جستجوی استاد راهنمای دانشگاهی

• Object Recognition
• Computer Vision
• Artificial Intelligence
• +۷ مورد دیگر

Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, with a Faculty Affiliate role at the Vector Institute for Artificial Intelligence since 2018. He earned a B.A.Sc in Systems Design Engineering from the University of Waterloo (1983), followed by an M.S. (1988) and Ph.D. (1991) in Computer Science from the University of Maryland. Education : B.A.Sc, Systems Design Engineering, University of Waterloo, 1983 M.S., Computer Science, University of Maryland, 1988 Ph.D., Computer Science, University of Maryland, 1991 His research focuses on generic object recognition, emphasizing shape representation, image abstraction, and feature matching. Dickinson's work bridges human and computer vision, exploring medial axis transforms, shock graphs, and many-to-many feature correspondence for applications in content-based retrieval, robotics, and biomedical imaging. Recent publications highlight geometric disentanglement, symmetric part detection, and contour analysis via deep learning. Scientific Contributions include advancing skeletal abstraction, deformable models, and saliency maps. Collaborative efforts span the University of Maryland, Rutgers, MIT, and the University of Waterloo. He has authored over 100 papers and edited interdisciplinary works like Shape Perception in Human and Computer Vision (Springer, 2013).

Stavros Avramidisمشاهده پروفایل

استاد

• Wood-water relations
• Sorption thermodynamics
• Fluid flow in wood
• +۸ مورد دیگر

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

Calvin Kuoمشاهده پروفایل

استادیار

• Biomechanics
• Wearable Sensors
• Musculoskeletal Modeling
• +۴ مورد دیگر

Calvin Kuo is an Assistant Professor at the School of Biomedical Engineering , University of British Columbia (UBC), and leads the Human Motion Biomechanics Lab (HuMBL) . His work bridges biomechanical engineering and wearable sensing technologies to address clinical challenges in motion analysis and traumatic brain injury. Research Themes: Biomechanical engineering, Medical devices, Wearable sensing Key Collaborations: Affiliated with UBC's Centre for Aging and SMART (Sensorimotor Assessment & Rehabilitation Training) cluster HuMBL focuses on sensorimotor neurophysiology, musculoskeletal modeling, and wearable sensor validation , with applications in concussion biomechanics and cardiac arrest detection. His lab employs Bayesian filtering and Kalman algorithms to improve high-rate motion measurement accuracy. The lab's recent publications analyze head kinematics, soft tissue artifacts, and real-world locomotion using inertial measurement units (IMUs). This work informs wearable device design, particularly for fall prevention in older adults and sports injury monitoring. As a UBC faculty member, Dr. Kuo supervises MASc and PhD students in interdisciplinary research, emphasizing diverse academic backgrounds. His lab partners with clinical and engineering teams to translate findings into healthcare solutions. Laboratory Affiliations: HuMBL contributes to UBC's Human Interfacing Devices research core, advancing wearable systems for balance deficits and musculoskeletal modeling. The lab actively recruits students for projects involving sensor fusion and biomechanical simulations.

Louis Fradetteمشاهده پروفایل

استاد

• Chemical engineering
• Separation processes
• Fluid mechanics
• +۵ مورد دیگر

Louis Fradette is a Full Professor at the Department of Chemical Engineering , Polytechnique Montréal. He holds a Ph.D. from Polytechnique Montréal and INP Lorraine. Education : B.Sc., M.Sc.A. from Laval University; Ph.D. from Polytechnique Montréal and INP Lorraine. Research Interests focus on emulsification , CO2 capture processes , and agitation/mixing systems for complex fluids and multiphase systems. He leads the Valorisation Carbone Québec project, demonstrating industrial-scale CO2 utilization technologies. Publication Trends emphasize Pickering emulsions for rare earth separation, CO2 capture using hydrates/absorption heat pumps, and solid-liquid mixing with advanced modeling techniques like CFD-DEM. Scientific Awards : Co-holder of the NSERC-Total Chair (2011–2016); 6 invention patents. Advising includes 2 postdoctoral researchers, 1 Ph.D., 1 research Master’s, 3 professional Master’s students, and 14 completed theses. He contributes to industrial ecology projects and green steel research.

