جست‌وجوی استاد و پژوهشگر

Wei Shaoمشاهده پروفایل

استادیار

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Wei Shao is an Assistant Professor in the College of Medicine at the University of Florida, specializing in artificial intelligence applications in medical imaging. His work focuses on developing machine learning algorithms for medical image registration, segmentation, and diagnosis, with a particular emphasis on integrating these tools into clinical workflows. Dr. Shao holds a Ph.D. in Electrical and Computer Engineering from Stanford University (2022), preceded by M.S. degrees in Mathematics and Electrical and Computer Engineering from the University of Iowa (2019-2018). His postdoctoral training focused on deep learning and medical imaging. His research projects include: Machine learning algorithms for multimodal image registration and segmentation AI-driven disease diagnosis on medical images Integrating image processing into clinical practices Notable contributions include advancements in 3D medical image segmentation, text-guided models for radiology, and AI-enhanced micro-ultrasound for prostate cancer screening. His work bridges computational methods with clinical needs, aiming to improve diagnostic accuracy and patient care. Recent publications highlight innovations in diffusion models, vision-language integration for medical imaging, and robust artifact detection in 4DCT scans. These studies underscore his commitment to advancing AI-driven solutions in healthcare.

Stepan Lucyszynمشاهده پروفایل

استاد

• Millimetre-wave Systems
• Terahertz Technology
• RF MEMS
• +۲ مورد دیگر

Professor Stepan Lucyszyn FREng, FIEEE is Professor of Millimetre-wave Systems at Imperial College London. A pioneer in terahertz technologies, he co-founded Imperial's Centre for Terahertz Science and Engineering and has developed innovative 'THz Torch' wireless communication systems. Research focus areas: Millimeter-wave and terahertz systems engineering Additive manufacturing of RF components RF MEMS technology and applications Low-cost security and defense technologies Honors include Fellow of the Royal Academy of Engineering (2023), IEEE Fellow (2014), and recipient of Junkosha's Technology Innovator Award (2022) for 3D printing innovations. He has authored over 200 papers and 12 book chapters in applied physics and engineering.

Rafeef Garbiمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +۷ مورد دیگر

Dr. Rafeef Garbi is a Professor at the Department of Electrical and Computer Engineering, University of British Columbia, and the Founder/Director of the Biomedical Signal and Image Computing Laboratory (BiSICL). Her multidisciplinary research integrates artificial intelligence, computer vision, and medical imaging for clinical applications in pediatric orthopedics, oncology, and neurology. PhD (Chalmers University, Sweden), MSc (with distinction), Technical Licentiate Research Focus: Specializing in Medical Image Computing and Visual Computing , her lab develops AI-driven solutions for: Automated segmentation and analysis of multi-dimensional biomedical data Clinically-translatable biomarkers for disease assessment Computer-aided intervention systems in surgical contexts Scientific Leadership: UBC Killam Faculty Research Fellow Peter Wall Institute for Advanced Studies Early Career Scholar Senior IEEE Member & Founding IEEE EMBS Vancouver Section Member Key Collaborations: Active in the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society and CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. Her team bridges engineering, medicine, and computational biology through translational research.

Masatoshi Okutomiمشاهده پروفایل

استاد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

• Augmented Reality
• Computer Vision
• Human-Computer Interaction
• +۴ مورد دیگر

Dr. Alexander Plopski is an Assistant Professor at the Institute of Visual Computing, Technische Universität Graz. His research focuses on advancing augmented reality (AR) technologies, human-computer interaction (HCI), and optical display systems. He holds a PhD, M.Sc., and BSc in relevant fields. His work emphasizes perceptual optimization in AR displays, eye tracking integration, and accessibility solutions for color vision deficiencies. Key research areas include gaze-contingent AR interfaces, light field manipulation for extended reality, and multimodal interaction techniques. Notable contributions include the development of the 'guitARhero' interactive AR guitar tutorial system and studies on focal distance effects in optical see-through displays. His publications span topics from AR display calibration to gesture recognition using radar sensing. He has explored applications in industrial training, medical AR, and robotic telemanipulation. His work often bridges theoretical perceptual studies with practical system implementations, aiming to enhance user experience and accessibility in AR/VR environments.

Priyakant Sinhaمشاهده پروفایل

مدرس ارشد

• Remote Sensing
• Geospatial Science
• Precision Agriculture
• +۳ مورد دیگر

Dr. Priyakant Sinha is a Senior Lecturer in Spatial Science at the University of New England's School of Environmental and Rural Science, with over 20 years of research experience in remote sensing and geospatial science. He specializes in applying remote sensing technologies to agriculture, environmental monitoring, and natural resource management. His research focuses on: Advanced agricultural remote sensing and precision agriculture Time-series crop monitoring and yield prediction UAV/Drone-based 3D imaging for farm management Vegetation species mapping and change detection Hyperspectral and LiDAR data analysis Dr. Sinha teaches courses in GIS, spatial analysis, precision agriculture, and remote sensing applications. He has successfully supervised multiple PhD students in areas ranging from flood hazard mapping to drought monitoring using earth observation data. Technical expertise includes advanced digital image processing, GIS analysis and modeling, and specialized software including ENVI, ArcGIS, QGIS, and Pix4D. He develops innovative methods for temporal change analysis using machine learning and Google Earth Engine.

