جست‌وجوی استاد و پژوهشگر

Anina Richمشاهده پروفایل

استاد

• Sensory Processing
• Selective Attention
• Multisensory Integration
• +۳ مورد دیگر

Anina Rich is a Professor at Macquarie University's School of Psychological Sciences and an ARC Future Fellow. She is affiliated with the Performance and Expertise Research Centre and the Perception in Action Research Centre. Her research focuses on sensory processing, particularly selective attention mechanisms and multisensory integration, including synaesthesia studies. She has received numerous awards, including being a finalist in the Australian Museum Eureka Prizes (2001, 2002) and the Australian Postgraduate Award (2001-2004). Her work combines psychophysics, neuroimaging, and cognitive neuroscience to explore attention dynamics and synaesthesia. Research Interests: - Selective attention: Balancing voluntary and involuntary attention, applied in medical imaging contexts, sustained attention, and neural underpinnings. - Multisensory integration: Investigating synaesthesia (e.g., mirror-touch, grapheme-color), exploring how sensory modalities interact. - Neuroimaging techniques: Using fMRI and MEG to study brain regions involved in attention and perception. Awards: 2001-2004 Australian Postgraduate Award 2004 Cognitive Neuroscience Society Graduate Student Award 2005 Australian Psychological Society Excellent PhD Thesis Award Multiple media engagements on synaesthesia and perception Grants/Projects: Dealing with Distraction: Understanding Recovery After Interruption (2024-2028) Improving Inferences from Brain Imaging to Understand Selective Attention (ongoing) Magnetic Resonance-Compatible Audio-Visual Stimulus Delivery System (2014-) Labs/Teams: Active member of the Perception in Action Research Centre and collaborator in the Centre for Elite Performance Expertise and Training (CEPET).

• Computer Vision
• Robotics
• Intelligent Automation
• +۵ مورد دیگر

Professor Alireza Bab-Hadiashar is a Professor of Mechatronics at RMIT University's School of Engineering, leading the Intelligent Automation Research Group. He holds a PhD in Robotics from Monash University and has held academic roles at Swinburne University and the University of Copenhagen. His research focuses on computer vision, robotics, and intelligent automation, with industrial applications in manufacturing and healthcare. He leads the Master of Robotics and Mechatronics Engineering program and has extensive experience in interdisciplinary projects involving AI, biomedical imaging, and advanced manufacturing. Research interests span computer vision, AI-driven automation, and robotics with emphasis on safety-critical systems like autonomous vehicles and medical imaging. Notable projects include defect detection in additive manufacturing and domain generalization in medical imaging. He collaborates with industry partners like Ford Australia on lightweight vehicle design and quality control. His recent publications emphasize distributed tracking systems, robotic calibration, and domain adaptation techniques. He actively supervises research projects in automated inspection, computer vision for transportation safety, and cancer detection via CT imaging. Academic memberships include IEEE, FIEAust, and professional organizations in computer vision and robotics. Labs/Teams: Director of the Intelligent Automation Research Group at RMIT. Current initiatives include developing AI solutions for Industry 4.0 and smart manufacturing systems.

Sara Beeryمشاهده پروفایل

استادیار

• Computer Vision
• Artificial Intelligence
• Environmental Science
• +۳ مورد دیگر

Sara Beery is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Electrical Engineering and Computer Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on leveraging computer vision for environmental sustainability and conservation challenges. Beery's work spans computational vision, self-supervised learning, and multimodal AI systems. She has contributed to advancements in neural latent dynamics, face recognition bias analysis, and spatio-temporal dataset creation for autonomy. Her recent publications emphasize computer vision applications in conservation, medical imaging, and robotics. Notable topics include synthetic image generation, denoising low-SNR video, and cell segmentation using foundational AI models. Scientific Awards Resnick Graduate Scholar

• Deep Learning
• Computer Vision
• Medical Image Understanding
• +۳ مورد دیگر

Adrian Barbu is a Professor in the Department of Statistics at Florida State University. His research focuses on deep learning, computer vision, and medical image understanding, with applications in feature selection, unsupervised learning, and scalable algorithms. He has contributed to advancements in neural networks, probabilistic models, and medical imaging technologies. His work spans theoretical developments in machine learning, including stochastic optimization, clustering methods, and feature selection techniques. Notable contributions include PCA-UNET architectures, compact support neural networks, and methodologies for automated image analysis in healthcare and materials science. Barbu's publications explore cutting-edge topics like semi-supervised few-shot learning, hierarchical classification systems, and the application of Monte Carlo methods in complex data analysis. His research often bridges theory and practice, addressing challenges in big data, real-time processing, and medical diagnostics. While no specific awards are listed, his extensive publication record highlights a prolific career in advancing machine learning and computational methods across interdisciplinary domains.

