Multisensor Systems Professors and Supervisors in Canada | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱-۱۵ از ۱۹ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Jianguo Wangمشاهده پروفایل

استاد

York UniversityCanada

Dr. Jianguo Wang is a Professor in the Department of Earth and Space Science Engineering at York University's Lassonde School of Engineering. He has been a faculty member since 2006 and is a founding member of the Lassonde School. With over 35 years of academic and industrial experience, he specializes in multisensor integration, GNSS technology, and precision engineering surveying. He holds a Dr.-Ing. in Geomatics Engineering from Universität der Bundeswehr München, Germany, alongside Bachelor’s and Master’s degrees from Wuhan Technical University of Surveying and Mapping (WTUSM). His research focuses on advanced data processing methodologies, including Kalman filtering, error analysis, and LiDAR systems. He has authored/co-authored over 60 publications, including textbooks like Error Theory and Foundation of Surveying Adjustment and Foundation of Geodesy . He is a Fellow of Engineers Canada and licensed as a Professional Engineer in Ontario. Education: Dr.-Ing., Geomatics Engineering, Universität der Bundeswehr München (Germany) M.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping B.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping Dr. Wang teaches courses such as Advanced Optimization and Applications , GNSS , and Global Geophysics and Geodesy . He leads the Earth Observation Laboratory (PSE 432), focusing on multisensor integration for navigation and positioning. His work explores innovative solutions for sensor calibration, data fusion, and geospatial applications. Grants & Labs: Active in lab-based research with collaborators like Baoxin Hu, his laboratory integrates GNSS, IMUs, LiDAR, and cameras for precision navigation. His recent work addresses challenges in sensor error calibration, LiDAR point cloud accuracy, and Kalman filter enhancements.

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Christopher Storieمشاهده پروفایل

استاد

The University of WinnipegCanada

GIS
Remote Sensing
Urban Geography
+۳ مورد دیگر

Christopher Storie is a Professor of Geography at the University of Winnipeg, specializing in GIS, Remote Sensing, and Urban Geography. He holds an office in Lockhart Hall (5L03) and teaches courses such as Introduction to GIS, Remote Sensing, and Urbanization in the Developing World. His research focuses on Deep Learning applications for automated land use/land cover mapping, informal settlement mapping, and urban-rural fringe detection. Key research interests include leveraging neural networks for geospatial analysis, urban dynamics in regions like Mexico City, and collaborative international fieldwork. He has contributed to over 20 peer-reviewed articles since 2000, emphasizing satellite imagery analysis and environmental monitoring. His work bridges technical geospatial methods with socio-environmental challenges in urban and developing regions.

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Theodore Limمشاهده پروفایل

دانشیار

University of British ColumbiaCanada

Theodore Lim is an Associate Professor at the University of British Columbia's School of Community and Regional Planning (SCARP). He holds a PhD in City & Regional Planning from the University of Pennsylvania, an MS in Environmental Science and Engineering from Tsinghua University, and a BA in Immigrant Studies from Swarthmore College. PhD: University of Pennsylvania (City & Regional Planning) MS: Tsinghua University (Environmental Science and Engineering) BA: Swarthmore College (Immigrant Studies) Theo's research focuses on urban climate adaptation and environmental justice , particularly through participatory action research with marginalized communities. His work bridges environmental science, data modeling, and community engagement to address issues like urban heat resilience using both grassroots and technical approaches. His recent publications span interdisciplinary topics in climate adaptation , participatory modeling , and urban analytics . Key themes include integrating diverse knowledge systems into environmental governance, addressing power dynamics in socio-ecological systems modeling, and developing trauma-informed approaches to heat resilience planning. NSF CAREER Grant for computer model governance in sustainable water quality $1M NSF CIVIC Innovation award for heat resilience in Roanoke, VA Theo mentors students in urban analytics and policy research, with former advisees including PhD student Ayda Kianmehr and undergraduate researcher Jack Carroll. He also serves as Social Media Editor for Planning Theory and Practice .

