جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Signal and Image Processing
• +۴ مورد دیگر

Dr. Salim Bouzerdoum is a Senior Professor of Computer Engineering at the University of Wollongong (UOW), affiliated with the School of Electrical, Computer & Telecommunications Engineering. He holds a Ph.D. and M.Sc. in Electrical Engineering from the University of Washington. His roles include former Associate Dean for Research (2007–2013) and Head of School (2004–2006). He has served on the Australian Research Council panels and held visiting professorships globally. Education: Ph.D. in Electrical & Computer Engineering, University of Washington, Seattle, USA M.Sc. in Electrical Engineering, University of Washington, Seattle, USA Research Interests: His work focuses on Artificial Intelligence , Machine Learning , and Signal & Image Processing , with applications in radar imaging, computer vision, and smart sensors. Key areas include neural networks, object detection/tracking, and compressive sensing. Recent projects include assistive navigation tools for vision-impaired individuals and underwater mine detection via sonar imaging. Grants & Funding: He leads or co-leads over 30 funded projects, including: AI-based SAR Satellite Imaging System for Oceanic Waves (AGO, 2024–2025) A portable AI-guided navigation tool for vision-impaired people (KONEKSI, 2024–2026) Deep Learning for Vessel Surveillance using Satellite Imagery (NSW Space Research Network, 2022–2023) Teaching & Supervision: With 30+ years of experience, he has supervised 38 Ph.D. and 22 master’s students, mentored 12 early-career researchers, and delivered courses like Applied Data Analytics and Neural Networks . Current supervision includes projects on deep learning for obstacle detection and semantic segmentation. Awards: Eureka Prize (2011) for Defence Science ARC College of Experts Member (2009–2011) Multiple Vice-Chancellor Research Awards (1998–1999)

• Computer Graphics
• Human-Computer Interaction
• Robotics
• +۳ مورد دیگر

Dr. Daniel Berio is a researcher at Goldsmiths, University of London, specializing in computational models for human-like movement in digital art and robotics. His work bridges computer graphics, cognitive psychology, and robotic manipulation, focusing on stylized stroke generation, graffiti analysis, and kinematic modeling. He collaborates with Frederic Fol Leymarie and Rejean Plamondon, utilizing the Sigma Lognormal model to simulate human handwriting dynamics. Education : Doctoral thesis on AutoGraff (2021), exploring computational understanding of graffiti and calligraphy. Research Themes : Human-like motion in digital art, kinematic reconstruction from static traces, robotic graffiti generation, and perceptual fluency in aesthetic evaluation. Publications : 15+ works since 2015, spanning ACM Transactions on Graphics, British Journal of Psychology, and conferences like MOCO and IROS. Applications : Font stylization tools, synthetic graffiti generation, compliant robot control, and semantic typography systems.

Filiz Bunyak Ersoyمشاهده پروفایل

استادیار

• Image Processing
• Computer Vision
• Biomedical Image Analysis
• +۶ مورد دیگر

Filiz Bunyak Ersoy is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri's College of Engineering. She holds a B.S. and M.S. in Control and Computer Engineering from Istanbul Technical University (Turkey) and a Ph.D. in Computer Science from Missouri University of Science and Technology. Education: PhD, Computer Science, Missouri University of Science and Technology MS, Control and Computer Engineering, Istanbul Technical University BS, Control and Computer Engineering, Istanbul Technical University Research Interests: Her work focuses on image processing, computer vision, and artificial intelligence, particularly in biomedical image analysis and visual surveillance. She specializes in motion analysis, level set methods, and deep learning applications. Her research has led to over 100 publications and two U.S. patents. Funding & Collaborations: Her projects are supported by organizations such as the National Science Foundation (NSF), National Institutes of Health (NIH), Coulter Foundation, and the Army/Navy Research Laboratories. Notable collaborations include developing AI-driven techniques for carbon nanotube growth and biomedical image segmentation. Professional Contributions: She has organized workshops (e.g., BIBM Workshop on Machine Learning in Microscopy Imaging) and serves on editorial boards for MDPI Sensors Journal and ACM Computing Surveys .

