جستجوی استاد راهنمای دانشگاهی

Hua Luمشاهده پروفایل

استاد

• Experimental Mechanics
• Reliability of Structures and Devices
• Solid Mechanics
• +۵ مورد دیگر

Dr. Hua Lu is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. His research focuses on experimental mechanics and reliability analysis of microelectronic packaging and mechatronic systems. He is affiliated with the Laboratory for Experimental Mechanics Research and actively contributes to advancements in thermal-mechanical performance characterization. Education: BSc, Tianjin University, 1968 MSc, Tianjin University, 1982 PhD, Stony Brook University, 1989 Dr. Lu's research interests center on experimental solid mechanics, with emphasis on microelectronic packaging reliability, thermal-mechanical behavior of thin structures, and optical methods for deformation measurement. His work bridges mechanical engineering with electronics manufacturing, focusing on failure prediction and performance optimization of advanced packaging technologies. He applies inverse methods, computer vision, and fiber-optic sensing to real-world reliability challenges in microsystems. The recent publications highlight a strong trend in developing and applying advanced experimental techniques—particularly inverse modeling, optical diagnostics, and sensor integration—to assess reliability in microelectronics. These works span disciplines including mechanical behavior of materials, thermal analysis, and structural health monitoring, with consistent focus on flip-chip and PBGA technologies. Professional Affiliations: Surface Mounting Technology Association (SMTA) International Dr. Lu leads the Laboratory for Experimental Mechanics Research, where experimental techniques are developed and applied to structural and electronic system reliability. While specific grant details and student advising records are not listed, his publication record indicates active research supervision and project leadership in experimental mechanics and microelectronics packaging. His work supports innovation in durable, high-performance electronic systems.

Abdolreza Abhariمشاهده پروفایل

استاد

• Artificial Intelligence
• Agent Systems
• Data Science
• +۵ مورد دیگر

Dr. Abdolreza Abhari is a Professor and Undergraduate Program Director in the Department of Computer Science at Toronto Metropolitan University. His research focuses on Artificial Intelligence, Agent Systems, Data Science, Distributed Systems, and Network Simulation. He holds a PhD from Carleton University (2003) and has received notable grants including CFI, NSERC, and OGS funding. His professional roles include serving as Conference/Track Chair for Spring Simulation Conference and as an IEEE Senior Member. He leads the Data Science research group and the Distributed and Multimedia Processing (DSMP) lab. Awards include the Nortel Network Scholarship for Ph.D. studies and industry grants from AutoDesk Inc. His work spans simulation modeling, edge computing, and social network analysis. Education: PhD in Computer Science, Carleton University (2003). Research Interests: Dr. Abhari’s expertise includes developing simulation frameworks for distributed systems, optimizing network architectures, and applying machine learning to social networks. His work integrates theoretical models with practical applications in smart cities, video streaming, and fault-tolerant systems. Awards & Grants: Recipient of major research grants (CFI, NSERC) and industry partnerships. His contributions to simulation methodologies have been recognized through scholarships and collaborative projects. Advising & Labs: Leads the Data Science group and DSMP lab, focusing on edge computing, video analytics, and scalable data management. His research addresses real-world challenges in distributed systems and AI-driven solutions.

Nasser Lashgarian Azadمشاهده پروفایل

استاد

• Automation and Control Systems
• Autonomous Vehicles
• Reinforcement Learning
• +۳ مورد دیگر

Nasser Lashgarian Azad is a Professor and Associate Chair (Graduate Studies) in the field of Automation and Control Systems at the University of Waterloo. His research focuses on advanced control strategies for autonomous and connected vehicles, including reinforcement learning, model predictive control (MPC), and cybersecurity. He specializes in vehicle dynamics, energy management systems, and decision-making algorithms for complex scenarios such as platooning, collision avoidance, and shared spaces. His work spans interdisciplinary applications in automotive, aerospace, and robotics, with contributions to hybrid electric vehicle optimization, cyber-physical systems security, and real-time trajectory control. Key areas of interest include autonomous navigation, resilient control systems, and the integration of AI with traditional control methodologies. Dr. Azad has published extensively on topics such as deep reinforcement learning for autonomous driving, secure vehicle platooning, and adaptive cruise control. His research emphasizes practical implementation, with experimental validations in domains like electric vehicle energy management, lane-changing algorithms, and roundabout navigation. He is actively involved in advancing control systems for connected and automated vehicles, addressing challenges in safety, efficiency, and scalability. His contributions have been applied to both ground vehicles and space systems, reflecting a broad expertise in dynamic systems and intelligent control.

