information and Computer Sciences , University of California, Irvine. She holds administrative roles including Director of the Master of Software Engineering Program and former Director of the Institute for Software Research . Her research focuses on software engineering, programming languages, and distributed systems, with contributions to open-source projects like OpenSimulator and AspectJ. She has authored the influential book Exercises in Programming Style . Education: PhD from Northeastern University, MS/BS from Instituto Superior Técnico (Portugal). Awards include IEEE Fellow (2019), ECOOP Test of Time Award (2017), and ACM Distinguished Scientist (2011). She co-founded a virtual reality company for urban redevelopment and leads NSF-funded projects, including the prestigious CAREER Award. Research Interests: Large-scale software systems, aspect-oriented programming, code clone detection, and applying AI to programming challenges. Her work bridges theory and practice, with applications in urban simulation and healthcare systems. Awards and Grants: Over 15 awards including national and international distinctions. Grants focus on scalable software tools and environmental sustainability in computing. Labs/Teams: Leads the Software Engineering Research Group and collaborates with industry partners on open-source infrastructure and AI-driven development tools.