جست‌وجوی استاد و پژوهشگر

Laurie Wilcoxمشاهده پروفایل

استاد

• Vision Science
• Neuroscience
• Binocular Vision
• +۴ مورد دیگر

Laurie Wilcox is a Full Professor in the Department of Biology at York University, affiliated with the Faculty of Science. Her research focuses on stereopsis, binocular vision, and depth perception, particularly exploring how the visual system processes binocular disparity signals. She leads a laboratory investigating cortical systems for fine and coarse disparities, with studies on amblyopia and applied collaborations with companies like Christie Digital and IMAX. Her work bridges basic neuroscience and applied research, addressing depth perception in 2D/3D displays and VR environments. Key interests include stereoscopic volume representation, perceptual grouping, and the impact of monovision on depth judgments. Recent studies examine lightness constancy in virtual reality, depth magnitude errors in 3D displays, and neural activation patterns in object-selective visual cortex. Wilcox has published extensively on binocular vision mechanisms, including coarse stereopsis in strabismus patients and the role of motion parallax in depth perception. Her applied projects evaluate visual fidelity in stereoscopic content, compression algorithms, and ergonomic considerations for XR devices. She also investigates how environmental context (e.g., familiar size, natural scenes) modulates depth perception accuracy across real and virtual environments. Her research emphasizes translational applications, aiming to optimize display technologies through insights from human visual processing. Ongoing work explores perceptual integration of binocular and monocular cues, attention modulation by depth, and the neurophysiological underpinnings of stereoscopic vision.

Jonathan Cantمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Visual Perception
• Object-Ensemble Perception
• +۶ مورد دیگر

Dr. Jonathan Cant is an Associate Professor at the Department of Psychology, University of Toronto Scarborough (UTSC). His research focuses on cognitive neuroscience, particularly visual perception and cognition using fMRI and behavioral techniques. Born in Hamilton, Ontario, educated at Western University (Psychology) and Harvard University (postdoctoral) Expertise: Object perception, ensemble processing, texture/shape perception, scene recognition The Cant Lab investigates high-level human visual cortex organization, with a focus on the ventral stream's role in perception. Key research includes dissociating shape and surface property processing, material properties in cognition, and object-scene interactions in neural systems. Recent publications highlight ensemble encoding of facial expressions (2025), cross-modal ensemble coding (2024), and texture-shape relationships in ventral cortex (2015). His work bridges modular/distributed cortical organization theories and explores neural substrates for 3D shape vs. material compliance. Teaching includes PSY5222 (Visual Cognitive Representation), PSYC75 (Cognitive Psychology Laboratory), and PSYC51 (Cognitive Neuroscience of Vision). The lab welcomes motivated students and researchers through direct email applications.

• Computer Vision
• Deep Learning
• Semantic Segmentation
• +۳ مورد دیگر

Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.

Hassan Forooshمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Hassan Foroosh is a Professor in the Department of Electrical Engineering and Computer Science at the University of Central Florida (UCF), directing the Computational Imaging Laboratory (CIL). He holds a Ph.D. in Computer Science from INRIA-UNSA, France (1996). Prior to UCF, he worked as a Senior Research Scientist at UC Berkeley (2000–2002) and an Assistant Research Professor at the University of Maryland, College Park (1997–2000). Research Interests: His work focuses on Computer Vision, Image Processing, Machine Learning, and Signal Processing. Notable contributions include LiDAR-based perception, adversarial attacks on detectors, medical imaging analysis, and dataset design for action recognition. His research is supported by NASA, NSF, ONR, and industry partners. Publications & Impact: Over 130 peer-reviewed papers, including influential work on super-resolution techniques, transformer networks for 3D object detection, and adversarial machine learning. His recent work explores analytical reasoning in LLMs and multimodal fusion in sports analytics. Awards: Pierro Zamperoni Award (2004), Best ICPR Paper (2004), Sun Microsystems Academic Excellence Award (2004). Labs/Teams: Director of the Computational Imaging Lab (CIL), UCF. Grants: Active funding from NASA, NSF, and industry collaborators.