• Granular Media
• Solid mechanics
• Constitutive and multiscale modeling
• +۶ مورد دیگر

Émilien Azéma is a Professor at the University of Montpellier since 2023, affiliated with the Department of Mechanics in the Faculty of Sciences and the Mechanics and Civil Engineering Laboratory (LMGC). Since 2024, he also holds an Associate Professor position at Polytechnique Montréal in the Department of Civil, Geological and Mining Engineering. Primary Institution: University of Montpellier, France Secondary Appointment: Polytechnique Montréal, Canada Research Recognition: French University Institute (IUF) Research Chair (2020-2025) Dr. Azéma received his complete education at the University of Montpellier, earning degrees in Mathematics, Solid Mechanics, and completing his Doctorate and Habilitation (HDR) in Mechanics. His doctoral research focused on railway ballast behavior using Discrete Element Method (DEM) simulations, supported by a joint CNRS and SNCF grant. Licence/Maîtrise (Master 1) in Mathematics DEA (Master 2) in Solid Mechanics Doctorate in Mechanics Habilitation (HDR) in Mechanics His research program centers on the micromechanics of granular systems with realistic microstructure. Using innovative Discrete Element Method (DEM) approaches, he systematically investigates the nonlinear effects of particle shape characteristics (angularity, non-convexity, elongation) and size polydispersity on granular rheology across flow regimes from quasi-static to rapid. His work extends to evolving granular systems where particles can fragment or undergo large deformations, with applications to geomechanical materials, industrial powders, and extraterrestrial granular systems. An analysis of his recent publications (2023-2025) reveals a strong emphasis on particle shape effects, cohesive strength mechanisms, and polydisperse system behavior. His research bridges fundamental granular physics with practical applications in railway engineering, planetary science (particularly asteroid regolith modeling), and geomechanics, appearing in high-impact journals including Physical Review E, Granular Matter, and Icarus. Dr. Azéma's significant research contributions have been recognized through: A prestigious Research Chair from the French University Institute (IUF) as Junior Member (2020-2025) An extensive publication record with 81 publications across multiple high-impact journals He teaches core courses in solid mechanics, numerical computation, finite element method, discrete approaches, and rheology of granular media. His administrative responsibilities include: International affairs coordinator at the Faculty of Sciences (since 2018) Responsible for the first year curriculum of the Mechanical Engineering Master's program (since 2021) His primary research laboratory is the Mechanics and Civil Engineering Laboratory (LMGC) at the University of Montpellier, where he conducts his investigations into granular media and solid mechanics phenomena, with significant collaborations extending to Polytechnique Montréal and international institutions.

Douglas Woolfordمشاهده پروفایل

استاد

• Data Science
• Forest and Wildland Fire Science
• Forest Fire Management
• +۳ مورد دیگر

Douglas Woolford is a Professor of Environmetrics at the Department of Statistical and Actuarial Sciences, University of Western Ontario. His research focuses on data science, forest fire prediction, and stochastic modeling. Education: Ph.D., University of Western Ontario, 2007 Woolford's work bridges statistical methodology with practical applications in wildland fire management, risk assessment, and mathematical education. He develops predictive models for fire occurrence and suppression effectiveness, collaborating with forestry and environmental agencies. His 15 most recent publications (2025-2021) span topics like rare event prediction, fire weather interpolation methods, and industrial protocol evaluation. Articles appear in journals including Canadian Journal of Statistics , International Journal of Wildland Fire , and Environmetrics . Woolford supervises postdoctoral and graduate researchers, including Shuai You (Ph.D.), Nathan Phelps, Zixuan Yang, and others. His lab contributes to wildfire risk modeling and policy analysis in Canada.

