جست‌وجوی استاد و پژوهشگر

Olga Vekslerمشاهده پروفایل

استاد

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

Olga Veksler is a Professor at the University of Waterloo's Department of Computer Science, part of the Faculty of Mathematics. She holds a Ph.D. and M.Sc. from Cornell University (1999) and a B.A. from New York University (1995). Her research focuses on computer vision, machine learning, and discrete optimization, with notable contributions to image segmentation, graph algorithms, and deep learning integration. Her work emphasizes semantic segmentation, salient object detection, and efficient optimization techniques for graphical models. Education: Ph.D. in Computer Science, Cornell University, 1999 M.Sc. in Computer Science, Cornell University, 1999 B.A. in Computer Science, New York University, 1995 Her research explores intersections between machine learning and traditional computer vision challenges, particularly leveraging graph-based optimization and CRF models. Recent trends in her work include weakly supervised learning, sparse non-local CRF applications, and test-time adaptation strategies for salient object detection. She has pioneered methods for shape priors in multi-object segmentation and efficient graph-cut algorithms. Her advising and grant activities are foundational to her research, though specific grant details are not listed here. She maintains a lab focused on advancing computer vision through algorithmic innovation, with contributions to both theoretical frameworks and practical applications in medical imaging and scene understanding.

Huaizu Jiangمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Natural Language Processing
• +۵ مورد دیگر

Huaizu Jiang is an Assistant Professor at Khoury College of Computer Sciences, Northeastern University. His research bridges computer vision, graphics, and natural language processing to develop AI systems that understand and reconstruct 3D visual environments. Prior to joining Northeastern, he was a Postdoc Researcher at Caltech and Visiting Researcher at NVIDIA. He holds a Ph.D. from UMass Amherst (advised by Prof. Erik Learned-Miller), and M.E./B.E. degrees from Xi'an Jiaotong University. His research focuses on fundamental challenges in 3D scene understanding, including geometry reconstruction, semantic interpretation, novel view synthesis, motion generation, and optical flow estimation. Core interests span video processing, human-object interactions, multimodal reasoning, and efficient edge-device implementations. Recent publications emphasize diffusion models for motion/scene generation, transformer-based 3D perception, and video interpolation. Key trends include multi-view consistency techniques, text-to-3D synthesis, and efficient real-time algorithms for robotics applications. Awards & Honors: Winner of the VQA Challenge 2020 He advises 15+ graduate students on projects spanning 3D reconstruction, motion synthesis, and vision-language models. His group collaborates with institutions like NVIDIA and Caltech, focusing on generative AI for dynamic scene understanding.

Weihao Liمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Weihao Li is a Research Fellow at The Australian National University's School of Computing, specializing in computer vision and machine learning. His research focuses on object detection, image segmentation, open-set recognition, and point cloud segmentation. He holds a Dr. rer. nat. (PhD equivalent) and is registered to supervise research students. His research interests revolve around advancing techniques for dynamic instance segmentation, open-set learning, and 3D point cloud analysis. Notable projects include the ANU bushfire smoke dataset and contributions to generalized semantic segmentation and anomaly recognition. His work emphasizes data augmentation strategies and weakly-supervised learning methods. Key technical areas include synthetic dynamic instance copy-paste for video segmentation, curved geometric networks for anomaly detection, and cross-modal fusion in building facade analysis. He collaborates on computing-for-social-good initiatives, such as environmental monitoring via hyperspectral imaging. Dr. Li's publications span 2016–2024, with a focus on advancing computer vision through innovative architectures and methodologies. His recent work explores open-set recognition, few-shot learning with reinforced attention, and geometric prior-based segmentation techniques.

Rynson W.H. Lauمشاهده پروفایل

استاد

• Computer Graphics
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Rynson W.H. Lau is a Professor of Computer Science at City University of Hong Kong (CityU), leading research in Computer Graphics, Computer Vision, and Deep Learning. He holds an Honorary Professorship at Swansea University. Previously, he served on faculties at Durham University and The Hong Kong Polytechnic University. His work focuses on advancing graphics and vision techniques, including deep learning applications for graphics/vision problems, with publications in top venues like SIGGRAPH, CVPR, and NeurIPS. He has received the Adobe Research Gift (2023) and the Springer Nature Editorial Contribution Award (2025) for his editorial contributions to the International Journal of Computer Vision . Education: B.Sc. (First-class Honors) in Computer Systems Engineering from University of Kent Ph.D. in Computer Science from University of Cambridge Research Interests: Computer Graphics: Focused on 3D reconstruction, rendering, and real-time performance capture. Computer Vision: Specializing in saliency detection, object recognition, and low-light scene enhancement. Deep Learning: Developing generative models and diffusion-based frameworks for graphics and vision tasks. Editorial Roles: Editorial Board Member, International Journal of Computer Vision and IET Computer Vision . Guest Editor for special issues in journals like ACM Transactions on Internet Technology and IEEE Transactions on Multimedia. Teaching: 2024/25 Academic Year: CS4185: Multimedia Technologies and Applications CS4188/CS5188: Virtual Reality Technologies and Applications Research Team: Advises over 20+ students and collaborates internationally. Recent projects include AI-driven VR systems for healthcare and advanced 3D content generation using diffusion models.