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

Olov Anderssonمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

David Clausiمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۲ مورد دیگر

David Clausi is a Professor in the Department of Systems Design Engineering at the University of Waterloo. His research focuses on computer vision, image processing, and pattern recognition, with applications in SAR imagery and biomedical imaging. He leads the Vision and Image Processing (VIP) Lab, which explores AI, transparent AI systems, and scalable solutions. He has supervised numerous graduate and undergraduate students, contributing to advancements in medical imaging, SAR sea ice classification, and sports analytics. Awards include Teaching Excellence recognitions and a Research Excellence Award. His work spans publications in remote sensing, medical imaging, and computer vision, with a focus on real-world applications like environmental monitoring and healthcare. Education: Ph.D., P.Eng. (details not explicitly provided, inferred from titles). Research Interests include automated image interpretation, SAR and biomedical imagery analysis, and AI ethics. His lab emphasizes scalable AI and transparent AI explanations. Recent articles address sea ice mapping, hockey analytics, and wildlife detection using advanced machine learning techniques. Awards highlight contributions to research and teaching excellence. Advising spans over 30 students, with notable theses in SAR segmentation, medical image analysis, and computer vision. The VIP Lab collaborates on projects like the AI4Arctic initiative and hockey analytics tools like PuckNet and GoalieNet.

• Machine Learning
• Computer Vision
• Multimodal Learning
• +۷ مورد دیگر

Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.

• Optimization
• Learning Systems
• Image & Video Processing
• +۳ مورد دیگر

Dr. Ufuk ÖZKAYA is a Lecturer in the Department of Electrical and Electronics Engineering at Süleyman Demirel University's Faculty of Engineering and Natural Sciences. His research focuses on optimization, learning systems, and image/video processing. He holds a PhD in Electronics and Communication Engineering from Yildiz Technical University (2011), following a Master's (2005) and Bachelor's (2003) from Süleyman Demirel University. His work spans interdisciplinary applications, including Ottoman character recognition, biomedical signal analysis, and generative adversarial networks (GANs) for image processing. Notable contributions include GAN-based text segmentation methods and secure digital image scrambling algorithms. With an h-index of 6 and 133 citations, his research bridges theoretical advancements and practical solutions in telecommunications and medical imaging. Education: BSc (2003), MSc (2005) from Süleyman Demirel University; PhD (2011) from Yildiz Technical University Research Interests: Optimization algorithms, neural networks, biomedical signal processing, document analysis Selected Projects: Development of automated calcium imaging analysis systems, Ottoman document digitization His recent work emphasizes applying deep learning to historical document analysis and medical diagnostics, reflecting a commitment to both technological innovation and cultural preservation.

• Multimodal Learning
• Brain Decoding
• Computer Vision
• +۳ مورد دیگر

Weihao Xia is a postdoctoral researcher at the University of Cambridge's Department of Computer Science and Technology, affiliated with the CORE Lab under Cengiz Öztireli. His work bridges computer science and human cognition, focusing on multimodal learning, brain decoding, and tactile representation. He specializes in integrating visual, textual, neural, and tactile data to enhance AI systems for perception and creativity. Research interests include neural decoding from brain signals, tactile representation learning, and generative models for cross-modal translation. Notable projects include UMBRAE (unified multimodal brain decoding) and DREAM (visual decoding via human visual system reversal). His work addresses challenges in generating realistic images, interpreting neural signals, and improving tactile-based material editing. Teaching: Supervises Part II, Part III, and M.Phil. projects at Cambridge. Research topics for students include brain-based art creation, tactile material editing, and benchmarking sensory modalities. Projects emphasize multimodal alignment, brain-imagery reconstruction, and tactile representation learning. Contact: wx258@cam.ac.uk | Room GC01, William Gates Building.

Roger Zimmermannمشاهده پروفایل

استاد

• Multimedia and Distributed Systems
• Multimedia and Mobile Networking
• Spatio-temporal Data Management
• +۸ مورد دیگر

Roger Zimmermann is a Full Professor at the School of Computing, National University of Singapore (NUS), where he is also a Co-PI at the Grab-NUS AI Lab and leads the Location AI project. He previously served as Deputy Director of the NUS Smart Systems Institute (SSI) and Co-Director of the Centre of Social Media Innovations for Communities (COSMIC), both funded by Singapore’s National Research Foundation (NRF). Before joining NUS, he was a Research Area Director and Research Assistant Professor at the University of Southern California (USC). Ph.D. in Computer Science, University of Southern California (1998) M.S. in Computer Science, University of Southern California (1994) His research focuses on multimedia systems , spatio-temporal data management , streaming media architectures (especially DASH), machine learning applications , AR/VR , and location-based services . He leads the Media Management Research Lab (MMRL) at NUS, which conducts cutting-edge work in distributed multimedia and intelligent systems. His work combines theoretical depth with real-world applications in urban computing, smart mobility, and immersive media. The recent publications reflect a strong trend toward multimodal learning , spatio-temporal AI , adaptive streaming , and urban intelligence . His team explores zero-shot learning, 3D scene understanding, traffic forecasting, and open-vocabulary audio-visual segmentation, often leveraging foundational models and deep neural architectures. There is a clear emphasis on real-time, scalable systems for smart cities and immersive experiences. Dr. Zimmermann has received numerous accolades, including: DASH-IF Excellence in DASH Award (multiple years) Best Paper Awards at ACM SIGSPATIAL, IEEE ICME, and ACM MMSys Silver Award at ACM MMSys 2020 Grand Challenge IEEE Communications Society Best Editor Award (2017) ACM Distinguished Member (2017) Top 1% Publons Reviewer in Computer Science (2018) He has advised numerous students and led major research initiatives funded by MOE, NRF, A*STAR, NSF, and industry partners like Seagate, Intel, and HP. He has served as General Chair for IEEE MIPR 2023, ACM Multimedia 2020, and IEEE ISM 2015, and as TPC Co-Chair for several top-tier conferences. His editorial roles include Associate Editor for IEEE Transactions on Multimedia (TMM), ACM TOMM, and IEEE OJ-COMS. He leads the Media Management Research Lab (MMRL) , which focuses on intelligent multimedia systems, spatiotemporal data mining, and immersive media technologies. The lab develops scalable solutions for real-world challenges in urban computing, smart transportation, and interactive media.

