Computer Vision for Inspection Professors and Supervisors in Canada

Abdel Hakim Bendadaمشاهده پروفایل

استاد

Laval UniversityCanada

Abdel Hakim Bendada is a retired Full Professor at Laval University's Department of Electrical Engineering and Computer Engineering. His career spans over three decades, from Assistant Professor (2005-2010) to Full Professor (2014-present). He also held adjunct roles at Université du Québec en Abitibi-Témiscamingue and served as Head of the Aerospace Group (GAUL) at Laval University. Education: Ph.D. (1991-1995) and M.Sc.A. (1990-1991) from Institut National Polytechnique de Lorraine, France, focusing on infrared thermography and chemical reactor modeling. Research interests include infrared vision, nondestructive testing via thermography, medical imaging, and thermal physics. He pioneered advancements in quantitative infrared vision and developed methodologies for industrial applications like aerospace materials inspection and biomedical thermoregulation studies. Notable awards include the Andronicos Kantsios Award (2007), Star Professor Award (2007, 2009, 2013), and Order of Merit from NRC (2001). He has supervised over 20 Ph.D. and M.Sc. students, contributing to impactful research in materials science and imaging technologies. He has authored 218 publications, with recent work emphasizing 3D thermography, infrared face recognition, and composite material analysis. His grants include NSERC funding for infrared vision tools and Quebec-based projects on smart environmental systems.

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Clemente Ibarra-Castanedoمشاهده پروفایل

پژوهشگر

Laval UniversityCanada

Clemente Ibarra-Castanedo is a Researcher at Laval University's Electrical and Computing Engineering Department, affiliated with the Computer Vision and Systems Laboratory. He holds a Ph.D. in Electrical Engineering (2005), M.Sc. and B.Sc. in Mechanical Engineering from Laval University and Regiomontana University, respectively. His primary research focuses on Nondestructive Testing (NDT) using Infrared Thermography, Digital Signal Processing, and Heat Transfer modeling. He has extensive experience in academic and industrial projects, including roles as a Research Engineer in France and Canada, and Graduate Assistant at Laval University. Key research interests include quantitative subsurface defect evaluation, thermal modeling of composites, and image analysis techniques. He has authored over 138 publications, including peer-reviewed articles on pulsed phase thermography and composite inspections. Awards include the Quebec Excellence Scholarship (2000-2001). Affiliations include ASME, IEEE, and SPIE memberships. His work integrates advanced imaging systems, drone-based inspections, and machine learning for structural health monitoring. Collaborative projects span aerospace composites, civil infrastructure, and cultural heritage preservation. His lab develops innovative non-invasive inspection tools, including drone-enabled thermography and automated defect detection algorithms. Publications highlight contributions to thermal data enhancement, fusion techniques, and deep learning applications in NDT.

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Hai Zhangمشاهده پروفایل

پژوهشگر ارشد

Laval UniversityCanada

Hai Zhang is a Research Fellow affiliated with the Computer Vision and Systems Laboratory at Université Laval. His work focuses on interdisciplinary research spanning Computer Vision, Machine Learning, Non-Destructive Testing, and Materials Science. He specializes in developing advanced algorithms for defect detection, image processing, and material analysis leveraging techniques like infrared thermography, terahertz imaging, and deep learning models such as Faster R-CNN and Swin Transformers. His research also intersects with financial modeling, exploring topics like debt overhang and option compensation. Zhang’s contributions to privacy-preserving distributed learning and optimization algorithms further highlight his expertise in computational methods. He has published extensively on topics including super-resolution imaging, finite element analysis for material inspection, and mathematical theories of electromagnetic phenomena. Zhang’s work frequently emphasizes practical applications in cultural heritage conservation, medical imaging, and industrial material evaluation. His interdisciplinary approach bridges theoretical advancements with real-world problem-solving, particularly in non-invasive inspection techniques for art, historical artifacts, and engineering materials.

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Paul Fieguthمشاهده پروفایل

استاد

University of WaterlooCanada

Paul Fieguth is a Professor and Associate Vice President - Academic Operations at the University of Waterloo. He holds affiliations with the Full-time Faculty, Faculty of Mathematics, and the Intelligent and Autonomous Systems research group. His work focuses on interdisciplinary areas including machine learning, computer vision, medical imaging, and deep learning techniques for solving complex engineering and biological problems. He has contributed to advancements in photoacoustic remote sensing, autonomous systems, and large-scale biodiversity datasets like BIOSCAN-5M. His research bridges theoretical foundations (e.g., pattern recognition, inverse problems) with practical applications in robotics, medical diagnostics, and environmental monitoring. Education details are not explicitly provided in the text, but his professional roles suggest advanced training in computer science and engineering disciplines. His research interests span a wide range, including but not limited to: pattern recognition algorithms, deep learning architectures, remote sensing technologies, and computational methods for medical imaging. Recent work emphasizes innovations in rail defect detection, 3D reconstruction, and biodiversity assessment through multimodal datasets. Publications from 2022–2025 highlight contributions to fields like neural network optimization, uncertainty quantification, and generative adversarial networks for medical applications. While no specific awards are listed, his prolific publication record reflects recognition in academic circles. Advising and grants sections remain underdeveloped in the provided information, though his leadership roles suggest involvement in institutional research initiatives. He is a key member of teams advancing technologies such as PARS imaging and autonomous systems at the University of Waterloo.