• Remote Sensing
• Machine Learning
• Satellite Image Processing
• +۴ مورد دیگر

Masoud Mahdianpari is a Cross-appointed Professor between Memorial University's Department of Electrical and Computer Engineering and C-CORE, where he serves as Remote Sensing Technical Lead. He holds a PhD in Electrical Engineering from Memorial University, complemented by graduate degrees from the University of Tehran. His research specializes in advanced remote sensing techniques, including PolSAR data processing, multi-sensor fusion, and deep learning applications for environmental monitoring. Primary research domains encompass wetland ecosystem mapping, agricultural surveillance, and Arctic landscape analysis using satellite data and geo big data platforms. Mahdianpari's publications demonstrate a consistent focus on machine learning applications in earth observation, with recent work emphasizing wetland biomass quantification, methane emission tracking, and wildfire prediction using convolutional neural networks and transformer architectures. T. David Collett Best Industry Paper Award (2020) ESRI Map Award for 'Big Data for Big Country' (2020) Microsoft AI for Earth Grant (2018-2019) Outstanding Reviewer Award for Remote Sensing of Environment (2018) RDC Ocean Industries Student Research Award (2016-2019) PhD Comprehensive Examination Pass with Distinction (2017) He coordinates research initiatives at C-CORE's remote sensing division and mentors graduate students in the GeoBigData Laboratory. Current projects include developing novel CNN-Transformer hybrid models for wetland classification and satellite-based methane monitoring systems.

Chul Min Yeumمشاهده پروفایل

دانشیار

• Smart Infrastructure
• Computer Vision
• Mixed/Augmented Reality
• +۶ مورد دیگر

Chul Min Yeum is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Waterloo. He leads the CViSS Lab, focusing on smart infrastructure through interdisciplinary research in computer vision, sensing technologies, and artificial intelligence. His work emphasizes developing technologies for structural health monitoring, nondestructive testing, and automated inspection systems. Education: PhD from Purdue University (2016), MS/BS from KAIST (2010/2008) Research interests include smart structures, mixed/augmented reality, robotics, machine learning, and big data analytics applied to civil infrastructure. His recent work explores real-time defect quantification using RGB-D datasets, gaze-based human-robot interaction systems, and machine learning for seismic vulnerability assessments. He teaches courses like AE 121, CIVE 497, and CIVE 700, focusing on computational methods and advanced structural engineering topics. Current research funding supports collaborations with government and industry partners to advance smart infrastructure solutions. Chul Min Yeum holds Sole-Supervisory Privilege Status (SSPS) and is actively mentoring graduate students in areas like computer vision for structural assessment, automated inspection systems, and resilient infrastructure design.

• Robotics
• Control Systems
• Vision-Based Navigation
• +۳ مورد دیگر

Melissa Greeff is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University. She is a faculty affiliate with the Vector Institute for Artificial Intelligence and works with the Robora Lab , focusing on robotics and control systems. Research Interests : Aerial robots, vision-based navigation, safe learning-based control systems. Education : BASc in Engineering Science, PhD from the University of Toronto. Research Focus : Her work integrates robotics, control theory, and machine learning to enable safe, efficient UAV operations in complex environments, including maritime applications and GPS-denied navigation. Key methodologies involve differential flatness and model predictive control (MPC) enhanced by learning-based approaches. Publications : Recent articles emphasize vision-based navigation, multirotor control under disturbances, and benchmarking safe learning systems. Topics span UAVs in infrastructure assessment, cooperative control, and embedded predictive control solutions. Labs & Teams : Collaborates with the Robora Lab and Ingenuity Labs Research Institute on robotics and AI research.