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Thia Kirubarajanمشاهده پروفایل

استاد

McMaster UniversityCanada

Dr. Thia Kirubarajan is a Professor and Distinguished Engineering Professor in the Electrical and Computer Engineering Department at McMaster University. He holds the NSERC/General Dynamics Mission Systems-Canada Industrial Research Chair in Target Tracking and Information Fusion. His expertise spans Estimation Theory, Multisensor-Multitarget Tracking, Information Fusion, and Signal Processing. He has led the Estimation, Tracking and Fusion Research Laboratory (ETFLab) with over 50 students and researchers, including PhD candidates and postdoctoral fellows. His academic journey includes degrees from Cambridge University (B.A., M.A.) and the University of Connecticut (M.S., Ph.D.). Education: B.A./M.A. (Cambridge, UK), M.S./Ph.D. (University of Connecticut, USA) Research Interests: Multisensor tracking, sensor fusion, fault diagnosis, and autonomous systems Dr. Kirubarajan has received notable awards including the Barry Carlton Award and Ontario Premier's Research Excellence Award. He teaches advanced courses like Algorithms for Parameter and State Estimation (ECE 771) and has advised numerous students across PhD, M.A.Sc., and undergraduate levels. His research lab collaborates internationally, hosting visiting scholars and fostering innovation in smart systems and transportation.

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Siva Sivoththamanمشاهده پروفایل

استاد

University of WaterlooCanada

Professor Siva Sivoththaman is a Full Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Director of the Centre for Advanced Photovoltaic and Display Systems (CAPDS) and Associate Dean of Graduate Studies & Postdoctoral Affairs. He holds a PhD from the University of Paris XII (1993) and previously worked as a Research Scientist at IMEC (Belgium). His research focuses on photovoltaic technologies, nanotechnology, quantum dots, and energy systems. He has led prestigious chairs, including the NSERC Industrial Research Chair in RF-MEMS (2002-2006) and Ontario Research Chair in Renewable Energy (2010-2015). Education: Doctorate in Engineering Sciences, University of Paris XII (1993) Master's in Energy Technology, Asian Institute of Technology (1989) Bachelor's in Electrical & Electronics Engineering, Madurai Kamaraj University (1987) Research Interests: His work spans solar photovoltaics, semiconductor materials, nanostructure synthesis, and smart energy systems. Notable contributions include quantum dot-enabled devices, advanced photodetectors, and thin-film technologies. He also investigates safety aspects of nanomaterials processing. Publications & Awards: Over 100 peer-reviewed publications, including key works on quantum dot functionalization and photovoltaic device design. Recipient of the Ontario Premier's Research Excellence Award (2002). Teaching & Leadership: Teaches courses on semiconductor physics, microfabrication, and photovoltaic energy conversion. Leads CAPDS, advancing renewable energy and display technologies. Active in IEEE Electron Devices Society.

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Ihsen Hedhliمشاهده پروفایل

پژوهشگر

Laval UniversityCanada

Ihsen Hedhli is a Researcher affiliated with the Computer Vision and Systems Laboratory, focusing on advanced image analysis techniques. His work bridges remote sensing, computer vision, and statistical modeling to address challenges in multisensor/multiresolution data interpretation and image generation. Key research areas include hierarchical Markov models for land cover classification, image-to-image translation with low-resolution conditioning, and lifelong machine learning systems. His contributions emphasize contextual classification frameworks, causal modeling approaches, and generative models for diverse image enhancement tasks. Publications span 2010–2024, demonstrating expertise in satellite image processing, super-resolution techniques, and fusion of multisource remote sensing data. Notable work includes causal hierarchical frameworks for multiresolution image analysis and domain-agnostic translation models. Active in disaster monitoring applications through remote sensing data fusion, with methodologies applied to tropical forest analysis and urban area classification. Current research trends highlight integration of low-resolution guidance in visual synthesis and adaptive learning systems.