• Process control/optimization
• Energy systems
• Sustainable design
• +۵ مورد دیگر

Jake Nease is an Associate Professor in the Department of Chemical Engineering at McMaster University's Faculty of Engineering. A McMaster graduate himself, he has built his academic career at this institution with a strong focus on teaching excellence and innovative pedagogy in chemical engineering education. Dr. Nease's research interests span energy systems, sustainable design, and environmental engineering, with particular expertise in process control and optimization. His work demonstrates a strong commitment to addressing climate change challenges through sustainable energy solutions, focusing on the efficient use of fossil fuels en route to carbonless power generation. His research portfolio reveals a consistent theme of integrating advanced optimization techniques with sustainable energy systems, particularly in the areas of solid oxide fuel cells, CO2 capture technologies, and zero-emissions power generation. His scholarly output shows a clear evolution from fundamental process control and optimization research toward increasingly applied sustainable energy systems. The most recent publications demonstrate his expanding interests into biomedical applications and educational research, while maintaining his core focus on energy systems optimization. His work bridges theoretical process systems engineering with practical environmental applications, particularly in carbon management technologies. Dr. Nease has taught a wide range of courses including Process Control/Optimization, Numerical Methods, Big Data Methods, Chemical Process Design, Engineering Economics, and Biomedical Control Systems. His teaching portfolio reflects both his technical expertise in process systems engineering and his commitment to preparing students for emerging challenges in sustainable engineering. His research collaborations span multiple disciplines, with significant work alongside colleagues in chemical engineering, environmental science, and biomedical engineering. While specific grant information isn't detailed in the available materials, his publication record suggests active research funding supporting his work in sustainable energy systems and process optimization.

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

Hui Guo is an Adjunct Assistant Professor in the Department of Civil Engineering. Their research intersects computer vision, machine learning, and image processing, with a focus on advanced restoration and super-resolution techniques. Recent publications highlight contributions to image super-resolution challenges (NTIRE 2024/2025), vision transformers (QID), diffusion models for video restoration, and continual learning frameworks (PCL). Work spans OCR-free visual document analysis, adversarial degradation modeling (AND), and parametric image restoration. Contact: guoh15

Dimitrios Konstantinidisمشاهده پروفایل

استادیار مدعو

• Earthquake Engineering
• Engineering Mechanics
• Base Isolation and Structural Control
• +۲ مورد دیگر

Dimitrios Konstantinidis is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University , specializing in earthquake engineering and structural mechanics. Primary research: base isolation, nonstructural component dynamics, seismic performance of critical facilities Methodologies: experimental testing, analytical modeling, numerical simulation Teaching: Structural Dynamics, Earthquake Engineering, Engineering Mechanics His work focuses on reducing earthquake-induced economic losses and enhancing community resilience through improved design codes. Recent publications address fiber-reinforced elastomeric isolators, seismic qualification of nuclear components, and nonstructural equipment behavior in isolated buildings. Key collaborations include studies on seismic fluid dampers, low-cost isolation systems, and bridge bearing performance. His research has appeared in journals like Earthquake Engineering and Structural Dynamics , Journal of Structural Engineering , and Engineering Structures .

• Neonatal invasive and non-invasive ventilation
• Hypoxic-ischemic encephalopathy
• Heart rate variability in preterm infants
• +۳ مورد دیگر

Guilherme Sant'Anna, MD, PhD is a Senior Scientist at The Research Institute of the McGill University Health Centre (RI-MUHC) Professor in the Department of Pediatrics, Faculty of Medicine and Health Sciences, McGill University Division of Neonatology at MUHC His research focuses on Optimizing respiratory assistance for preterm infants through the EXPERT Project Pathophysiology and management of asphyxiated neonates Automated extubation readiness prediction (APEX study) Mechanisms of non-invasive respiratory therapies International collaborations on mild HIE (PRIME study) and neurophysiological maturation (NEMO project) Key trends in his 15 most recent publications reveal Heavy emphasis on wireless monitoring systems for NICU Development of AI/ML tools for extubation prediction Multicenter clinical trials on HIE and PDA management Focus on translating engineering solutions to neonatal care Integration of cardiorespiratory variability analysis Improving long-term neurodevelopmental outcomes

