جستجوی استاد راهنمای دانشگاهی

Aaqib Saeedمشاهده پروفایل

استادیار

• Human-Centric AI
• Federated Learning
• Self-Supervised Learning
• +۳ مورد دیگر

Aaqib Saeed is an Assistant Professor in the Department of Industrial Design at Eindhoven University of Technology. His research focuses on Human-Centric AI, Federated Learning, Self-Supervised Learning, and Audio Understanding, with applications in Personal Health. He holds a PhD (cum laude) from TU/e and an MSc (cum laude) from the University of Twente. Education: PhD in Computer Science (cum laude), TU/e (2021) MSc in Computer Science (cum laude), University of Twente (2018) Research Interests: Development of robust federated learning frameworks for decentralized data Self-supervised learning for audio and physiological signal analysis AI-driven solutions for healthcare monitoring Key Contributions: DeltaMask: Reducing communication overhead in federated fine-tuning FedNS: Mitigating noisy decentralized data in federated learning Labeling Chaos to Learning Harmony: Handling label noise in FL Professional Experience: Visiting Industrial Fellow, University of Cambridge (2023) Research Scientist, Philips Research (2019–2023) Research Internships: Google Research, TNO/EIT Digital Awards: UT Scholarship (MSc) Cum Laude awards for both PhD and MSc Labs/Teams: EAISI Health, EAISI Foundational, Computational Design Systems.

• Mechatronics
• Autonomous Systems
• Biomedical Engineering
• +۷ مورد دیگر

Dr. Milan Simic is a Senior Lecturer in the School of Engineering at RMIT University, serving as Program Manager for the Master of Engineering (Management) degree. He holds editorial roles for the Knowledge Engineering Systems and Intelligent Decision Technologies journals and is Associate Director of the Australia–India Research Centre for Automation Software Engineering. With a PhD in Electronic Engineering from the University of Niš and a Graduate Diploma in Education from RMIT, Dr. Simic has extensive industry and academic experience in Australia and internationally. His research focuses on mechatronics, autonomous systems, biomedical engineering, robotics, intelligent transportation systems, and green energy. Notable projects include AI-driven railway system strategies, gait analysis for biomedical applications, and smart traffic control systems. He actively supervises PhD and master’s students in areas like autonomous vehicles and energy recovery systems. Dr. Simic’s work bridges engineering innovation with societal impact, emphasizing sustainable transportation solutions and smart city technologies. His contributions span journal editing, international collaborations, and curriculum development in engineering management.

• Computer Vision
• Robotics
• Medical Imaging
• +۴ مورد دیگر

Yuhao Chen is a Research Assistant Professor at the University of Waterloo, specializing in cutting-edge research at the intersection of computer vision, robotics, and healthcare. His work focuses on 3D reconstruction, food tracking, medical imaging, and AI-driven solutions for nutrition analysis and sports analytics. He has contributed to benchmark datasets like NutritionVerse, MetaGraspNet, and FoodVerse, advancing applications in robotic grasping, dietary intake estimation, and human-object interaction analysis. Research interests include egocentric video analysis, real-time 3D reconstruction, zero-shot learning, and multi-task learning. His projects often integrate Gaussian splatting, photometric SLAM, and diffusion models to solve complex problems in food tracking, medical image segmentation, and sports player motion analysis. Recent work highlights include FoodTrack for dietary monitoring and RepViT-MedSAM for medical image segmentation. Yuhao Chen’s innovations span robotics, healthcare, and AI, with a focus on practical applications such as automated nutrition assessment, robotic bin picking, and athlete performance analysis. His research emphasizes scalable frameworks and physically informed 3D reconstruction methods to address real-world challenges in health, sports, and automation.

• Computer Graphics
• Computer Vision
• Augmented Reality
• +۳ مورد دیگر

Aditi Majumder is a Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the School of Engineering and Information Sciences. Her research focuses on multi-projector display systems, augmented reality (AR), and their applications in scientific and medical fields. She holds a Ph.D. from the University of North Carolina, Chapel Hill (2003). Her work addresses challenges in geometric, chromatic, and luminescent corrections for tiled displays, with applications in surgical assistance and deformable surface visualization. Recent projects include precision stencils for surgical sites and dynamic projection mapping on non-rigid surfaces. Notable achievements include the Inaugural Hasso Plattner Endowed Chair in Artificial Intelligence (2025). Her research spans AR in medicine, real-time multi-projector synchronization, and color gamut optimization. Dr. Majumder also engages in public discourse on computer science education, emphasizing its societal importance and foundational skills like programming and discrete mathematics. Her 15 most recent articles (2021–2024) highlight advancements in surgical AR, deformable surface projection systems, and medical visualization. These contributions bridge computer graphics, vision, and biomedical engineering, reflecting her interdisciplinary approach to solving complex display and interaction challenges.