Burkhard Maessمشاهده پروفایل

استاد پژوهشی

• Neuroscience
• Auditory Processing
• Signal Analysis
• +۳ مورد دیگر

Dr. Burkhard Maess is a Research Professor and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences, leading the Methods and Development Group Brain Networks. His research focuses on auditory and language processing, signal analysis, and dynamic modeling of neuronal networks. He holds a Diploma in Physics (University of Leipzig, 1987) and a PhD in Physics (University of Leipzig, 1990). His career includes postdoctoral positions at the Academy of Sciences of the GDR and the Free University of Berlin before joining the MPI in 1995. Since 2000, he has led research groups on MEG/EEG signal analysis and cortical network dynamics. His work integrates advanced neuroimaging techniques like MEG and EEG to study sensory processing, neural network dynamics, and the effects of aging on auditory attention. Key contributions include developing high-resolution BEM-FMM methods for source localization and analyzing cross-frequency coupling in neuroscience data. His group also explores spinal cord electrophysiology and the neural underpinnings of perceptual decision-making. Dr. Maess’ research spans cognitive neuroscience, biomedical engineering, and computational modeling, with a focus on bridging empirical findings with theoretical frameworks in neuroscience.

Dimitris Samarasمشاهده پروفایل

استاد

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

Dimitris Samaras is a SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He leads the Computer Vision Lab and holds adjunct roles in Biomedical Informatics and Radiology. His research focuses on computer vision, machine learning, medical imaging, and computational behavioral sciences, with interdisciplinary collaborations in psychology and neuroscience. Education: Ph.D. in Computer Science (University of Pennsylvania, 2001), M.S. in Computer Science (Northeastern University, 1994), Diploma in Computer Engineering (University of Patras, Greece, 1992). Research Interests: Modeling 3D shape and illumination interactions, facial expression analysis, medical image analysis, and applying machine learning to brain imaging. Current funded projects include NIH/NIDA grants, NSF initiatives, and collaborations with institutions like Brookhaven National Lab and Adobe. Publications: Over 150 peer-reviewed papers in top venues like ICCV, CVPR, and MICCAI, with impactful work on shadow removal, face relighting, and digital pathology. Recent trends emphasize medical AI, generative models, and multimodal interactions. Awards: SUNY Chancellor’s Award (2018), Dean’s Millionaire’s Club (2016), and multiple NIH/NSF grants. Recognized for contributions to scholarship and creative activities in academia. Grants & Teams: Leads over $10M in active grants, including projects on AI for penguin population tracking, histopathology image analysis, and robotic assistance. Collaborates with interdisciplinary teams in medicine, engineering, and cognitive science. Labs & Initiatives: Directs the Computer Vision Lab, contributes to the ColdSteel/NSF CVDI-NY SPIR consortium, and co-leads the Sensor and Transportation Security Center with Farmingdale State College.

Lauren Embersonمشاهده پروفایل

دانشیار

• Developmental Psychology
• Cognitive Psychology
• Infant Perception
• +۴ مورد دیگر

Dr. Lauren Emberson (she/her/hers) is an Associate Professor in the Department of Psychology at the University of British Columbia, Faculty of Arts. She directs the Baby Learning Lab, which is part of UBC's Early Development Research Group, a consortium focused on infant and child development. Prior to her position at UBC, Dr. Emberson was an Assistant Professor at Princeton University where she co-founded and co-directed the Princeton Baby and Princeton Kid Labs. Education: Postdoctoral Associate, University of Rochester (PI Aslin) Ph.D, Cornell University (PIs Amso, Goldstein, Spivey) B.Sc, University of British Columbia Dr. Emberson's research focuses on learning, perception (audition, vision, crossmodal or multisensory), language development, face/object perception, and attention in infants. She investigates these capacities using behavioral and neuroimaging techniques, particularly fNIRS (functional near infrared spectroscopy), working primarily with very young infants (birth through 1 year) and preterm/premature infants. Her work examines how infants' learning capacities contribute to rapid development of perception in ecological contexts, with implications for understanding how early life experiences affect later outcomes. Analysis of Dr. Emberson's recent publications reveals a consistent focus on infant perception, learning mechanisms, and neuroimaging methodology. Her work increasingly incorporates advanced fNIRS techniques while maintaining focus on fundamental questions about how infants learn from their environment. There's a growing emphasis on individual differences, cross-cultural comparisons, and applications to infants facing developmental challenges. Dr. Emberson serves on the editorial board of Infancy (journal of the International Congress of Infancy Studies) and is a consulting editor for the Journal of Cognitive Neuroscience . Her research has been published in top journals including PNAS, Current Biology, Psychological Science, Cognition, Developmental Science, and the Journal of Neuroscience. Dr. Emberson has secured significant research funding from prestigious organizations including the Bill and Melinda Gates Foundation, James S. McDonnell Foundation, Natural Sciences and Engineering Research Council (NSERC), Canadian Institutes of Health Research (CIHR), and the National Institutes of Health (NIH). She collaborates with clinicians at BC Women's and Children's Hospitals to understand how different early life experiences impact learning and brain development. Dr. Emberson is currently accepting graduate students into her research program. The Baby Learning Lab, under Dr. Emberson's direction, is part of UBC's Early Developmental Research Group and collaborates with multiple institutions. The lab strives to provide interactive research experiences for infants and families while advancing scientific understanding of early cognitive development. The lab acknowledges that it operates on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people.