Chenliang Xuمشاهده پروفایل

دانشیار

• Computer Vision
• Audio-Visual Learning
• Vision and Language
• +۵ مورد دیگر

Chenliang Xu is an Associate Professor in the Department of Computer Science at the University of Rochester, affiliated with the Goergen Institute for Data Science and Artificial Intelligence (GIDS-AI). His research focuses on computer vision, audio-visual learning, and trustworthy AI. He holds a PhD from the University of Michigan (2016), with prior degrees from Nanjing University of Aeronautics and Astronautics and the University at Buffalo. Notable awards include the Best Paper Award at ACCV 2024 and the James P. Wilmot Distinguished Professorship. His work spans interdisciplinary topics such as video understanding, multimodal reasoning, and robust AI. Key research contributions include audio-visual scene synthesis, bias mitigation in models, and applications in public health. He has secured over $3M in grants, including NIH funding for AI-driven video description tools and public health initiatives. Prof. Xu advises a dynamic research group with 11 PhD students and numerous collaborators. His lab explores cutting-edge projects like egocentric audio-visual understanding, generative AI for avatars, and multimodal defense mechanisms. He teaches courses in machine vision, deep learning, and advanced computer vision.

Danna Gurariمشاهده پروفایل

استادیار

• Computer Vision
• Human-Computer Interaction
• Accessibility
• +۴ مورد دیگر

Dr. Danna Gurari is an Assistant Professor in the Computer Science Department at the University of Colorado Boulder and Founding Director of the Image and Video Computing Group. She specializes in computer vision, human-computer interaction, accessibility, and biomedical data analysis, emphasizing interdisciplinary approaches. Since 2017, she has taught over 400 students in graduate and undergraduate courses while leading a multidisciplinary research team. Her research spans 60+ peer-reviewed articles and has been supported by NSF, CZI, Microsoft, Adobe, and Amazon. She mentors over 30 mentees annually, from undergraduates to postdocs. Her work bridges academia and industry, informed by prior roles at Boulder Imaging and Raytheon. Education: Ph.D. in Computer Science from Boston University, postdoctoral fellowship at University of Texas at Austin, and industry experience at Raytheon. Research focuses on visual interpretation for accessibility, biomedical image analysis, privacy preservation, and hybrid human-machine systems. Key lab projects include AI solutions for blind individuals, cancer radiation therapy optimization, and visual privacy tools. Awards: Recognitions at top venues including WACV, CHI, CSCW, and MICCAI. Active in funding and grants, including NSF Explore Access and Google Cloud Education grants. Labs/Teams: Leads the Image and Video Computing Group, collaborating with medical campuses and industry partners. Projects include VizWiz Grand Challenge and disability-first dataset development.

• Biomedical Engineering
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Aggelos K. Katsaggelos is a Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research focuses on biomedical imaging, machine learning, and computer vision applications in healthcare. He has collaborated extensively with interdisciplinary teams, including clinicians and engineers, to develop advanced algorithms for medical diagnosis and image analysis. Key research interests include medical image processing, deep learning for diagnostics, and computational methods in cardiology. His work spans applications such as MRI and ultrasound analysis, automated pathology detection, and multimodal sensing for health monitoring. Recent articles highlight contributions to myocardial scar quantification, lung ultrasound scoring, and AI-driven cough detection. His methodologies often combine domain-specific physics with modern machine learning techniques to solve real-world clinical challenges. Notable collaborations include projects with institutions like the University of Chicago and international teams in astrophysics and cognitive science. His work emphasizes translating algorithmic advancements into practical clinical tools.