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Nicholas Coopsمشاهده پروفایل

استاد

University of British ColumbiaCanada

Nicholas Coops is a Professor and Department Head at the University of British Columbia Faculty of Forestry , Department of Forest Resources Management. He holds the UBC Chair in Forest Management and directs the Integrated Remote Sensing Studio (IRSS) , focusing on remote sensing technologies for forest conservation, management, and production. His research spans vegetation physiology modeling, LiDAR applications, biodiversity monitoring, and climate change impacts on forests. Department Head, Forest Resources Management UBC Chair in Forest Management Director, Masters in Geomatics for Environmental Management Co-director, Master of Sustainable Forest Management Key research areas include: Integration of high spectral/temporal remote sensing with plant physiological models Development of light-use efficiency (LUE) estimation techniques National LiDAR deployment for forest structure analysis Dynamic Habitat Index (DHI) for biodiversity assessment 3PG/3PGS forest growth modeling with remote sensing inputs His recent work emphasizes: Advancements in LiDAR for forest inventory and carbon quantification UAV-based monitoring of tree physiological behavior Time-series analysis of forest recovery post-wildfire Deep learning fusion of ALS, multispectral, and topographic data Intertidal biofilm dynamics using hourly UAV acquisitions Climate adaptation studies in boreal and Pacific Northwest forests Scientific accolades include: Highly Cited Researcher (2019-2024) Fellow of the Royal Society of Canada (2022) Marcus Wallenberg Prize (2020) Gold Medal – Canadian Remote Sensing Society (2020) UBC Killam Research Prize (2020) Canadian Forest Service Collaboration Award (2013) Collaborations with Canadian Forest Service and international teams have produced: Best practice guides for LiDAR applications Nationally significant datasets via Canada Forest Information System Advancements in biodiversity monitoring frameworks (Science 2015, Nature 2016) Development of the Dynamic Habitat Index (DHI) applied globally

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Dustin Isleifsonمشاهده پروفایل

دانشیار

University of ManitobaCanada

Dr. Dustin Isleifson is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Manitoba, affiliated with the Clayton H. Riddell Faculty of Environment, Earth, and Resources. He serves as Director of the Centre for Earth Observation Science (CEOS). His academic background includes a B.Sc. (2005) and Ph.D. (2011) in Electrical & Computer Engineering from the University of Manitoba. His research focuses on remote sensing technologies, sensor development, and Arctic environmental studies, particularly sea ice dynamics and oil spill detection using microwave radar systems. Key projects include experimental studies at the Sea-ice Environmental Research Facility (SERF), satellite data analysis, and antenna design for space applications. Teaching: Courses include ECE 4290 Microwave Engineering, GEOG 4200/7480 Advanced Methods in Remote Sensing, and ECE 7440 Introduction to Microwave Remote Sensing. Research Interests: Sea ice remote sensing, antenna design for satellite systems, oil contamination monitoring, and numerical modeling of microwave interactions with environmental surfaces. Lab/Affiliations: Leads research teams at CEOS and collaborates on Arctic field experiments, CubeSat development (ArcticSat project), and sensor integration with solar cells. His work bridges electrical engineering and environmental science, addressing challenges in Arctic observation and pollution detection. Over 80 publications span radar backscatter modeling, antenna innovation, and interdisciplinary environmental studies.

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Max MIGNOTTEمشاهده پروفایل

استاد

University of MontrealCanada

Max Mignotte is a Full Professor at the Department of Computer Science and Operations Research , Faculty of Arts and Sciences, Université de Montréal. He leads research in Image Processing, Remote Sensing, and Bayesian Inference , focusing on applications in medical imaging, computer vision, and data fusion. His work includes developing advanced algorithms for saliency estimation, multimodal change detection, and 3D reconstruction. Research Interests: Image processing techniques for medical diagnostics, remote sensing analysis, and machine learning-driven solutions for visual attention and segmentation. His methodologies often integrate Bayesian models and fusion strategies to address complex problems in computer vision and biomedical applications. Key Projects: Leading the Multi-Dimensional Scaling Maps for Saliency Estimation initiative. Developing Bayesian fusion models for remote sensing applications. Advancing 3D biplanar reconstruction of human limbs using statistical models. Grants & Teams: Principal investigator on CRSNG-funded projects, including New unsupervised Bayesian and energy-based models (2022–2028) and Bayesian fusion models (2016–2023). Collaborates with the Laboratoire de traitement d’images and interdisciplinary teams in biomedical research.