Christophe Charrierمشاهده پروفایل

استاد

• Digital Image Forensics
• Video Forensics
• Biometrics
• +۷ مورد دیگر

Christophe Charrier is a Full Professor in Forensics and AI at Université de Caen Normandie, affiliated with GREYC UMR CNRS 6072 and IUT Grand Ouest Normandie's Multimedia and Internet Department (Dept. MMI). He obtained his PhD in Computer Science from Université Jean Monnet (Saint-Etienne) in 1998, followed by an HDR (Habilitation à Diriger des Recherches) in 2011 from Université de Caen Normandie. His academic journey includes roles as a Postdoctoral Researcher at Université Laval (1998-2001), Associate Professor at IUT Saint-Lô (2001), and Visiting Scholar/Professor positions at University of Texas at Austin (2008) and University of Sherbrooke (2009-2011). His research focuses on Digital Image and Video Forensics (e.g., deepfake detection), Image/Video Quality Assessment , Computational Vision , and Biometrics (fingerprint quality, template update, presentation attack detection). He leads the SAFE research group since 2016 and collaborates with the E-payment & Biometrics team at GREYC. His work integrates machine learning for quality metrics, biometric system evaluation, and forensic analysis. Recent publications highlight advancements in deepfake detection , 3D mesh quality assessment , and biometric security . Articles span journals like Intelligent Service Robotics (2024), IEEE Access (2024), and conferences such as CORESA (2024) and Cyberworlds (2023-2024). His studies on fingerprint systems, behavioral biometrics, and environmental impacts on data quality underscore his interdisciplinary approach. Scientific Awards : Best PhD Paper Award (ASONAM 2022) Best Full Paper Award (CW2022) He has mentored 14 PhD students since 2003, including notable alumni like Xinwei Liu (Zhejiang Wanli University) and Antoine Cabana (ALTEN, Toulouse). His projects span biometric certification, latent space manipulation, and 3D mesh evaluation, often in collaboration with institutions in Canada, Norway, and Morocco.

Shimeng Yuمشاهده پروفایل

استاد

• Semiconductor Devices
• Non-Volatile Memories
• 3D Integration
• +۵ مورد دیگر

Shimeng Yu is a full professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering, holding the Dean’s Professorship. He earned his B.S. from Peking University (2009) and M.S./Ph.D. from Stanford University (2011/2013). His research focuses on semiconductor devices, non-volatile memories, 3D integration, and AI hardware accelerators. Yu leads SRC/DARPA JUMP 2.0 centers on memory/storage and 3D integration, with over 400 publications and 30,000+ citations (H-index 82). He serves on flagship conference committees (e.g., IEDM, VLSI) and editorial boards (IEEE EDL, JETCAS). Education: B.S., Microelectronics, Peking University (2009) M.S./Ph.D., Electrical Engineering, Stanford University (2011/2013) Research Themes: Emerging non-volatile memories for AI Monolithic 3D integration Energy-efficient computing systems His work spans device fabrication, circuit design, and system-level co-optimization. Recent projects are funded by NSF, DARPA, DOE, and industry partners (TSMC, Intel, Samsung), totaling >$17M. His lab, located at the Pettit Microelectronics Research Center, develops prototypes with cleanroom access. Awards: IEEE Fellow (2024) ACM/IEEE DAC Under-40 Innovators Award (2020) NSF CAREER Award (2016) Multiple editorship roles and distinguished lecturer appointments (IEEE EDS/CASS) Grants & Funding: Lead of two SRC/DARPA JUMP 2.0 centers Total research funding exceeds $17M

Tivon Riceمشاهده پروفایل

استادیار

• Digital Art
• Experimental Media
• Computer Vision
• +۵ مورد دیگر

Tivon Rice is an Assistant Professor in DXARTS (Digital Arts and Experimental Media) at the University of Washington. His work critically examines digital culture's impact on representation, communication, and environmental systems. Rice holds a PhD from UW (2016) and an MFA in Sculpture (2006), with a BFA in Sculpture & Electronic Media (2000). He is a Fulbright Scholar (Korea, 2012) and collaborated with Google's Artists + Machine Intelligence program. Education: PhD, DXARTS, University of Washington, 2016 MFA, Sculpture, University of Washington, 2006 BFA, Sculpture & Electronic Media, University of Colorado, 2000 Research focuses on digital culture's poetics, emerging technologies, and environmental narratives. Projects use AI, 3D scanning, and experimental film to explore how new technologies shape visual communication and social systems. Recent exhibitions include venues in New York, Seoul, Berlin, and São Paulo. Awards: Fulbright Scholar Award (2012) Google Artists + Machine Intelligence Collaboration Grants and advising details are pending explicit mentions in texts. Affiliated with UW eScience Institute, exploring computational methods in artistic research.