Chunming Qiaoمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Optical Networks
• Wireless Networks
• +۶ مورد دیگر

Dr. Chunming Qiao is a SUNY Distinguished Professor and Chair of the Department of Computer Science and Engineering at the University at Buffalo (SUNY) , leading the Lab for Advanced Network Design, Evaluation and Research (LANDR) since 1993. His work spans cyber-physical systems , optical networks , and Internet of Things (IoT) , with a focus on safety, reliability, and protocol design. Education: PhD in Computer Science from the University of Pittsburgh (1993) BS in Computer Science and Engineering from the University of Science and Technology of China (1985) Dr. Qiao’s research interests combine theoretical and applied network design, including autonomous vehicles , quantum computing , and cloud services . He pioneered optical burst switching (OBS) and iCAR systems for wireless convergence, cited in BusinessWeek and Wireless Europe . His recent publications emphasize quantum networking , federated learning , and autonomous driving security , with projects on entanglement routing , edge inference optimization , and LiDAR adversarial attacks . Articles span IEEE and ACM venues , and include best paper awards . Scientific Awards: TC-CSR Distinguished Technical Achievement Award (2015) SUNY Chancellor's Award for Excellence (2013) IEEE Fellow (2009) UB Exceptional Scholar-Sustained Achievement Award (2005) Dr. Qiao has secured over two dozen NSF grants and collaborations with Google , Cisco , and NEC Labs . His 7 US patents and consulting experience highlight his industry impact, while his editorial roles and conference leadership underscore academic influence. He actively contributes to multi-disciplinary research through the New York State Center of Excellence in Bioinformatics and Life Sciences and CEDAR , advancing high-performance computing and document analysis .

Hong Qinمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Physics-Based Modeling
• +۵ مورد دیگر

Hong Qin is a Full Professor (with tenure) in the Department of Computer Science at Stony Brook University, where he is also a member of the Center for Visual Computing. He holds a B.S. and M.S. from Peking University, and a Ph.D. from the University of Toronto. His research focuses on computer graphics, geometric modeling, physics-based simulation, and scientific visualization. Education: B.S. (1986) and M.S. (1989) in Computer Science from Peking University; Ph.D. (1995) in Computer Science from the University of Toronto. Research interests include geometric modeling, computer-aided design, scientific visualization, medical imaging, and robotics. He has received notable awards like the NSF CAREER Award (1997), Alfred P. Sloan Fellowship (2001-2005), and multiple NSF grants. Qin has organized major conferences such as Computer Graphics International 2005 and chairs editorial boards for IEEE TVCG and The Visual Computer.

Jing Zhangمشاهده پروفایل

مدرس

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

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

Riccardo Renzulliمشاهده پروفایل

پژوهشگر

• Object-Centric Representation Learning
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Riccardo Renzulli is a Researcher at the Department of Computer Science, University of Turin, focusing on object-centric representation learning, medical image analysis, and AI-based computer vision applications. His research emphasizes capsule networks, deep learning models for hierarchical relationships, and applications in healthcare and aerial/satellite imagery. Education: MSc and BSc in Computer Science from University of Turin (2018 and 2015). Previous research with Prof. Valentina Gliozzi explored description logics and non-monotonic reasoning. Professional experience includes a 2022 post at Aalto University (supervised by Prof. Ville Kyrki and Francesco Verdoja) and roles at Addfor and Machine Learning Reply as a deep learning scientist. Research interests span concept learning, few-shot learning, interpretability, and medical imaging. Notable work includes visual localization systems for UAVs, AI-assisted diagnosis for COVID-19 via CXR analysis, and lung nodule segmentation using DeepHealth Toolkit. He contributed to the UniToChest dataset for cancerous nodule detection. His recent publications (2022-2025) address efficient neural architectures, medical imaging applications, and 3D scene modeling. Collaborations include EIDOSLAB, with research emphasizing scalable compression, entropy-based pruning, and ensemble methods for neural networks.

Mariusz Wzorekمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +۴ مورد دیگر

Mariusz Wzorek is an Assistant Professor and Head of Unit at the Department of Computer and Information Science (IDA) at Linköping University, Sweden. He is affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division, which focuses on advancing research and education in artificial intelligence, robotics, and collaborative systems. Dr. Wzorek holds a PhD from Linköping University (2023) and has expertise in autonomous systems, unmanned aerial vehicles (UAVs), and robotics. His research emphasizes safe navigation, collaborative robotics, and applications in emergency response and public safety scenarios. Key areas include UAV-based collision avoidance, geolocation using aerial imagery, and distributed systems for multi-agent coordination. His recent work involves developing systems like the RGS⊕ (RDF Graph Synchronization) for collaborative robotics, secure remote ID protocols for UAVs, and algorithms for autonomous search and rescue missions. These projects address challenges in sensor fusion, real-time decision-making, and robust communication in dynamic environments. Publications highlight contributions to UAV navigation, wireless mesh networks in emergencies, and 3D reconstruction using heterogeneous UAV teams. His research bridges theoretical foundations in AI with practical applications in robotics and safety-critical systems. While no specific scientific awards are listed, his active role in the AIICS division and numerous peer-reviewed publications reflect his significant contributions to the field.