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Matthew Giamouمشاهده پروفایل

استادیار

McMaster UniversityCanada

Dr. Matthew Giamou is an Assistant Professor in the Department of Computing and Software at McMaster University, leading the Autonomous Robotics and Convex Optimization (ARCO) Lab. His research focuses on global optimization, sensor calibration, SLAM, and motion planning, with applications in robotics, manufacturing, and geosciences. He emphasizes developing interpretable and safety-certifiable algorithms as alternatives to deep learning. Education: B.A.Sc. in Engineering Science (University of Toronto, 2015), M.Sc. in Aeronautics and Astronautics (MIT, 2017), Ph.D. in Aerospace Studies (University of Toronto, 2022). Postdoctoral work at Northeastern University’s Institute for Experiential Robotics. Research interests include mobile robotics , convex optimization , computer vision , and machine learning . ARCO Lab’s current projects involve scalable spatiotemporal algorithms for robust perception and planning. His work bridges theoretical optimization with practical robotics challenges like multi-robot communication and medical robotics. Publications reflect a focus on geometric robotics, optimization methods, and sensor systems. Key themes include inverse kinematics, SLAM, and calibration algorithms with formal safety guarantees. Scientific Awards: - 2020 IEEE IROS Best Workshop Paper Award - 2020 Robotics: Science and Systems Best Student Paper - 2019 RBC AI Graduate Fellowship Advising: Actively mentoring graduate students in robotics and optimization. Lab activities emphasize collaboration with non-experts in fields like space exploration and manufacturing. Teaching includes advanced courses on group theory for optimization and machine learning (CAS 752). Lab Infrastructure: ARCO Lab is housed in ABB C536, with facilities for robotics prototyping and algorithm testing. Ongoing projects aim to deploy safe autonomy tools for diverse end-user domains.

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Georgia Fotopoulosمشاهده پروفایل

استاد

Queen's University at KingstonCanada

Geodesy
Satellite Geodesy
Geophysics
+۷ مورد دیگر

Professor Georgia Fotopoulos is a Geomatics Engineer and Professor at Queen's University's Department of Geological Science and Geological Engineering, part of the Faculty of Arts and Science. She leads the Geodesy and Geophysics Lab, focusing on geodetic data integration for Earth observation. Her work bridges satellite geodesy, positioning systems, and geophysical surveys, with applications in environmental monitoring and mineral exploration. Education: PhD (2003): Gravity Field and Geodynamics, University of Calgary Master's: Positioning and Navigation (Geomatics), University of Calgary Research Interests: Merging multi-platform geodetic data High-resolution UAV aeromagnetic surveys Height reference systems and geoid determination 3D digital outcrop modeling Autonomous navigation in GNSS-denied environments Publications: Her work emphasizes geospatial innovation, with recent focus on GRACE/GRACE-FO data analysis, UAV magnetometry standards, and urban seismic noise characterization. She co-authored Geoid Determination: Theory and Applications (2013). Awards & Affiliations: Loránd Eötvös Award Associate Editor, Geomatica Research Director, Queen's GeoEngineering Centre Advising & Labs: Supervised over 20 graduate students in geophysics/geodesy Co-directs the Geodesy & Geophysics Lab Labs conduct fieldwork globally, including Arctic surveys and mineral exploration

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Howard Liمشاهده پروفایل

استاد

University of New BrunswickCanada

Howard Li is a Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick's Faculty of Engineering. His research focuses on robotics, artificial intelligence, unmanned vehicles, control systems, and quantum computing. Dr. Li completed his Ph.D. at the University of Waterloo and has conducted research internationally at institutions including EPFL (Switzerland) and University of Pavia (Italy). He has industry experience developing training systems for fighter aircraft and control systems for autonomous vehicles. His research interests span robotics, autonomous systems, quantum computing, and AI applications in mechatronics. Recent publications demonstrate focus on LiDAR simulation, deep learning architectures, satellite imagery analysis, and geospatial algorithms. Dr. Li has secured over $1,000,000 in research grants from agencies including NSERC, CFI, and DRDC. He chairs the IEEE Autonomous Robotics Group and contributed to the first IEEE robotics standard. Harrison McCain Foundation Young Scholars Award NSERC Postgraduate Scholarship IEEE Senior Member recognition He established the COBRA research group and open-source Quad Rotor Simulator. All his former postdocs/PhDs secured positions at prestigious institutions including MIT and NASA.