Jun Baiمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۲ مورد دیگر

Jun Bai is an Assistant Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. His research focuses on Machine Learning, Deep Learning, Medical Image Analysis, AI-driven diagnostics for cancer and diseases, and drug discovery. He holds a Ph.D. in Computer Science and Engineering from the University of Connecticut (2023), an M.S. in Computer Science from the University of Dayton (2019), and an M.S. in Interdisciplinary Studies in Education (2015). His work emphasizes applying AI to healthcare challenges, such as robust mammogram classification, 3D biomedical image registration, and peptide generation for drug discovery. Recent studies include hybrid transformer models for medical imaging and weakly-supervised systems for prostate cancer diagnosis. His computational methods span molecular dynamics simulations and graph neural networks. Despite his prolific research output, no specific grants, advising roles, or lab affiliations are explicitly listed in the provided data. Contact: Rhodes Hall 891, Cincinnati, OH | Email: baiju@ucmail.uc.edu

Xin Fuمشاهده پروفایل

استاد

• Computer Architecture
• Machine Learning
• Energy-Efficient Computing
• +۲ مورد دیگر

Dr. Xin Fu is a Professor in Electrical and Computer Engineering at the University of Houston's Cullen College of Engineering, holding a PhD from the University of Florida. His research spans computer architecture, energy-efficient systems, machine learning acceleration, and hardware reliability, with applications in edge computing and quantum systems. Awarded the NSF CAREER Award and named Miller Scholar, he leads innovations in heterogeneous computing architectures. Research focuses on optimizing hardware-software co-design for AI workloads, with current projects in federated learning optimization, quantum computing reliability, and neural network acceleration. Recent publications demonstrate cross-cutting work in mobile AI deployment, adversarial defense mechanisms, and quantum error correction. Honors include: NSF Faculty Early CAREER Award (2014) Four-time recipient of competitive NSF research grants Miller Scholar recognition for teaching and research excellence

Uwe Meyer-Baeseمشاهده پروفایل

دانشیار

• Digital Signal Processing
• FPGA Design
• VLSI Systems
• +۴ مورد دیگر

Uwe Meyer-Baese is an Associate Professor in the Electrical and Computer Engineering Department at the FAMU-FSU College of Engineering. He holds a Ph.D. (Dr.-Ing. habil) from Darmstadt University of Technology, Germany. His research focuses on Digital Signal Processing with FPGAs, VLSI design, and medical imaging applications. He has authored over 100 publications, 5 books, and holds 3 patents. He has been recognized with awards such as the Humboldt Fellowship (2009) and the FAMU-FSU Teaching Award (2007). Education History: Dr.-Ing. habil (Venia Legendi), Darmstadt University of Technology, Germany, 2003 Ph.D. (Dr. Ing.), Darmstadt University of Technology, Germany, 1995 M.S., Darmstadt University of Technology, Germany, 1989 Research Interests: FPGA-based embedded systems and real-time DSP Low-power VLSI architectures Medical image processing (e.g., breast MRI, brain tumor analysis) Hardware security and intellectual property protection Graph theory applications in biological networks Recent work includes advancements in FPGA implementations for microprocessor systems, brain network controllability studies, and AI-driven medical diagnostics. His lab focuses on bridging hardware design with biomedical applications, emphasizing practical implementations through FPGA platforms. Awards: Max-Kade Award in Neuroengineering (1997) ECE Department Research Award (2005) Humboldt Fellowship (2009) FAMU-FSU Teaching Award (2007) He has advised over 60 master’s theses and contributed to major grants in FPGA-based medical systems. His book Digital Signal Processing with Field Programmable Gate Arrays is a widely used textbook in the field.

• Autonomous Driving
• Machine Learning
• Wireless Communication
• +۱۱ مورد دیگر

Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.