• visual perception
• color vision
• transparency
• +۸ مورد دیگر

Frederick Kingdom is a Senior Scientist at the McGill Vision Research unit within the Research Institute of the McGill University Health Centre (RI-MUHC), Montreal General Hospital site. He holds the academic rank of Professor in the Department of Ophthalmology and Visual Sciences at McGill University 's Faculty of Medicine and Health Sciences. His research focuses on bridging local feature detection (edges, bars, surface markings) with intermediate visual processing that forms contours, textures, and surfaces. Key domains include spatial vision , color vision , stereopsis , binocular vision , and transparency perception , combining psychophysical methods with natural scene image analysis and mathematical modeling . Recent publications explore stereoscopic depth adaptation , color-enhanced edge classification , co-circularity in texture , and binocular contrast adaptation . His work appears in journals like Vision Research and PLoS Computational Biology . Kingdom's lab maintains the Palamedes Toolbox for psychophysical data analysis. He investigates perceptual mechanisms in contexts ranging from 2D visual illusions to 3D environmental interpretation , with ongoing projects examining visual biases (e.g., falling objects) and contextual modulation in neural coding.

Sridhar Krishnanمشاهده پروفایل

استاد

• Adaptive Signal Representation and Analysis
• Biomedical Signal/Image Analysis
• Multimedia Information Security
• +۴ مورد دیگر

Sridhar Krishnan is a Professor and Associate Dean (Research, Development and External Partnerships) in the Faculty of Engineering and Architectural Science at Ryerson University, holding the Canada Research Chair in Biomedical Signal Analysis. He serves as Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST) and Affiliate Scientist at St. Michael's Hospital's Keenan Research Centre. He earned his B.E. in Electronics and Communication Engineering from Anna University (1993), and M.Sc./Ph.D. in Electrical and Computer Engineering from the University of Calgary (1996/1999). His research specializes in adaptive signal processing with applications across biomedicine, multimedia security, and biometrics. Key focus areas include gait analysis for neurological disorders, cardiac signal interpretation, assistive technologies, and secure biometric authentication systems. His work bridges engineering principles with clinical and security applications through innovative algorithm development. Publication analysis reveals consistent contributions to biomedical signal processing, particularly in movement disorders and cardiac diagnostics, while extending methodologies to multimedia security and behavioral biometrics. This interdisciplinary approach demonstrates strong translational impact from theoretical signal analysis to real-world healthcare and security solutions. Major recognitions include: Fellow of the Canadian Academy of Engineering (2014) IEEE Exemplary Service Award (2014) Innovate Calgary Achievement Award (2013) Sarwan Sahota Distinguished Scholar Award (2011) Engineers Canada Young Engineer Achievement (2007) Multiple best paper awards and chapter leadership honors He has mentored 10 postdocs, 10 PhD, and 39 Master's students while securing significant research funding. His 310+ publications, 12 invention disclosures, and US patent reflect substantial scholarly output. He actively serves on advisory boards for research institutes and innovation centers, driving translational research initiatives. Leading the Signal Analysis Research Laboratory, he directs teams developing assistive technologies and affective computing systems through collaborations with hospitals and industry partners, focusing on practical implementations of signal processing breakthroughs.