Hang Daiمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Segmentation
• 3D Object Detection
• +۲ مورد دیگر

Dr. Hang Dai is transitioning to a Full Professor position at Wuhan University after serving as Honorary Research Fellow at the University of Glasgow's School of Computing Science. His research spans computer vision, medical image analysis, and autonomous driving systems. With over 26 publications, he develops deep learning methods for 3D segmentation, object detection, and video enhancement. Research areas include: Advanced 3D semantic segmentation for LiDAR data Semi-supervised medical image analysis Multi-modal fusion for autonomous vehicles Current projects focus on certainty-guided contrastive learning for medical imaging and attention mechanisms for depth super-resolution.

• Computer vision
• Robotics
• Machine perception
• +۳ مورد دیگر

Christopher Hollitt is a Senior Lecturer and Programme Director of Electrical Engineering & Mechatronics at the School of Engineering and Computer Science, Victoria University of Wellington. His research focuses on developing autonomous mobile robots capable of interacting with dynamic environments through advanced visual perception systems. Dr. Hollitt holds a BE(Elec), BSc(Hon), and PhD from the University of Adelaide, Australia. His academic journey has led him to specialize in machine perception and robotic control systems. Dr. Hollitt's research interests center on machine perception, particularly machine vision, and the control of robotic and space systems. He aims to develop artificial robot 'minds' capable of perceiving and modeling the world in real time. His specific expertise includes active vision with time constraints, statistical underpinnings of active perception, vision-based estimation of robot pose, and multi-modal active perception systems. His work bridges the gap between theoretical computer vision and practical robotic applications, with emphasis on real-world implementation challenges. His publication record shows a clear progression from fundamental visual attention models to sophisticated context-aware object detection systems. Early work focused on color spaces and basic feature extraction, while recent research explores top-down contextual information integration and uncertainty modeling for improved target object detection. His approach increasingly incorporates evolutionary algorithms and probabilistic methods to create adaptive visual systems that can handle the complexities of real-world environments. Dr. Hollitt actively supervises graduate students, with current doctoral projects examining 'Low cost analysis of milk contaminants' and 'Droplet evaporation of complex fluids.' He previously supervised Jane, Celine's Master's research on spacecraft magnetic disturbance rejection. His grant history includes the 'Generic Shoulder Relief Appliance' project funded by the New Zealand Artificial Limb Service. Within the university, Dr. Hollitt contributes significantly to the academic community through his role as Programme Director and his teaching of advanced engineering courses, helping to shape the next generation of engineers and computer scientists.

Ali Ayubمشاهده پروفایل

استادیار

• Artificial Intelligence
• Interactive Machine Learning
• Continual Learning
• +۳ مورد دیگر

Dr. Ali Ayub is an Assistant Professor at the Concordia Institute for Information Systems Engineering (Concordia University). His research focuses on advancing human-robot interaction through continual learning and interactive machine learning, particularly in domains like healthcare, household assistance, and collaborative robotics. He investigates how robots can adapt to user preferences over time and learn from limited human input in dynamic environments. Research Interests His core interests span Artificial Intelligence , Interactive Machine Learning , and Continual Learning , with applications to Human-Robot Interaction , Robotics , and Computer Vision . He explores topics such as: Long-term personalization of robots through user feedback Adaptive learning architectures for home service and assistive robots Cognitive models for incremental learning in robotics Human teaching patterns and trust dynamics in HRI Key Research Trends His recent work emphasizes few-shot learning for robots to acquire new tasks with minimal demonstrations and continual learning frameworks that avoid catastrophic forgetting. He also studies how robots can interactively gather data through curiosity-driven exploration and adapt to human preferences in collaborative tasks. His projects often bridge theory (e.g., Markov decision processes for deceptive robotics) with practical applications like grocery reminder robots and episodic memory systems for companion robots. Grants & Advising No specific grants or advising information is provided in the text, but his prolific publication record suggests sustained research activity in these areas.