• Magnetic Resonance Imaging (MRI)
• Atherosclerosis
• Biomedical Informatics
• +۵ مورد دیگر

Chun Yuan, PhD is a Professor in the Radiology & Imaging Sciences department at the University of Utah School of Medicine, with an adjunct appointment in Biomedical Informatics. His research focuses on atherosclerosis imaging , magnetic resonance imaging (MRI) technology , and vascular health assessment using advanced imaging techniques. Education: PhD in Radiology & Imaging Sciences, University of Utah School of Medicine BS in Physics, Beijing Normal University Research Interests: Dr. Yuan's work explores atherosclerotic plaque characterization , carotid artery imaging , and intracranial vessel wall analysis . He develops novel MRI sequences for simultaneous noncontrast angiography , plaque hemorrhage detection , and quantitative vascular assessments . His studies often integrate biomechanical modeling and AI-driven image analysis to improve stroke risk stratification and atherosclerosis monitoring . Scientific Contributions: Dr. Yuan has pioneered MRI techniques like SNAP (Simultaneous Non-Contrast Angiography and Intraplaque Hemorrhage) and iCafe (Intracranial Artery Feature Extraction) . His research demonstrates correlations between plaque composition and cardiovascular outcomes , with applications in diabetes , hypertension , and lipid-lowering therapies . Scientific Awards: Fellow of the American Heart Association (FAHA) Fellow of the International Society for Magnetic Resonance in Medicine (FISMRM) Collaborations: He collaborates with institutions globally, including the Chinese Atherosclerosis Risk Evaluation (CARE-II) Study and European Society of Cardiology initiatives . His work bridges clinical cardiology , neuroradiology , and biomedical engineering .

Amy Sheppardمشاهده پروفایل

مدرس ارشد

• Digital Eye Strain
• Ocular Accommodation
• Presbyopia
• +۳ مورد دیگر

Dr. Amy Sheppard is a Senior Lecturer at Aston Optometry School within Aston University's College of Health and Life Sciences. She serves as Deputy Head (Education) and maintains UK optometry registration. Her academic appointments include roles as External Examiner for the University of Central Lancashire's MSci Optometry programme and iPRO research adviser for the College of Optometrists. She also chairs Vision Care for Homeless People. Education: 2018: Master's in Education with Distinction (Aston University) 2014: Senior Fellow of Higher Education Academy 2010: PhD in Optometry (Aston University) 2003: BSc (Hons) Optometry, First Class Honours (Aston University) Research Focus: Dr. Sheppard's work centers on anterior eye physiology with specialization in Digital Eye Strain, ocular accommodation mechanisms, and presbyopia management. Her investigations employ clinical optometry approaches to address contemporary vision challenges, particularly screen-related visual fatigue and age-related focusing disorders. Recent work explores mobile health applications for vision assessment and AI integration in ophthalmic care. Publication Trends: Her recent articles demonstrate strong focus on digital eye health epidemiology, innovative myopia control interventions, presbyopia diagnostics, and technological applications in optometry. Collaborative work frequently addresses clinical guidelines development and vision-related public health challenges across different demographics. Awards & Honors: Senior Fellow of Higher Education Academy (2014) Fellow of UK Higher Education Academy (2013) Master's in Education with Distinction (2018) Academic Leadership: She leads modules in Clinical Practice (undergraduate) and Ageing Eye (postgraduate), supervises research dissertations, and directs educational initiatives. She accepts PhD students and contributes to curriculum development across optometry programs. Affiliations: Member of Optometry & Vision Science Research Group (OVSRG) and Aston Research Centre for Health in Ageing. Maintains active collaborations with national optometry organizations and international research teams.