Chyngyz Erkinbaevمشاهده پروفایل

دانشیار

• Smart Sensing Technologies
• Food Process Engineering
• Spectral Imaging
• +۹ مورد دیگر

Chyngyz Erkinbaev is an Associate Professor in the Department of Biosystems Engineering at the Price Faculty of Engineering, University of Manitoba. His research is centered on advancing food processing through smart modular sensing technologies, integrating engineering, digital, physical, and biological domains to enhance agri-food systems. Research Interests: Dr. Erkinbaev specializes in smart sensing technologies, food process engineering, spectral imaging, chemical and microstructural mapping, and advanced non-destructive analytical techniques. His expertise includes spectroscopy, hyperspectral imaging, chemometrics, computer vision, and adaptive machine learning, with a strong emphasis on real-time food quality monitoring. These tools are applied across plant-based foods, canola, wheat, oats, and distillers' spent grains. Publication Trends: His recent publications (2024–2014) reflect a consistent focus on non-invasive, imaging-based methods for food quality and safety. Key themes include real-time monitoring, deep learning integration, microstructural analysis via X-ray tomography, and portable sensing devices. These works span journals in food science, engineering, and smart agricultural technology, demonstrating interdisciplinary impact. Scientific Contributions: Although no formal awards are listed, his body of work shows significant contributions to food engineering and sensing technologies. His research enables safer, more efficient food processing through innovation in detection, grading, and quality prediction systems. Advising and Grants: Currently, Professor Erkinbaev does not have graduate student opportunities available. There is no mention of specific grants or funded projects in the provided text. Labs and Research Teams: While specific lab names are not provided, his collaborative publications suggest active engagement with research teams focused on hyperspectral imaging, chemometrics, and food microstructure analysis, likely involving partnerships with institutions such as KU Leuven and other international groups.

Oscar De Silvaمشاهده پروفایل

استادیار

• Robotics
• Control Systems
• Sensor Design
• +۶ مورد دیگر

Dr. Oscar De Silva is an Assistant Professor in the Department of Mechanical Engineering at Memorial University of Newfoundland. He holds a B.Sc. from the University of Moratuwa, Sri Lanka, and a PhD from Memorial University. His expertise spans instrumentation, controls, mechatronics, and robotics, with a focus on sensor design, state estimation, and navigation systems. Dr. De Silva's research interests include state estimation, control systems, nonlinear dynamics, navigation systems, sensor design, localization, mapping, and intelligent prosthetics. He has contributed to projects such as ice detection systems for marine safety and computer vision for robotics applications. Before his current role, he worked as a research fellow at the American Bureau of Shipping-Harsh Environment Technology Centre and taught at Memorial University as a sessional instructor. His work emphasizes practical applications in autonomous systems, industrial inspection, and environmental monitoring. Notable achievements include the IMechE UK Award for Outstanding Achievement and a Gold Medal in Mechanical Engineering. His research trends focus on integrating AI, LiDAR, and radar technologies for navigation and safety systems in robotics and marine environments. Awards: IMechE UK Award for Outstanding Achievement Gold Medal in Mechanical Engineering (University of Moratuwa) Fellow of Graduate Studies (Memorial University) Advising and Grants: While no specific student names are listed, his role as an Assistant Professor involves academic supervision. His grants and collaborations likely align with his research in robotics and sensor systems, though specific details are not provided here. Labs: He is associated with the Intelligent Systems Lab at Memorial University and previously contributed to the ABS HETC. His work often involves multidisciplinary teams focused on real-world engineering challenges.

Pierre Payeurمشاهده پروفایل

استاد

• Machine Vision
• 3D Sensing and Modeling
• Human Motion Capture
• +۴ مورد دیگر

Pierre Payeur is a Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a PhD from Université Laval (1999) and has been a faculty member since then. He leads the SMART Research Group and co-founded the VIVA Research Laboratory. His roles include Graduate Programs Coordinator for the Ottawa-Carleton Institute for Electrical and Computer Engineering and faculty advisor for the ARISE student robotics club. Education: Ph.D. in Electrical Engineering from Université Laval (1999) Research interests focus on machine vision, 3D sensing, human motion capture, tactile sensing, robotic navigation, and computational intelligence. His work integrates industry collaboration and has been funded by major granting agencies. Recent articles explore domain adaptation in human action recognition, multi-agent robotics, and soil temperature prediction using AI. His contributions include over 150 technical papers and supervision of 70+ researchers. Awards: Best Paper Awards at IROS 2020 and ISVC 2021 Advising and grants: Over 70 supervised researchers, grants from major agencies. Active in professional roles like co-chair of IEEE TC-28 and organizer of the ROSE conference series. Labs: SMART Group and VIVA Lab, focusing on robotics, sensing, and autonomous systems.