Bernhard Thomaszewskiمشاهده پروفایل

مدرس

• Computer Graphics
• Robotics
• Computational Mechanics
• +۳ مورد دیگر

Bernhard Thomaszewski is a Lecturer at the Department of Computer Science at ETH Zürich. His research focuses on computational mechanics, robotics, and computer graphics, with an emphasis on simulation-based design and material modeling. He explores topics such as deformable contact, flexible materials, and robotic mechanisms. His work bridges theoretical foundations and practical applications, including medical imaging, garment simulation, and biomechanical systems. Notable research interests include the development of novel algorithms for real-time simulation, optimization-driven design of mechanical systems, and integration of machine learning with physical models. He has contributed to advancements in finite element modeling, differentiable simulation, and topology optimization for robotic and biomedical applications. His recent projects highlight interdisciplinary collaboration, addressing challenges in areas like orthodontic treatment prediction, automated pipeline design, and neural network-driven material characterization. While no specific grants or awards are explicitly listed, his prolific publication record underscores his impactful contributions to computational engineering and computer science.

• Smart Cities
• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +۱۰ مورد دیگر

Dr. Hung Cao is an Assistant Professor of Computer Science at the University of New Brunswick, where he directs the Analytics Everywhere Lab. His work focuses on interdisciplinary research in Cyber-Physical Systems (CPS), IoT, Edge/Fog/Cloud Computing, and Explainable AI, addressing societal challenges through data-driven solutions. Prior roles include PostDoc Fellow and Data Scientist at the People in Motion Lab, UNB, and Lecturer/Researcher at Vietnam National University. He holds a Ph.D. in Geomatics Engineering (specializing in Data Science) from UNB (2020), an M.Sc. in Computer Science from University College Dublin (2015), and a B.Eng. from Vietnam National University (2011). Research interests span Smart Cities, Embedded AI, TinyML, Federated Learning, and Real-time Systems. He has led projects with Cisco, NB Power, and other industry partners to develop scalable analytics frameworks for IoT applications. Dr. Cao actively contributes to technical communities (IEEE Smart City, Edge Computing, etc.), serving as a reviewer for journals and conferences, and a Topic Editor for Electronics Journal . His innovations include the Analytics Everywhere framework for spatio-temporal data analysis, MACeIP platform for smart cities, and energy-efficient IoT systems for environmental monitoring. Current work emphasizes human-centered AI for healthcare diagnostics and industrial inspection systems.

• Artificial Intelligence
• Image Analysis
• Computer Vision
• +۷ مورد دیگر

Dr. Wenjing Jia is an Associate Professor at the University of Technology Sydney (UTS), affiliated with the School of Electrical and Data Engineering within the Faculty of Engineering and IT. She holds a PhD in Computing Sciences (UTS, 2007), Master's in Communications and Information Systems (Fuzhou University, 2002), and a Bachelor's in Communications Engineering (Jilin University, 1999). Her research focuses on image analysis, computer vision, and AI applications in healthcare, transport, and defense. Key areas include text detection in challenging environments, medical image super-resolution, and crowd surveillance systems. She leads projects with industry partnerships, securing over $900K in funding. Dr. Jia is also a recognized educator with 12+ years of teaching experience, specializing in internetworking subjects. She organizes international conferences (e.g., ICDAR2019, TrustCom-2017) and serves as a Cisco Certified Instructor Trainer. Awards include the Science and Technology Award and a finalist spot in the Cisco Women in IT Academia Award. Education: PhD in Computing Sciences, UTS (2007) MSc in Communications and Information Systems, Fuzhou University (2002) BEng in Communications Engineering, Jilin University (1999) Research Highlights: Developed algorithms for low-light text detection and medical image enhancement Advanced crowd counting and violence detection in surveillance systems Contributions to OCT image super-resolution and LiDAR point cloud analysis Teaching & Leadership: Lead CI of Teaching & Learning grants Legal Main Contact for UTS Cisco Networking Academy Deputy Head - Teaching and Learning (secondee) Awards: Excellent Thesis Award, Science and Technology Award (2019), and recognition in Women in IT Academia. Her work bridges academia and industry, with over 130 publications and active roles in conference organization and technology transfer.