• Developmental Biology
• Genetics
• Molecular Biology
• +۵ مورد دیگر

Professor Kim Dale is a distinguished researcher and academic at the University of Dundee's School of Life Sciences, Department of Cell and Molecular Biology. With an extensive publication record spanning from 1997 to the present, her work focuses on the molecular mechanisms underlying embryonic development, particularly the segmentation clock and somitogenesis processes. Her research interests center on developmental biology with specific expertise in Notch signaling, segmentation clock dynamics, somitogenesis, and cell fate determination during embryonic development. Professor Dale's work integrates molecular biology, genetics, and stem cell approaches to understand how oscillatory gene expression patterns guide the formation of body segments in developing embryos. Her research has significant implications for understanding congenital disorders and developmental abnormalities. Analysis of Professor Dale's recent publications reveals a consistent focus on the molecular mechanisms of the segmentation clock, with increasing use of hiPSC-derived models and advanced imaging techniques. Her work bridges basic developmental biology with potential clinical applications, particularly in understanding how disruptions in segmentation processes lead to developmental disorders. The research shows a trajectory from fundamental mechanism discovery toward translational applications using stem cell models. Professor Dale has secured significant funding from the Medical Research Council for multiple projects spanning from 2004 to 2027, demonstrating sustained research excellence and impact. Her work on the segmentation clock, Notch signaling, and cell fate decisions has established her as a leader in developmental biology. As an active educator and mentor, Professor Dale has supervised numerous students including Scott Haston through vacation scholarships. She is also engaged in public outreach activities, having participated in events like the Dundee Science Centre International Women's Day Campaign, Women in STEM webinars, and Discovery Days, demonstrating her commitment to science communication and inspiring the next generation of researchers.

Neil D. B. Bruceمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۶ مورد دیگر

Neil D. B. Bruce is an Associate Professor in the School of Computer Science at the University of Guelph, Canada. His research focuses on computer vision, deep learning, and computational neuroscience, with a strong emphasis on visual saliency, neural networks, and semantic segmentation. He holds a BSc in Computer Science & Pure Math from the University of Guelph, an MASc in Systems Design Engineering from the University of Waterloo, and a PhD in Computer Science from York University. Prior to Guelph, he held academic positions at Ryerson University and the University of Manitoba. Dr. Bruce leads the Vision Lab, exploring topics like attention mechanisms, image processing, and AI-driven solutions for visual computing challenges. His work bridges theoretical models with practical applications, including real-world gaze behavior analysis and exposure blending techniques. Key contributions include saliency prediction frameworks (e.g., AIM model) and semantic segmentation networks (EML-Net, Iterative Gating Networks). His research also intersects with interdisciplinary fields such as neuroscience and healthcare informatics, as seen in recent studies on avian influenza outbreak detection using social media data. Teaching highlights include courses in neural networks, data science, and machine learning. He actively supervises graduate and undergraduate research projects, emphasizing computational methods and AI innovation.

• Visual Perception
• Cognitive Neuroscience
• Neuroscience
• +۳ مورد دیگر

Zijiang J He, PhD, is a Professor and Distinguished University Scholar in the Department of Psychological and Brain Sciences at the University of Louisville. His research focuses on visual perception, particularly the mechanisms underlying 3D depth perception and middle-level vision. He holds a B.S. in Biophysics from the University of Science and Technology of China (1983), M.S. in Neurobiology from Shanghai Institute of Physiology (1986), and Ph.D. in Physiological Optics & Neuroscience from the University of Alabama at Birmingham (1990). Postdoctoral training was completed at Harvard University (1994). Research interests include space vision mechanisms and middle-level vision processes. His lab explores how the brain integrates 2D retinal images into 3D spatial perception, leveraging virtual reality and psychophysical methods. Key contributions include studies on sensory eye dominance, binocular rivalry, and perceptual learning protocols for amblyopia. He leads the Visual Perception and Cognition Lab, emphasizing interdisciplinary approaches combining neuroscience, computational modeling, and behavioral experiments. Notable achievements include the Distinguished University Scholar title and impactful publications in *Nature*, *Science Advances*, and *Proceedings of the National Academy of Sciences*. His work bridges ecological regularities and neural processing, advancing understanding of visual system adaptations to spatial environments.