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Jonathan Liمشاهده پروفایل

استاد

University of WaterlooCanada

Jonathan Li is a full professor at the University of Waterloo, holding dual appointments in the Department of Geography and Environmental Management and the Department of Systems Design Engineering. His expertise lies in geospatial data science and artificial intelligence, focusing on urban remote sensing, LiDAR point cloud processing, and applications in digital twin cities and autonomous vehicles. He leads research in GeoAI for multi-scale mapping, 3D urban modeling, and environmental monitoring. Dr. Li is a Fellow of multiple prestigious institutions including IEEE, Royal Society of Canada, and Canadian Academy of Engineering. He currently serves as President of the Canadian Institute of Geomatics (CIG) and Editor-in-Chief of the International Journal of Applied Earth Observation and Geoinformation (JAG). His work bridges academic research with practical applications in infrastructure inspection, disaster management, and geospatial software development. Research Interests: Urban remote sensing using multispectral, hyperspectral, and LiDAR data; machine learning for geospatial analysis; GeoAI for environmental monitoring; 3D object detection in mobile mapping systems. His lab focuses on developing innovative software tools for HD mapping and geospatial data analysis. Recent Publications: Over 50 articles in journals like Remote Sensing of Environment, IEEE Transactions on Geoscience and Remote Sensing, and ISPRS Journal of Photogrammetry. Awards: Multiple fellowships and editorial roles reflect his leadership in geomatics and remote sensing. Grants: Extensive funding for research in AI-driven geospatial solutions. Lab Activities: Prof. Li's research group collaborates with industry on projects involving autonomous vehicle mapping, urban flood modeling, and smart city infrastructure. Their work emphasizes cross-disciplinary collaboration between geomatics, computer science, and civil engineering.

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Mohand Said Alliliمشاهده پروفایل

استاد

University of Quebec in OutaouaisCanada

Mohand Said Allili is a full professor in the Department of Computer Science and Engineering at the University of Quebec in Outaouais (UQO). He directs the Imaging, Vision, and Artificial Intelligence Research Laboratory (LARIVIA), focusing on machine vision, machine learning, multimedia data processing, and cybersecurity applications in medical and aerial imagery analysis. His research spans over three decades with notable contributions in image segmentation, texture analysis, deep learning models for medical diagnostics, and AI-driven infrastructure inspection systems. Key projects include drone-based bridge defect detection systems and dermatological imaging networks. His work integrates probabilistic modeling (e.g., Gaussian mixtures) with modern neural networks for robust computer vision solutions. Publications highlight trends in: Deep learning applications for infrastructure monitoring (bridge defect detection) Medical imaging advancements (skin lesion segmentation) Augmented reality systems for remote inspection Innovative texture modeling techniques Allili's lab develops cutting-edge tools like MEDiXNet for dermatological analysis and SES-ReNet for human detection in adverse conditions. His work bridges theoretical computer science with practical engineering solutions. Research focuses on: Semantic image/video annotation Multimodal data fusion Lightweight neural networks for edge computing Context-aware feature extraction

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Tony Yangمشاهده پروفایل

استاد

University of British ColumbiaCanada

Dr. Tony Yang is a Professor in the Department of Civil Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He leads the UBC Smart Structures research group, focusing on innovative technologies for structural and earthquake engineering. Yang holds a B.Sc. from the University of Buffalo (2001), an M.Sc. (2002), and a Ph.D. from UC Berkeley (2006). His research spans advanced numerical simulations, robotic construction, AI-driven monitoring, and seismic design of resilient infrastructure. Yang is a Fellow of the Canadian Academy of Engineering and serves on 15+ technical committees, including the National Building Code of Canada’s seismic design committee. His work integrates 5G networks, robotics, and AI to advance smart construction technologies. Recent projects include a $812K industrial grant (2024) for 5G-enabled autonomous construction systems, AI inspection robots, and rapid construction methodologies. His research interests include: robotic construction automation, AI-based structural monitoring, performance-based seismic design, and sustainable infrastructure. Key contributions include over 250 publications, 14 patents, and influential roles in high-impact journals like Computer-Aided Civil and Infrastructure Engineering . Yang’s accolades include the EGBC Meritorious Achievement Award (2020), New Zealand Concrete Society Technology Award (2019), and the H.A. Krentz Research Award (2014). His lab develops cutting-edge solutions for autonomous construction sites, advanced structural fuses, and AI-driven damage detection systems.