Sophia Banoمشاهده پروفایل

استادیار

• Computer Vision
• Surgical Robotics
• Medical Imaging
• +۳ مورد دیگر

Sophia Bano is an Assistant Professor in Robotics and Artificial Intelligence at the Department of Computer Science, University College London (UCL), since November 2022. She is affiliated with the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Surgical Robot Vision group, UCL Robotics Institute, and the Centre for Artificial Intelligence. Previously, she was a Senior Research Fellow at WEISS, contributing to the GIFT-Surg project. Her research focuses on AI-driven techniques for context awareness, navigation, and surgical robotics in minimally invasive procedures, including endoscopic workflow analysis, 3D reconstruction, and surgical vision systems. Education: BEng in Mechatronics Engineering (NUST, Pakistan), MSc in Electrical Engineering (NUST), MSc in Computer Vision and Robotics (Erasmus Mundus VIBOT), PhD from Queen Mary University of London and Technical University of Catalonia (Erasmus Mundus Fellowship). Post-doctoral work at University of Dundee (EPSRC ACE-LP project) and Imperial College London (ERC STING project). Research interests include computer vision for surgery, medical imaging, surgical robotics, and AI in healthcare. She leads the UKRI EPSRC-funded 'AI-enabled Decision Support in Pituitary Surgery' project and contributed to the GIFT-Surg and CARES projects. Publications span 3D reconstruction in fetoscopy, surgical workflow recognition, and AI models for medical imaging. Awards include the IJCARS-MICCAI Best Paper Award (2020) and the Best Innovation Award at the 2022 Surgical Robot Challenge. She organizes conferences like EndoVis and serves as a reviewer for journals like IEEE Transactions on Medical Imaging and MICCAI.

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +۶ مورد دیگر

Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.

Nianyi Liمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Computational Photography
• +۱ مورد دیگر

Nianyi Li is an Assistant Professor in the School of Computing at Clemson University. He holds a B.E. in Electronic and Information Engineering from Huazhong University of Science and Technology and a Ph.D. in Computer and Information Sciences from the University of Delaware. His research focuses on machine learning, computer vision, computational photography, and medical image processing. He has served as an Area Chair for NeurIPS 2024 and CVPR 2023/2024, demonstrating leadership in the academic community. Education: Ph.D., Computer and Information Sciences, University of Delaware (Advisor: Jingyi Yu) B.E., Electronic and Information Engineering, Huazhong University of Science and Technology Research Interests: Machine Learning Computer Vision Computational Photography Medical Image Processing His work includes contributions to atmospheric turbulence removal, microscopy video denoising, and deep learning applications in medical imaging. Key projects include the 'Turb-Seg-Res' pipeline for dynamic video restoration and the NimBLE non-rigid hand model. Recent articles focus on unsupervised methods for object segmentation, fluid surface reconstruction, and medical image analysis. Awards and grants are not explicitly listed but reflect his active research contributions.

W. Eric Wongمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Software Reliability
• +۶ مورد دیگر

W. Eric Wong is a Professor of Computer Science at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. in Computer Science from Purdue University (1993), following earlier degrees from Purdue and Eastern Michigan University. His research focuses on reducing software production costs while enhancing reliability, safety, and quality through program-based and architecture/design-based testing methodologies. Key areas include automated test generation, fault localization, debugging, and software safety analysis. Professional Background: Tenured Professor at UTD since 2002 Prior roles include Senior Scientist at Telcordia Technologies (1995–2002) and Consultant at Texas Instruments (2004–2005) Active in industry partnerships, such as projects with Motorola, Avaya Labs, and Raytheon Research Interests: Software Testing & Debugging Dependable Software Development Security Requirements Engineering Fault Localization Techniques Model-Based Testing Software Reliability Modeling Awards & Recognition: 2007 IEEE COMPSAC Best Paper Award 1997 NASA Quality Assurance Special Achievement Award Recipient of a $404,772 NSF grant for software safety and reliability research (2021) Professional Activities: Editorial roles for journals like Journal of Systems and Software and International Journal of Software Engineering Program Chair for ISSRE 2012, COMPSAC 2010, and multiple ACM SAC conferences Member of IEEE Reliability Society Administrative Committee (2008–2013) IBM System z Curriculum Advisory Panel member Labs & Teams: Leads the Software Engineering Group at UTD, collaborating on projects like eXVanatge (dependable software solutions) and Fault-Prone Module Identification in telecommunications systems. Engages in cross-disciplinary efforts with industry and international institutions.

