Find Academic Supervisors

Jianguo WangView profile

Professor

• Multisensor Integration
• GNSS Technology
• Precision Engineering Surveying
• +5 more

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.

Christopher StorieView profile

Professor

• GIS
• Remote Sensing
• Urban Geography
• +3 more

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.

Theodore LimView profile

Associate Professor

• Climate Adaptation
• Environmental Justice
• Participatory Action Research
• +5 more

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 .

Siva SivoththamanView profile

Professor

• Solar Photovoltaic Technologies
• Microelectronic Fabrication
• Nanotechnology
• +6 more

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.

Stavros AvramidisView profile

Professor

• Wood-water relations
• Sorption thermodynamics
• Fluid flow in wood
• +8 more

Dr. Stavros Avramidis is a Professor and Head of the Department of Wood Science at the University of British Columbia's Faculty of Forestry. His research focuses on advanced wood physics and drying technologies, integrating computational modeling, spectroscopic analysis, and machine learning applications. Wood-water relations and sorption thermodynamics Fluid flow and heat transfer mechanisms Fractal modeling of nano-pore networks Radio frequency vacuum drying optimization Machine learning for wood property prediction Recent publications demonstrate his leadership in applying data science to wood processing challenges, with emphasis on thermal modification, moisture dynamics, and multispectral identification. He actively supervises graduate research through thesis programs in Forestry (MSc, MASc, PhD) while collaborating on interdisciplinary projects related to wood technology and sustainability. Contact: stavros.avramidis@ubc.ca

• Remote Sensing
• Image Processing
• Computer Vision
• +3 more

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.

Dustin IsleifsonView profile

Associate Professor

• Remote Sensing
• Arctic Science
• Microwave Engineering
• +5 more

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.

• Image processing
• Remote sensing
• Estimation of model parameters
• +7 more

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.

• Remote Sensing
• Sustainable Forest Management
• Forest Biodiversity Monitoring
• +3 more

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

Matthew GiamouView profile

Assistant Professor

• Mobile Robotics
• Convex Optimization
• Computer Vision
• +7 more

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.

Georgia FotopoulosView profile

Professor

• Geodesy
• Satellite Geodesy
• Geophysics
• +7 more

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

Laura CowenView profile

Professor

• Ecological Statistics
• Capture-Recapture Methods
• Tag Loss Models
• +5 more

Laura Cowen is a Professor in the Department of Mathematics and Statistics at the University of Victoria, specializing in ecological statistics with a focus on capture-recapture methods. Her work addresses real-world challenges in wildlife, fisheries, and public health, including estimating population parameters for species like seabirds and fishery populations, and modeling hidden human populations such as injection drug users and homeless individuals. She leads the Cowen Lab, committed to interdisciplinary collaboration and equity, diversity, and inclusion (EDI) initiatives. Her research integrates statistical methods with ecological and public health applications, including tag loss modeling, hidden Markov models, and large-scale data analysis. She collaborates with diverse fields such as microbiology, sociology, and anthropology. Key projects include estimating salmon exploitation rates, California sea cucumber movement, and lemur population viability in fragmented habitats. Cowen received her PhD from Simon Fraser University and has held roles in initiatives like the VADA (Visualization and Data Analytics) team and the WHALE program at SFU. She was awarded the 2025 Bob Riffenburgh Award for bridging statistical ecology and public health applications, including pandemic modeling and serotype occupancy analysis. Her lab emphasizes fostering an inclusive environment and supports Indigenous student initiatives through AISES chapter formation and travel awards.

Howard LiView profile

Professor

• Robotics
• Artificial Intelligence
• Control Systems
• +3 more

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.

Rakesh MishraView profile

Adjunct Professor

• Remote Sensing
• Computer Vision
• Artificial Intelligence
• +1 more

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.

Jean-François LalondeView profile

Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

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.