Nagi N. Mekhielمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Processing
• High Performance Memory Systems
• +۵ مورد دیگر

Nagi N. Mekhiel is a Professor in the Department of Electrical and Computer Engineering at Toronto Metropolitan University (formerly Ryerson University), where he teaches courses including Digital Systems, Microprocessors, Advanced Computer Architecture, and Parallel Computing. His office is located in the George Vari Engineering and Computing Centre at 245 Church St., Toronto. Dr. Mekhiel received his B.Sc. in Electrical Engineering from Assiut University, Egypt (1973), M.A.Sc. from University of Toronto (1981), and Ph.D. in Computer Engineering from McMaster University (1995). Prior to academia, he worked as a Biomedical Engineer at Toronto's Hospital for Sick Children (1981-1987), Senior Hardware Engineer at Definicon Systems Corporation (1987-1990), and Senior Member of Technical Staff at Yarc System Corporation (1996-1998). His research focuses on solving fundamental computer industry challenges, particularly the processor/memory speed gap and scalability of parallel processors. His work spans computer architecture, parallel processing, high-performance memory systems, VLSI, and performance evaluation. Mekhiel's research has been cited in patents by major tech companies including IBM, Google, Apple, Intel, Microsoft, and others. Analysis of his recent publications reveals a strong trajectory toward quantum computing applications, orbital data processing architectures, and memory system innovations for big data applications. His work consistently addresses the processor/memory speed gap through novel architectural solutions, with increasing emphasis on quantum information representation and parallel time computing models. Intel Xeon+FPGA system access via Intel's Hardware Accelerator Research Program Senior Member of IEEE Dr. Mekhiel has supervised numerous graduate students who have co-authored publications with him in areas including facial recognition systems, quantum computing implementations, and parallel processing architectures. His research has received industry support through patent licensing agreements with Intellectual Ventures US. He leads a research group focused on advanced computer architecture solutions, with current projects involving quantum bit encoding, reconfigurable memory systems, and orbital network architectures for scalable multi-core processing.

Matthew Reidمشاهده پروفایل

استاد

• Ultrafast Phenomena
• Terahertz Spectroscopy
• Nonlinear Optics
• +۲ مورد دیگر

Dr. Matthew Reid is a Professor in the Department of Physics at the University of Northern British Columbia within the Faculty of Science and Engineering. He holds a PhD in Electromagnetics from the University of Alberta and a BSc in Mathematics and Physics from UNBC. Professor, Department of Physics, UNBC PhD, Electrical and Computer Engineering, University of Alberta (2005) BSc, Mathematics and Physics, UNBC (1999) Research Expertise Dr. Reid specializes in ultrafast phenomena with a focus on terahertz spectroscopy and nonlinear optics . His work explores: Generation and detection of terahertz radiation Nonlinear optical response at semiconductor surfaces Industrial applications of THz imaging Polarization-sensitive THz systems Waveform modulation in doped semiconductors Anisotropic electron dynamics in condensed matter His recent publications demonstrate an emphasis on terahertz single-pixel imaging , polarization modulation techniques , and industrial applications in wood science and semiconductor analysis.

Bardia Yousefiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +۶ مورد دیگر

Bardia Yousefi is a Researcher affiliated with the Department of Electrical and Computer Engineering at Laval University, Quebec, Canada. He is part of the Computer Vision and Systems Laboratory (CVSL). His academic journey includes a Doctorate candidate at Laval University (2014–present), a PhD in Artificial Intelligence from University Malaya (2012–2014), an M.Sc. in Biomedical Engineering (2010–2012), and a B.Sc. in Electrical Engineering from Shahrood University of Technology (2002–2007). His research interests focus on Artificial Intelligence , Computer Vision , and Medical Imaging , with applications in thermal image analysis, biomedical CAD systems, and robotics. He has contributed to projects involving deep learning (CNNs), kernel methods, clustering, and hyperspectral imagery analysis. Teaching Experience : TA roles in Electronic Systems, Computer Architecture, Signal Processing, and Robotics at Shahrood University. Affiliations : IEEE member (2008–present), ARL member (2012–present), and member of REPARTI, MIVIM, and E4m (2014–present). He has secured notable grants including the TransMedTech (2018), FRQ-NT (2014–2017), and HIR (2012–2014). As a co-founder of PIV Company (2008), he bridges academic and industrial contributions.