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Il-Min Kimمشاهده پروفایل

استاد

Queen's University at KingstonCanada

Professor Il-Min Kim is a Full Professor in the Department of Electrical and Computer Engineering at Queen's University (Smith Engineering). He leads the Wireless Artificial Intelligence Laboratory (WAI Lab), focusing on AI-driven wireless systems, IoT/IoE/IIoT, federated learning, edge computing, and 6G/V2X communications. His work integrates machine learning, signal processing, and cybersecurity with wireless infrastructure. Education: B.S. (Yonsei University, 1996), M.S./Ph.D. (KAIST, 2001). Postdoctoral research at MIT (2001-2002) and Harvard (2002-2003) preceded his faculty role at Queen's since 2003. Promoted to Associate Professor (2009) and Full Professor (2014). Research emphasizes Wireless AI , including on-device AI, federated learning, geoscience AI (Geo-AI), and secure communication protocols. Key areas: AI for 6G/V2X, energy-efficient edge computing, and robust spectrum sensing. His lab develops frameworks for distributed systems, compressive sensing, and nonlinear energy harvesting. Notable contributions include hybrid replay mechanisms for incremental learning, diffusion models for industrial IoT, and deep learning-based channel estimation. His work addresses challenges in data security, model robustness, and real-time inference under resource constraints.

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William Melekمشاهده پروفایل

استاد

University of WaterlooCanada

William Melek is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He serves as Director of the Laboratory of Computational Intelligence and Automation and RoboHub. His research focuses on intelligent systems, modular robotics, autonomous vehicles, and bioinformatics. He holds a B.A.Sc. in Electrical & Computer Engineering (Zagazig University, 1994), M.Sc. and Ph.D. in Mechanical Engineering (University of Toronto, 1998/2002), and a Post-Doctorate in Computer Science (Ryerson University, 2004). Education: Bachelor's: B.A.Sc in Electrical & Computer Engineering, Zagazig University (1994) Master's: Mechanical Engineering, University of Toronto (1998) Doctorate: Mechanical Engineering, University of Toronto (2002) Post-Doctorate: Computer Science, Ryerson University (2004) His research interests span artificial intelligence, intelligent control, mechatronics, automotive design, and computational intelligence. Recent work includes autonomous vehicle navigation, predictive maintenance in manufacturing, and bioinformatics-driven clinical decision support systems. He teaches courses such as ME 360 (Control Systems), ME 780 (Mechatronics), and MTE 100 (Intro to Mechatronics). Articles from 2023–2025 highlight advancements in autonomous driving decision-making, modular robotics, and 5G-enabled teleoperation systems. His lab develops algorithms for urban autonomous systems and industrial automation. He currently oversees graduate student applications in mechatronics and robotics. Labs/Teams: Laboratory of Computational Intelligence and Automation, RoboHub.

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Mohammad Nadimiمشاهده پروفایل

استادیار

University of ManitobaCanada

Photonics and Spectroscopy
Agri-Food Quality Monitoring
Machine Learning and Artificial Intelligence
+۷ مورد دیگر

Mohammad Nadimi is an Assistant Professor in the Department of Biosystems Engineering at the Price Faculty of Engineering, University of Manitoba. He holds a PhD in Electrical Engineering from the same institution and has a multidisciplinary research profile spanning photonics, food quality, and data analytics. His work focuses on integrating advanced optical techniques with machine learning to improve agri-food production and storage systems. Education: PhD in Electrical Engineering, University of Manitoba, 2018 M.Sc. in Electrical Engineering, Iran University of Science and Technology, 2011 Dr. Nadimi's research centers on real-time quality monitoring of agri-food products using electromagnetic imaging, spectroscopy, and smart sensing technologies. He applies machine learning and AI to optimize data analysis in large-scale agricultural datasets. His work also includes microstructural analysis of food materials and laser-based biostimulation to enhance crop viability. These efforts aim to reduce post-harvest losses and improve food safety and sustainability. The selected publications reflect a strong trend in applying photonics and artificial intelligence to food quality assurance. Key areas include hyperspectral imaging, NIR spectroscopy, terahertz sensing, and deep learning models for contamination detection, spoilage prediction, and structural analysis of grains and legumes. His work bridges engineering innovation with practical agricultural challenges. Scientific Service: Senior Editor, Measurement: Food (Elsevier) Dr. Nadimi has published over 40 peer-reviewed journal articles and 20 conference papers. He is actively involved in graduate education and is currently seeking new students for research projects. His industry experience as a Senior Data Analyst at Wawanesa Insurance further strengthens his expertise in big data analytics and risk modeling. He leads a research program that combines experimental photonics with computational intelligence to develop next-generation food monitoring systems. Laboratory and Research Team: Dr. Nadimi's research group focuses on developing integrated photonics and data analytics solutions for agri-food systems. The team works on sensor development, machine learning model training, and real-time monitoring platforms for grain and oilseed storage environments.