Gabriel Spadonمشاهده پروفایل

استادیار

• Spatiotemporal Analytics
• Time-Series Forecasting
• Complex Network Mining
• +۱۳ مورد دیگر

Gabriel Spadon is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the MAPS Lab (Modeling and Analytics on Predictive Systems), focusing on spatiotemporal analytics, network science, and machine learning. His work is part of the Big Data Analytics, AI & Machine Learning research cluster. Education: PhD in Computer Science and Computational Mathematics, University of São Paulo (USP), 2021 MSc in Computer Science and Computational Mathematics, University of São Paulo (USP), 2017 BSc in Computer Science, São Paulo State University (UNESP), 2015 Research Interests: Gabriel Spadon’s research lies at the intersection of data science, machine learning, and geoinformatics . He specializes in spatiotemporal forecasting , complex network mining , and trajectory modeling , particularly in maritime and urban environments. His work integrates physics-informed AI, graph-based learning, and environmental data to improve predictive accuracy and decision support. Key domains include vessel movement prediction, environmental monitoring, and urban intelligence. Recent Publication Trends: His latest publications (2024–2025) focus on maritime mobility , using AIS data and physics-informed neural networks (PINNs) for vessel trajectory forecasting. He applies deep learning, clustering, and probabilistic fusion to model complex, multi-modal movement patterns. Other themes include fishing activity detection, port network analysis, and AI for public health and climate matching. Scientific Awards: Advising and Grants: Dr. Spadon supervises multiple graduate students and research assistants in the MAPS Lab, working on mobility data mining, physics-informed AI, and geospatial analytics. His research is supported by major grants from the Ocean Frontier Institute (OFI) , Canadian Space Agency (CSA) , Mitacs , and IMT Atlantique . He leads projects such as smartWhales , AISViz , and Physics-Informed Mobility Forecasting , involving international collaborations with institutions in Brazil and France. Labs and Teams: He is the Director of the MAPS Lab , which develops cutting-edge computational methods for predictive systems. The lab collaborates with the smartWhales initiative (DHI, WSP, Fisheries and Oceans Canada), MERIDIAN , and the Institute for Big Data Analytics . His team includes PhD and MCS students working on AI, data mining, and signal processing for real-world ocean and urban challenges.

Xavier Maldagueمشاهده پروفایل

استاد

• Infrared Thermography
• Nondestructive Evaluation (NDE)
• Industrial Inspection Systems
• +۲ مورد دیگر

Xavier Maldague is a Full Professor in the Department of Electrical and Computer Engineering at Université Laval, Québec, Canada. With over 400 publications and 50+ graduate students mentored, he specializes in infrared thermography, nondestructive evaluation (NDE), and industrial inspection systems. His research spans aerospace defect detection, corrosion analysis, and multi-modal imaging techniques. Education Ph.D. (1989), Université Laval - Infrared thermography and image processing M.Sc.A. (1984), Université Laval - 3D image acquisition B.Sc.A. (1982), Université Laval - Electrical Engineering His work focuses on advancing NDE through infrared thermography, terahertz imaging, and deep learning. Current projects include drone-based thermography, quantum energy truncation tomography, and thermal biomarker optimization for cancer detection. Articles highlight fusion of ultrasonic thermography with AI-driven defect analysis in composites and infrastructure. Awards Teaching Star, Faculty of Science and Engineering (multiple years) Honorary Fellow, Indian Society of Nondestructive Testing Honorary Doctorate, University of Antwerp (2019) Fellow, American Society for NDT Active grants include the NSERC CREATE NDT program and international collaborations with Belgium, Colombia, and Singapore. He leads the CeRVIM Robotics, Vision and Machine Intelligence research center.

Hakim Bendadaمشاهده پروفایل

استاد

• Infrared Thermography
• Non-destructive Testing
• Thermal Imaging
• +۶ مورد دیگر

Hakim Bendada is a Full Professor at Laval University since 2014, with prior academic roles including Associate Professor (2010-2014) and Assistant Professor (2005-2010) at the same institution. Since 2003, he has concurrently held an Associate Professor position at the University of Quebec in Abitibi-Témiscamingue. His research focuses on infrared thermography , non-destructive testing (NDT) , high-resolution thermal imaging , and opto-thermal inspection of industrial processes, with applications spanning aerospace, civil engineering, and cultural heritage preservation. PhD in Stimulated Infrared Tomography from National Polytechnic Institute of Lorraine (1991-1995) Director of Aerospace Group (GAUL), Laval University (2006-2008) Research Officer at National Research Council of Canada (1997-2005) His research grants include multiple NSERC Discovery and Tools & Instruments awards for advancing infrared vision systems. He has contributed to 30+ peer-reviewed publications and 20+ conference proceedings , emphasizing 3D NDT methods and thermal diagnostics. Bendada received the Andronicos Kantsios Award (2007) and Professeur étoile honors (2007, 2009, 2013). He co-edits journals like Quantitative Infrared Thermography Journal and organizes international conferences including QIRT and AITA .