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Rakesh Mishraمشاهده پروفایل

استاد مدعو

University of New BrunswickCanada

Rakesh Mishra is an Adjunct Professor in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick and Chief Technology Officer at SceneSharp Technologies. His expertise includes multi-sensor data fusion, computer vision, and AI-based object detection using LiDAR, SAR, and satellite imagery. Dr. Mishra completed his PhD at UNB and has research experience with institutions including Indian Institute of Technology Kanpur and IIRS-ISRO. He has collaborated with Canadian government agencies including DND, NRC, and NRCan. His research focuses on LiDAR simulation, deep learning applications in remote sensing, and geospatial algorithm development. Recent publications demonstrate advancements in physics-aware deep learning for LiDAR and super-resolution techniques for satellite imagery.

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Jean-François Lalondeمشاهده پروفایل

استاد

Laval UniversityCanada

Jean-François Lalonde is a Full Professor in the Department of Electrical and Computer Engineering at Université Laval, where he has worked since 2013. He serves as Deputy Scientific Director at the Institute for Intelligence and Data (IID) and Director of the Digital Vision and Systems Laboratory. His research focuses on integrating physics-based models with data-driven machine learning techniques for computer vision, graphics, and AR/VR applications. Education: Ph.D. in Robotics, Carnegie Mellon University (2011) MS in Robotics, Carnegie Mellon University (2006) B.Eng. in Computer Engineering, Université Laval (2004) His research at the intersection of computer vision and machine learning emphasizes lighting estimation, HDR imaging, and photobiological applications in architecture. His work has produced significant datasets and tools for academic use. Scientific Awards: Teaching Star Award, Faculty of Science and Engineering, Université Laval (2025, 2024, 2022, 2020, 2019, 2018, 2017, 2016, 2015) He actively mentors students and has guided recipients of prestigious NSERC scholarships. His research involves collaborations with Adobe, Meta, and other high-tech companies, including technology transfer through patents and consulting.

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Marc Hébertمشاهده پروفایل

استاد

Laval UniversityCanada

Marc Hébert is a Full Professor in the Department of Ophthalmology and Otorhinolaryngology at Laval University's Faculty of Medicine, where he directs the Visual Electrophysiology and Photobiology Laboratory and serves as Deputy Scientific Director of Clinical Research at the CERVO Brain Research Center. His work bridges ophthalmology, neuroscience, and mental health through innovative light-based research. Dr. Hébert holds a Bachelor's degree in Psychology from the University of Ottawa and a PhD in Neurobiology from the University of Montreal, followed by postdoctoral training at Rush Presbyterian St-Luke’s Medical Centre in Chicago and the University of Alberta. His research centers on chronobiology and photobiology, investigating how light influences mood, sleep, and circadian rhythms. He pioneered light therapy applications for seasonal affective disorder and developed interventions to improve vigilance in night workers. His groundbreaking work on retinal biomarkers for psychiatric illnesses has revealed distinct electroretinographic patterns in depression and schizophrenia. Analysis of his recent publications shows a dominant focus on translating electroretinography and pupillometry into clinical diagnostics for Parkinson's disease and mood disorders, while expanding into architectural photobiology with northern lighting applications. His work consistently targets practical health solutions through interdisciplinary collaboration. Key recognitions include: Senior research scholar career award from FRSQ (2010-2014) Named one of Quebec City's 19 most fascinating personalities by Scope Magazine (2008) As an advisor, Dr. Hébert supervises Valérie Gagné (Master's), Marc-André Dubois (PhD), and Victoria Soto Linan (PhD), while securing significant funding through Quebec's InnovExport Fund for his spin-off Chronophotonix. His research has generated extensive media coverage and led to two commercial ventures: Chronophotonix for occupational safety lighting and diaMentis for psychiatric diagnostics. The Visual Electrophysiology and Photobiology Laboratory operates seven cutting-edge platforms for human and animal electrophysiology, collaborating with the CERVO Research Center on projects ranging from night worker adaptation to retinal biomarker validation. Current initiatives include refining Parkinson's diagnostic protocols and developing personalized light interventions for mental health.

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