João Paulo Costeiraمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Structure from Motion
• +۳ مورد دیگر

João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.

Ohad Friedمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Generative AI (GenAI)
• +۵ مورد دیگر

Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships

• high resolution MRI
• visual perception
• functional imaging
• +۶ مورد دیگر

Reza Farivar-Mohseni is an Associate Professor at McGill University , affiliated with the Faculty of Medicine and Health Sciences and the Department of Ophthalmology and Visual Sciences . He serves as a Scientist at the RI-MUHC (Montreal General Hospital site), contributing to the Brain Repair and Integrative Neuroscience (BRaIN) Program and the Centre for Translational Biology . Research Interests: Dr. Farivar-Mohseni’s work focuses on cortico-cortical communication, information processing in the brain, and disruptions in neurological disorders like traumatic brain injury. He specializes in advancing non-invasive brain imaging (MRI) for both fundamental and clinical applications, particularly improving concussion detection and diagnosis. Publications: His research spans high-resolution MRI, visual perception, and functional imaging. Key themes include depth-cue invariance in object recognition, gamma-band neural representations, and cortical deficits in amblyopia. Recent studies (2025–2022) address computational neuroscience, vision screening tools, and neural imaging techniques. Labs & Collaborations: He collaborates with the MGH-MRI Research Platform and works within the Centre for Translational Biology , focusing on translating imaging advancements into clinical tools.

• Structural Health Monitoring
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.

Sina Sarehمشاهده پروفایل

استاد

• Soft robotics
• Multi-modal mobility
• Manipulation and attachment
• +۱ مورد دیگر

Sina Sareh is a robotics researcher at the Royal College of Art (RCA), where he leads the RCA Robotics Laboratory within the School of Design. He has established himself as an expert in soft robotics and multimodal sensing, developing innovative solutions for human safety and access problems in industrial operations. Dr. Sareh's educational background includes: PhD in Robotics from the University of Bristol, where he worked on monolithic design of flexible actuators for operation in confined liquid environments MSc in Control Systems from the University of Sheffield BSc in Electrical Engineering from Amirkabir University of Technology, Tehran Dr. Sareh's research focuses on soft robotics, multi-modal mobility, manipulation and attachment, and multimodal sensing. His work bridges the gap between robotics engineering and practical applications, particularly in medical and industrial settings. He has developed novel approaches to robotic attachment inspired by octopus biology, created haptic interfaces that mimic the feeling of touching human internal organs, and designed soft robotic technologies to help articulate pain symptoms. His research consistently demonstrates innovation in creating adaptable robotic systems that can operate effectively in complex, unstructured environments where traditional rigid robots face limitations. His publication record demonstrates a strong trajectory in robotics research, with emphasis on soft robotics, medical applications, and novel sensing techniques. The research shows progression from fundamental soft actuator design to practical applications in surgery, industrial operations, and human-robot interaction, with a consistent focus on solving real-world problems through biologically inspired approaches. Dr. Sareh has successfully secured multiple research grants, including EPSRC funding for 'Getting a Grip' and 'Multi-vendor Interoperability in Robotics,' as well as InnoHK funding for 'Intelligent Medicine Warehousing.' He has also served as an impact assessor for the Research Excellence Framework (REF) 2021 in Engineering and is a member of the editorial board at IET Cyber-physical Systems and Robotics Journal. Currently, Dr. Sareh advises research students including Filippo Sanzeni, and maintains active collaborations with industry and academic partners through the RCA Robotics Laboratory, which serves as a hub for interdisciplinary robotics research at the intersection of design, engineering, and human-centered applications. His work on projects like 'Topographies of Pain' and 'Reminisys' demonstrates a commitment to applying robotics technology to improve healthcare outcomes and quality of life.