Leonidas Guibasمشاهده پروفایل

استاد

• Computational Geometry
• Computer Graphics
• Robotics
• +۶ مورد دیگر

Leonidas Guibas is the Paul Pigott Professor of Engineering and Professor (by courtesy) of Electrical Engineering at Stanford University's Department of Computer Science. He leads the Geometric Computation group and is affiliated with the Computer Graphics and Artificial Intelligence Laboratories. His research focuses on algorithms for sensing, modeling, and reasoning about the physical world, with expertise in computational geometry, robotics, sensor networks, and topological data analysis. Current work includes geometric modeling with point clouds, 3D reconstruction, and mobility data analysis. He holds prestigious awards including ACM Fellow (1999), Allen Newell Award (2008), and membership in the National Academy of Sciences (2022). Education: PhD, Stanford University (1976) MS & BS, California Institute of Technology (1971) Research Interests: Geometric and topological data analysis 3D reconstruction and 3D shape analysis Sensor networks and robotics Biological structure modeling Machine learning for geometric problems Recent Articles Focus: Recent work emphasizes neural radiance fields (NeRF), dynamic Gaussian splatting, and physically plausible 3D shape generation. Publications span advancements in symmetry detection, articulated object manipulation, and multi-view video generation. Awards & Honors: Fellow, ACM (1999) Allen Newell Award (2008) Fellow, IEEE (2011) Member, National Academy of Engineering (2017) Member, National Academy of Sciences (2022) Advising & Teams: Advises doctoral and master's students on topics like geometric computing and robotics. Leads interdisciplinary teams at Stanford's ICME, HAI, and Woods Institute. Current advisees include Ian Huang, Boxiao Pan, and Colton Stearns. Labs & Collaborations: Active in the Geometric Computation group and collaborates with the Stanford AI Lab. Works on projects funded by grants in robotics, computer vision, and computational biology.

Hyung Jin Changمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۲ مورد دیگر

Dr Hyung Jin Chang is an Associate Professor in the School of Computer Science at the University of Birmingham . He earned a PhD in machine learning and computer vision from Seoul National University in 2013, following a BSc in Electrical and Computer Engineering from the same institution in 2006. Research focuses on artificial intelligence , computer vision , robotics , and human-robot interaction , particularly human-centred visual learning for hand/body pose estimation, gaze tracking, and action/internal state understanding. Key projects include EU FP7 GRANT 612139 , EU H2020 GRANT 643783 , and industry collaborations with Samsung Electronics and Samsung GRO Grant . Recent publications address advancements in unsupervised learning , domain adaptation , 3D hand-object interaction modeling , and multimodal gaze following in conversational scenarios. His work bridges theoretical AI research with practical applications in vision-based robotics and medical image analysis, supported by both academic and industrial grants.

• Image Processing
• Video Compression
• Computer Vision
• +۲ مورد دیگر

Maggie Zhu (Fengqing Maggie Zhu) is an Assistant Professor at Purdue University's Department of Electrical and Computer Engineering, College of Engineering. She holds a Ph.D. in Electrical and Computer Engineering from Purdue (2011) and has focused on image processing, video compression, computer vision, and computational photography since joining the faculty in 2015. Ph.D. in Electrical and Computer Engineering (2011) Assistant Professor at Purdue (2015–present) Staff Researcher at Huawei Technologies (2012) Her research bridges machine learning with practical applications in image compression , 3D reconstruction , and nutrition analysis , particularly through wearable technologies and edge-cloud systems. Recent work includes class-incremental learning for 3D perception and low-rank adaptation for efficient vision models. Scientific awards include: Huawei Certification of Recognition (2012) NIH mHealth Summer Institute Participant (2011) Charles C. Chappelle Graduate Fellowship Motorola Foundation Fellowship She has contributed to food portion estimation using monocular imaging , neural video compression , and domain adaptation methods, with publications spanning learned compression techniques and healthcare applications. Recent grants focus on technology-enabled dietary assessment and collaborative computing frameworks.