Qian Liuمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Applied Statistics
• +۵ مورد دیگر

Dr. Qian Liu is an Assistant Professor in the Department of Applied Computer Science at the University of Winnipeg . She holds a PhD in Individual Interdisciplinary Studies, integrating Computer Science, Statistics, and Medical Genetics. Her research focuses on advanced data science techniques for healthcare and material science, including multi-modal data integration and interpretable AI. Education : 2023: Postdoc, Western University, Canada 2023: PhD, University of Manitoba, Canada 2019: MSc, University of Manitoba, Canada Research Interests : Machine learning, deep learning, bioinformatics, computational biology, medical imaging, and their applications in healthcare and material informatics. Her lab develops tools for genomic integration, radiogenomics, and AI-driven biomarker discovery. Recent Contributions : Her work spans AI in nanomaterial classification, breast cancer biomarker discovery, and emotion recognition. Articles highlight innovations in graph neural networks, diffusion models, and interpretable deep learning. Awards : 2024 University of Manitoba Distinguished Dissertation Award 2019 Best Oral Paper at IEEE Bioinformatics Conference Grants : NSERC 2024 Discovery Grant (Principal Investigator) CIHR 2023 Project Grant (Co-Applicant) Labs/Teams : Active in interdisciplinary collaborations across computer science, statistics, and biomedical domains. Lab members include postdocs, PhD/MS students, and undergraduate researchers.

John Guttagمشاهده پروفایل

استاد

• AI for Healthcare and Life Sciences
• Biological and Medical Devices and Systems
• Graphics and Vision
• +۳ مورد دیگر

John Guttag is the Dugald C. Jackson Professor in Electrical Engineering and Computer Science at MIT. His work focuses on AI-driven healthcare solutions, biomedical systems, and advanced computer vision applications. He leads research in medical image analysis, machine learning reliability, and healthcare equity. Guttag's contributions include innovative frameworks like MultiMorph and Scale-Space Hypernetworks, addressing challenges in medical imaging and clinical decision-making. Affiliations: MIT Electrical Engineering & Computer Science Department (EECS) Research emphasizes AI for healthcare, particularly in segmentation, predictive analytics, and ethical algorithm design. Notable projects include real-time fraud detection systems and studies on racial disparities in clinical risk scores. His work bridges computer science with clinical practice through tools like Voxelmorph for medical image registration and ScribblePrompt for interactive biomedical segmentation. Recent publications highlight advancements in uncertainty-aware AI, contrastive learning, and scalable medical data processing. Guttag’s methodologies prioritize practical clinical applications, aiming to improve diagnostics and healthcare workflows. His lab develops open-source tools and frameworks that enhance accessibility to advanced medical imaging technologies.

TAN Tiow Sengمشاهده پروفایل

دانشیار

• Computational Geometry
• GPU Computing
• Algorithm Design
• +۲ مورد دیگر

TAN Tiow Seng is an Associate Professor at the School of Computing, National University of Singapore . He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (1993), an M.Sc. in Computer Science from NUS (1988), and a B.Sc. in Mathematics & Computer Science from NUS (1984). Research Interests : Design of geometric algorithms for GPU acceleration Applications in interactive graphics, visualization, and game development Development of robust GPU software for geometric computation Selected Publications demonstrate expertise in Delaunay triangulation, Voronoi diagrams, convex hulls, and GPU-based distance transforms. His team pioneered GPU-accelerated mesh refinement techniques and parallel recurrence optimization. Scientific Awards : NUS/SOC Teaching Excellence Award (1999, 2004, 2022) C. W. Gear Outstanding Graduate Student Award (UIUC, 1992) National University Overseas Graduate Scholarship (1988–1992) Data Processing Managers’ Association Award (1984) Industry Contributions : Holds five US/Singapore patents Chairman/co-founder of G Element Pte Ltd , a graphics/visualization company Served as expert panel member for Media Development Authority (MDA) funding evaluations