Nikolaus Kriegeskorteمشاهده پروفایل

استاد

• Computational Neuroscience
• Artificial Neural Networks
• Visual Perception
• +۳ مورد دیگر

Nikolaus Kriegeskorte is a Professor of Psychology and Neuroscience, and Director of Cognitive Imaging at the Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University. He is affiliated with the Departments of Psychology, Neuroscience, and Electrical Engineering. Institution: Columbia University Academic Roles: Professor of Psychology and Neuroscience; Director of Cognitive Imaging Email: nk2765@columbia.edu Location: Jerome L. Greene Science Center, 3227 Broadway, L3-064 Research Focus: The lab explores the cognitive neuroscience of vision, modeling biological visual systems with artificial neural networks. Key areas include developing statistical inference and visualization techniques to bridge theory and experimental data, understanding representational geometry in neural systems, and optimizing deep learning frameworks for neuroscience. Recent Publications: Highlighted work spans neural network modeling of visual perception, representational similarity analysis, and the topology of brain representations. The lab's methods, such as the TorchLens Python package, enable transparent extraction and visualization of hidden layer activations in neural networks. Grants: Projects are supported by funding from the National Science Foundation (NSF) - Cognitive Neuroscience and the National Institutes of Health (NIH) - NIMH. Laboratory: The Visual Inference Lab (kriegeskortelab.zuckermaninstitute.columbia.edu) is located at Quad 3D, Zuckerman Institute, 3227 Broadway.

Marlene Behrmannمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Cognitive Science
• Visual Perception
• +۴ مورد دیگر

Marlene Behrmann is the Thomas S. Baker University Professor of Psychology and Cognitive Neuroscience at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. She leads the Behrmann Lab, which moved to the University of Pittsburgh in 2023. Her research focuses on visual cognition, object recognition, and neural mechanisms of perception, with a particular emphasis on face and word recognition. Behrmann holds a B.A. and M.A. in Speech and Hearing Therapy and a Ph.D. in Psychology from the University of Toronto. She is a leader in her field, recognized by her induction into the National Academy of Sciences (2015) and the American Academy of Arts and Sciences (2019). Her work combines neuropsychological studies of patients with brain damage, neuroimaging, and computational modeling to explore visual processing. Recent research highlights include studies on dorsal-ventral pathway interactions, functional reorganization post-hemispherectomy, and autism-related sensory processing differences. Behrmann has advised numerous graduate students and postdocs, contributing to their academic and professional development. Key awards include her National Academy of Sciences membership and American Academy of Arts and Sciences fellowship. Her lab collaborates widely, publishing in top journals like Cerebral Cortex , PNAS , and Trends in Cognitive Sciences . She also engages in translational research to improve interventions for perceptual and cognitive disorders.

Robert Laganièreمشاهده پروفایل

استاد

• Computer Vision
• Image and Video Analysis
• Visual Surveillance
• +۸ مورد دیگر

Robert Laganière is a Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa, where he has been actively contributing to the fields of computer vision and image analysis. He is a member of the VIVA research laboratory and holds a Ph.D. and M.Sc. from INRS-Telecommunications in Montreal, as well as a bachelor's degree in Electrical Engineering from École Polytechnique de Montréal. Bachelor's in Electrical Engineering: École Polytechnique de Montréal (1987) Master's Degree: INRS-Telecommunications (1990) Doctorate: INRS-Telecommunications (1996) Professor Laganière's research focuses on computer vision, with particular expertise in image and video analysis, visual surveillance, embedded vision systems, and deep learning applications. His work spans fundamental research in feature detection and matching to practical applications in autonomous driving, human recognition, and real-time object tracking. He has made significant contributions to the development of algorithms for pedestrian detection, age and gender recognition, and 3D object localization. His publication trends reveal a consistent focus on practical computer vision applications with a strong emphasis on real-time performance and embedded implementation. Over the past decade, his research has evolved from foundational work in feature matching and homography estimation toward more complex applications in action recognition, human-computer interaction, and intelligent surveillance systems. His work consistently bridges theoretical computer vision with practical engineering constraints, particularly for mobile and embedded platforms. Best Paper Award, IEEE International Conference on Computer and Robot Vision (CRV 2014) Best Paper Award, CVPR Embedded Vision Workshop, Providence, RI, June 2012 Best Real-time Tracker, IEEE International Conference on Computer Vision (ICCV) Workshop on Visual Object Tracking (VOT2015) Professor Laganière has supervised numerous graduate students through the years, with a particular focus on practical applications of computer vision in surveillance, human recognition, and embedded systems. His research has been supported through industry partnerships with companies including CogniVue Corp, NXP, iWatchLife.com, Solink Corp, CBSA Canada, Ross Video, Thales, Habitat Seven, and YouI Labs. He has successfully translated his research into commercial applications through his founding of Visual Cortek (acquired by iWatchLife in 2009) and Tempo Analytics (founded in 2016). As a member of the VIVA research laboratory, Professor Laganière collaborates with colleagues on advanced computer vision projects, particularly those involving intelligent video analytics for security and commerce applications. His work on NAVIRE (Virtual Navigation in Remote Environments) demonstrates his commitment to developing practical solutions for real-world navigation challenges using image-based representations of real environments.