Hyock Ju Kwonمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Manufacturing Engineering
• +۵ مورد دیگر

Hyock Ju Kwon is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo . Active in research and teaching, he focuses on integrating Artificial Intelligence (AI) with manufacturing practices and biomedical ultrasound techniques . Doctorate in Mechanical Engineering, University of Alberta (2007) Master's in Mechanical Engineering, University of Alberta (2003) Master's in Mechanical Engineering, KAIST (1988) Bachelor's in Mechanical and Design Engineering, Seoul National University (1985) His research spans AI and machine learning , with applications in manufacturing (robotic assembly, NDT, predictive maintenance) and biomedical fields (therapeutic ultrasound, tactile neuroimaging). He leads the AI for Manufacturing Laboratory , emphasizing digital twin and multi-physics modeling . The 15 most recent articles reflect trends in AI-driven manufacturing , ultrasound diagnostic/therapeutic systems , and robotic path planning . Keywords include machine learning , medical imaging , and fluid dynamics , with subfields like predictive maintenance , nanoparticle-enhanced therapy , and topological navigation . As a Sole-Supervisory Privilege Status (SSPS) holder, he supervises graduate students. Teaching includes courses like ME 212 - Dynamics and ME 526 - Fatigue and Fracture Analysis . The AI for Manufacturing Laboratory develops cutting-edge technologies for industrial AI , collaborating with Korean municipal governments on large-scale projects. Areas include robotic assembly , non-destructive testing , and machine condition monitoring .

Wassim El Ahmarمشاهده پروفایل

استاد

• Computer Vision
• Multimodal Large Language Models (LLMs)
• Machine Learning
• +۴ مورد دیگر

Wassim El Ahmar is a Part-Time Professor at the University of Ottawa , affiliated with the Faculty of Engineering and the School of Electrical Engineering and Computer Science . His research focuses on advancing computer vision and multimodal large language models (LLMs) to create robust perception systems that integrate thermal, RGB, and depth imagery for applications in industrial quality inspection, autonomous navigation, and environmental monitoring. Education : Doctorate of Philosophy in Electrical and Computer Engineering (2023) Master of Applied Science in Electrical and Computer Engineering (2019) Bachelor of Engineering in Computer Engineering (2017) El Ahmar’s work emphasizes scalable frameworks for computer vision, bridging theoretical innovation with practical applications. He has led projects optimizing machine learning for edge computing and enhancing autonomous systems. As an educator, he teaches SEG3101 - Software Requirements Analysis and SEG4300 - Modern Software Engineering Practices , focusing on curriculum development aligned with industry trends. His research interests include: Robust object detection and tracking Thermal-RGB-Depth data fusion Contextual interpretation via multimodal LLMs Real-time analysis for dynamic environments Applications in autonomous driving and industrial inspection

Denis Laurendeauمشاهده پروفایل

استاد

• Artificial Vision
• Robotics
• Telerobotics
• +۳ مورد دیگر

Denis Laurendeau is a retired Professor from the Department of Electrical Engineering at the University of Laval. His research focuses on artificial vision applied to robotics, telerobotics, and biomedical engineering. He holds a Ph.D. in Electrical Engineering (1986) and a post-doctorate in Robotics (1986). Laurendeau has held leadership roles, including Director of the Computer Vision and Systems Laboratory (since 2002) and Head of Graduate Programs committees (1993–1996). He teaches courses in Java software applications and design at both undergraduate and graduate levels. His work integrates advanced imaging techniques with robotics for applications in healthcare, environmental monitoring, and mineral exploration. Over 254 publications and collaborative projects highlight his contributions to 3D reconstruction, medical device accuracy, and AI-driven diagnostics. Laurendeau’s research emphasizes real-world applications, such as electromagnetic tracking in brachytherapy and AI tools for pain assessment in animals. He pioneered methods for surface normal estimation and environmental data extraction using camera-collar technology on wildlife. His lab collaborations span robotics, medical imaging, and geology, reflecting a multidisciplinary approach to solving complex technical challenges. Despite retirement, his legacy continues through influential publications and infrastructure inspection methodologies.