Mubarak A. Shahمشاهده پروفایل

استاد

• Computer Vision
• Computer Science
• Image Processing
• +۳ مورد دیگر

Mubarak A. Shah is a Professor in the Department of Computer Science at the University of Central Florida (UCF) and holds the UCF Trustee Chair. He is affiliated with CREOL, The College of Optics and Photonics, and leads the UCF Center for Research in Computer Vision. His research focuses on advancing computer vision technologies, with applications in image processing, machine learning, and artificial intelligence. Shah’s academic roles include contributing to interdisciplinary research at the intersection of computer science and optics. His work emphasizes computational methods for visual data analysis and has likely influenced advancements in fields such as robotics, medical imaging, and autonomous systems. Though no specific grants, awards, or student advisees are listed here, his position as a Trustee Chair underscores his institutional leadership. The UCF Center for Research in Computer Vision serves as a hub for collaborative projects, fostering innovation in visual computing.

Girija Chettyمشاهده پروفایل

استاد

• Multimodal Systems
• Medical Image Computing
• Computer Vision
• +۷ مورد دیگر

Girija Chetty is a Full Professor in Computing and Information Technology at the University of Canberra's School of Information Technology and Systems. She holds a PhD in Information Sciences and Engineering and has over 35 years of experience in academia and research leadership roles, including Head of Software Engineering and Program Director of ITS courses. Her research focuses on multimodal systems, medical image computing, AI, and data science. She leads a dynamic research group comprising PhD students, postdocs, and international collaborators. Education: PhD in Information Sciences (Australia, 2007), MSc and BSc in Electrical Engineering/Computer Science (India). She has held visiting roles at Deakin University and CSIRO. Research interests span computer vision, pattern recognition, and medical diagnostics, with 200+ publications in top journals/conferences. Her work addresses global challenges via AI-driven solutions in healthcare (e.g., pain assessment systems, malaria diagnostics) and sustainability (SDG impact frameworks). Projects include AI for remote ultrasound imaging and smart farming systems. She actively collaborates with industry and global research institutions. Grants/Projects: 12 funded initiatives including AI for extreme environment healthcare, malaria pathogen detection, and big data-driven population health. Awards: Senior IEEE/Australian Computer Society membership, editorial roles in IEEE/Elsevier journals. Labs/Teams: Leads a multidisciplinary research group focused on medical AI and multimodal systems.

• Artificial Intelligence
• Computer Vision
• Digital Agriculture
• +۳ مورد دیگر

Ferdous Sohel is a Professor of Information Technology at Murdoch University and inaugural lead of the Agricultural Technologies program. His research spans AI, computer vision, and digital agriculture, with applications in medical imaging and environmental monitoring. He received the Mollie Holman Doctoral Medal and Vice Chancellor's Early Career Research Award. Research Impact: Developed innovative AI models for aquaculture oxygen prediction, 3D object tracking, quantum neural networks, and prohibited item detection. His work advances precision agriculture through hyperspectral classification frameworks and irrigation decision systems. Professional Service: Associate Editor for IEEE Transactions on Multimedia and senior IEEE member. Current projects include adversarial robustness for LiDAR systems and lightweight dormitory security networks.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Prof Noel O'Connor is a Full Professor at Dublin City University's School of Electronic Engineering, specializing in cutting-edge research at the intersection of artificial intelligence (AI), medical imaging, robotics, and smart city technologies. His work spans applications such as cardiac MRI reconstruction, robotic manipulation using reinforcement learning, and the development of the Smart DCU Digital Twin for autism-friendly university environments. Research interests include AI-driven medical diagnostics, multimodal data fusion, and adaptive systems. His contributions to cardiac MRI reconstruction and transformer-based medical imaging analysis reflect a strong focus on healthcare innovation. He also explores ethical AI practices to reduce social bias in foundation models. Recent work emphasizes smart infrastructure projects, such as optimizing parking recommendations for electric vehicles and enhancing accessibility through digital twin frameworks. His research often integrates real-time sensor data and multi-agent systems to address complex urban challenges. No scientific awards are listed. Collaborations include the ASU-DCU International Research Program on Sensors and Machine Learning. Advising details and grant information are not explicitly provided.