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David Capsonمشاهده پروفایل

استاد

University of VictoriaCanada

David Capson is a Professor in the Department of Electrical and Computer Engineering at the University of Victoria. His research focuses on computational vision, emphasizing algorithms and architectures for accelerated and embedded image analysis, with applications in robotics, metrology, inspection systems, and servo systems. He holds credentials including a BScEng from the University of New Brunswick, MEng and PhD from McMaster University, and is a Professional Engineer (PEng). Dr. Capson's work bridges theoretical advancements in vision-based algorithms with practical industrial applications. His lab likely explores real-time embedded systems and robotics navigation, though specific grants or lab names are not detailed in the text. He is affiliated with the UVic Electrical & Computer Engineering department, actively involved in academic advising and possibly course instruction given his faculty role. No specific awards or grants are listed, but his professional engineering designation suggests industry-relevant contributions. He can be contacted at capson@uvic.ca and his office is located in EOW 419.

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Homayoun Najjaranمشاهده پروفایل

استاد

University of VictoriaCanada

Homayoun Najjaran is a Professor of Mechanical Engineering and Electrical and Computer Engineering at the University of Victoria (UVic), cross-listed between departments. He founded the Advanced Control and Intelligent Systems (ACIS) laboratory, focusing on AI, robotics, and control systems. Dr. Najjaran earned his Ph.D. from the University of Toronto in 2002 and holds a Professional Engineer (P.Eng.) license in British Columbia. As a Fellow of the Canadian Society for Mechanical Engineering (CSME), he leads research in autonomous systems, industrial automation, and smart manufacturing. His work integrates machine learning, computer vision, and MEMS technologies to enhance system reliability and safety. Research interests span control systems, robotics, AI-driven manufacturing, and wearable technologies. Notable projects include vacuum bagging leakage prediction in composite manufacturing and sim-to-real domain adaptation for robotics. He collaborates with industry partners in aerospace, healthcare, and automation, emphasizing practical applications. Dr. Najjaran also serves as President of Advanced Engineering Solutions Inc., providing technical consulting services. Awards & Recognition Fellow of the Canadian Society for Mechanical Engineering (CSME) Professional Engineer (P.Eng.) in British Columbia Lab & Collaborations The ACIS Lab develops AI-driven solutions for industrial automation, human-robot collaboration, and digital twins. Partnerships include aerospace, automotive, and healthcare sectors.

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Hong Zhangمشاهده پروفایل

استاد

McGill UniversityCanada

Professor Hong Zhang serves as Chair Professor in the Department of Electronic and Electrical Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, having transitioned from his full professorship at the University of Alberta where he maintains an adjunct appointment. He directs the Centre for Intelligent Mining Systems (CIMS) and affiliates with the Robotics Research Laboratory, establishing new robotics research groups at both institutions. His academic foundation includes a BSc from Northeastern University (USA), PhD from Purdue University, and postdoctoral training at the University of Pennsylvania, joined the University of Alberta in 1988. Zhang's research spans robotics, computer vision, and image processing with emphasis on visual navigation, simultaneous localization and mapping (SLAM), and biologically-inspired collective robotics. His work develops cooperative multi-robot systems for challenging environments like mining and underwater inspection, addressing practical industrial challenges through vision-based sensing and navigation solutions. Recent publications (2019-2020) demonstrate leadership in robotic perception, particularly moving object detection under illumination changes and HDR reconstruction from polarization data, reflecting consistent innovation in computer vision applied to real-world robotics. ASTech Award (2008) IEEE Millennium Medal Best Conference Paper at IEEE ROBIO 2019 2018 IROS Distinguished Service Award Fellow of Canadian Academy of Engineering (2015) Fellow of IEEE (2014) NSERC Industrial Research Chair (2003-2017) Mentoring notable students including Xuesong Wu, Sean, and Moein who produced award-winning conference papers, Zhang's research has secured substantial funding through a decade-long Syncrude Canada partnership (NSERC/Alberta Innovates), the NSERC Industrial Research Chair, and a recent $7.1M Alberta Government grant for the Centre for Autonomous Systems. He leads the Centre for Intelligent Mining Systems at the University of Alberta while establishing a new robotics research group at SUSTech focused on intelligent sensing systems for industrial automation and navigation.

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