• Precision Agriculture
• Optical Engineering
• Remote Sensing
• +۳ مورد دیگر

Dr. Jose Manuel Sánchez Peña is a Full Professor at Universidad Carlos III de Madrid (UC3M), affiliated with the Grupo Universitario de Tecnologías de Identificación (GUTI). His research focuses on precision agriculture technologies, optoelectronics, and neuroscientific interfaces. He leads projects on drone-based crop monitoring, renewable energy systems, and machine learning applications in environmental science. Key research areas include: UAV remote sensing for water stress and weed management in viticulture and maize Optical communication systems leveraging photovoltaic integration Machine learning models for precision agriculture Neuroscientific studies on multisensory emotion elicitation Publishing trends show strong focus on: Drone technology advancements (42% of recent articles) Optoelectronics and VLC systems (28% of recent articles) Neuroscience applications (15% of recent articles) Sustainable agricultural practices (12% of recent articles) Laboratory activities center around GUTI's interdisciplinary teams working at the intersection of engineering, agriculture, and neurotechnology.

Minglun Gongمشاهده پروفایل

استاد

• Visual Computing
• Computer Graphics
• Computer Vision
• +۶ مورد دیگر

Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.

David E. Breenمشاهده پروفایل

استاد

• Biomedical Image Informatics
• Geometric Modeling
• Textile Modeling and Design
• +۱۲ مورد دیگر

David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.

Keith D. Paulsenمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging Methodologies
• Cancer Therapeutics
• +۴ مورد دیگر

Keith D. Paulsen is the MacLean Professor of Engineering at Dartmouth College’s Thayer School of Engineering and holds the title of Professor of Radiology & Surgery at the Geisel School of Medicine. He serves as Scientific Director of the Center for Surgical Innovation at Dartmouth-Hitchcock Medical Center and Co-Director of the Translational Engineering in Cancer Research Program at the Norris Cotton Cancer Center. His roles emphasize interdisciplinary collaboration between engineering, medicine, and oncology. Paulsen earned a BSc in Biomedical Engineering from Duke University (1981), followed by MS (1984) and PhD (1986) degrees in Engineering Sciences from Dartmouth College. His research focuses on biomedical imaging, cancer therapeutics, and image-guided surgery, with particular expertise in optical and electromagnetic methodologies. He has pioneered technologies such as fluorescence-guided surgery, quantitative scatter imaging, and non-linear image reconstruction techniques, aiming to enhance surgical precision and cancer diagnosis. His awards include fellowships from OSA, SPIE, AIMBE, IEEE, and the National Academy of Inventors. Paulsen’s work has led to startups like CairnSurgical (where he serves as CTO) and InSight Surgical Technologies, translating research into clinical tools. Key projects include intraoperative imaging systems for brain and spine surgery, microwave imaging for breast cancer, and optical molecular imaging for real-time surgical guidance. Paulsen teaches advanced computational methods (ENGS 205, 105) and courses on medical device innovation (ENGM 189.1/2). His lab, part of Dartmouth’s Optics in Medicine cluster, collaborates with radiology, surgery, and oncology departments to develop clinical technologies funded by NIH, NCI, and DoD grants.

• Human-Computer Interaction (HCI)
• Computer Supported Cooperative Work (CSCW)
• Ubiquitous Computing
• +۳ مورد دیگر

Tony Tang is an Associate Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he leads the RICELab (Rethinking Interaction, Collaboration, and Engagement). Previously, he held roles at the University of Toronto and University of Calgary. His research focuses on Human-Computer Interaction (HCI), including Human-AI interaction, mixed reality interfaces, and ubiquitous computing. Education : PhD in Electrical and Computer Engineering, University of British Columbia (2010) MSc in Computer Science, University of Calgary (2005) BSc in Computer Science and Psychology, Simon Fraser University (2002) Research Interests : Tang’s work bridges theoretical and applied HCI, emphasizing systems that enhance human collaboration and interaction with technology. His lab explores topics like AI feedback mechanisms, mixed reality environments, and interfaces for distributed teams. Awards & Grants : ACM CHI Academy Member (2014) Funded by NAVER ($1.25M over 5 years), Meta ($30K), and SMU-SUTD Tier 1 Grant (S$100K) Advising & Grants : Supervised 26 graduate students and multiple postdoctoral fellows. His grants include work on immersive analytics and teleconferencing tools for physiotherapy. Labs & Teams : Directs the RICELab, which designs novel interaction systems for collaboration and learning. Active in the HCI community, serving as General Co-chair for CSCW 2022.

Kurt Keutzerمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.