جست‌وجوی استاد و پژوهشگر

Matthieu Schmittbuhlمشاهده پروفایل

استاد

• Dento-maxillofacial imaging
• Medical imaging
• Craniofacial imaging
• +۳ مورد دیگر

Matthieu Schmittbuhl is a Full Professor at the University of Montreal, affiliated with the Faculty of Dentistry's Department of Stomatology and the Department of Radiology, Radiation Oncology and Nuclear Medicine. He serves as Head of the Dentomaxillofacial Imaging Clinical Service and Department of Stomatology at the CHUM hospital. He holds a DMD from Strasbourg University, specialized training in maxillofacial imaging from Paris Descartes University, and a Master's/PhD in medical imaging. His research focuses on dento-maxillofacial imaging, cone beam CT applications, and phenotyping rare diseases, conducted within the Imaging and Biomedical Engineering Axis at CRCHUM. He co-directs the Advanced Dental and Craniofacial Imaging Lab and coordinates Montreal University's Cone Beam CT certification program. Research interests include craniofacial imaging, oral pathology, 3D modeling, and diagnostic tool development for rare diseases. Over 12 peer-reviewed articles highlight his work in imaging techniques, genetic disorders (e.g., Ectodermal Dysplasia, FAM20A mutation syndromes), and forensic handwriting analysis. His work spans interdisciplinary collaborations in medical imaging, genetics, and clinical dentistry. Recent studies explore maxillary expansion effects on skeletal asymmetry in juvenile arthritis patients and low-dose CBCT protocols for canine localization. He has supervised three Master's theses (2023–2024). A 2012–2013 FRQS-funded project supported his recruitment as a researcher in dental health networks. Laboratory affiliations include the CRCHUM's Imaging and Engineering Axis, fostering engineering-driven healthcare solutions. His expertise bridges clinical imaging, rare disease research, and educational leadership in advanced diagnostic techniques.

• Computer Vision
• Remote Sensing Systems
• Video Sports Analytics
• +۷ مورد دیگر

David A. Clausi is a Professor and University Research Chair at the University of Waterloo in the Department of Systems Design Engineering, Faculty of Engineering, with a distinguished career spanning computer vision, image processing, and pattern recognition. He previously served as Associate Dean - Research and External Partnerships (2019-2024) and leads the Vision and Image Processing (VIP) Research Group, whose work bridges academic research with commercial applications including the spinout company CREZ. His academic foundation was built entirely at the University of Waterloo: Doctorate in Systems Design Engineering (1996) Master's in Systems Design Engineering (1992) Bachelor's in Systems Design Engineering (1990) Professor Clausi's research pioneers AI-driven remote sensing for Arctic sea ice monitoring and video sports analytics in baseball/ice hockey. His group develops cutting-edge algorithms for sea ice classification, player tracking, and hyperspectral imaging, with strong emphasis on uncertainty quantification and weakly supervised learning techniques that solve real-world challenges in environmental monitoring and sports technology. Analysis of his 2023-2025 publications reveals dominant trends in sea ice analysis using SAR satellite imagery (particularly AI4Arctic Challenge datasets) and sports video analytics, with growing integration of Bayesian methods and transformer networks for enhanced accuracy in polar regions and athletic performance analysis. His exceptional contributions are recognized through: University Research Chair appointment (2024-2031) Triple Fellowship status (CAE, EIC, and Asia-Pacific AIA) CIPPRS Lifetime Achievement Award (2010) Five-time Outstanding Performance Award recipient Stanford University "Top 2% Scientist" designation As an active educator teaching SYDE 575 (Image Processing) and SYDE 121 (Digital Computation), he mentors graduate students under Sole-Supervisory Privilege Status. His research receives substantial grant support from federal agencies and industry partners, particularly for Arctic monitoring initiatives and sports technology commercialization through the VIP Research Group. The VIP Research Group maintains strategic partnerships with government agencies like the Canadian Ice Service and sports analytics firms, driving innovation in AI applications for climate resilience and athletic performance through cross-disciplinary collaboration between engineering, computer science, and domain specialists.

Léo Belzileمشاهده پروفایل

دانشیار

• Extreme values
• Likelihood-based inference
• Hierarchical modelling
• +۳ مورد دیگر

Léo Belzile is an Associate Professor in the Department of Decision Sciences at HEC Montréal. He specializes in extreme value analysis and statistical modeling, with affiliations to the Centre de recherches mathématiques and IVADO. His current research focuses on statistical methodologies for extreme events, hierarchical modeling, and software development. He is on sabbatical at the University of Lausanne's Expertise Center for Climate Extremes from August to December 2025. Education: M.Sc. in Statistics from McGill University and Ph.D. in Mathematics from École polytechnique fédérale de Lausanne (EPFL) Research interests include extreme value theory applications in environmental and demographic contexts, statistical software development, and likelihood-based inference techniques. Recent work explores climate extremes modeling and longevity studies. His teaching includes Bayesian modeling and advanced statistical methods courses. Over the past five years, he has supervised 26 student projects covering diverse topics like flood risk analysis, predictive modeling for pharmaceutical demand, and sports performance analytics. Notable projects include collaborations with Pratt & Whitney and Mantra Pharma. His work emphasizes interdisciplinary applications of statistical methods in business intelligence and data science.

Laurent Charlinمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +۳ مورد دیگر

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

Marc Fredetteمشاهده پروفایل

استاد

• Analysis of longitudinal data
• Prediction
• Biostatistics
• +۷ مورد دیگر

Marc Fredette is a Professor in the Department of Decision Sciences at HEC Montréal. His roles include academic supervision of the Master of Science (MSc) - User Experience in a Business Context and the Short Graduate Program in User Experience. He is also a board member of Tech3Lab, a member of CIRRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation), and GERAD (Group for Research in Decision Analysis). Education: M.Sc. in Statistics from Université de Montréal and Ph.D. in Statistics from the University of Waterloo. Research interests focus on longitudinal data analysis, biostatistics, Bayesian statistics, and random effects models. His work also explores human factors, UX design, and neuroIS applications, including studies on pedestrian safety, multitasking with IT systems, gamification, and neurophysiological correlates of collaborative tasks. Recent publications emphasize IT-related multitasking effects, gamification in workplaces, and neurophysiological studies of collaborative tasks. He has been recognized for contributions to UX evaluation methodologies and neuroIS research. Supervision includes 3 PhD dissertations and 7 MSc theses, along with 18 supervised projects on data analysis and business applications. His teaching includes courses on database analysis and project-based data analysis. Labs/Teams: Active in Tech3Lab, CIRRELT, and GERAD, focusing on interdisciplinary research in decision sciences and user experience innovation.

Chun Wangمشاهده پروفایل

استاد

• Sustainable Mobility Systems
• Eco-friendly Transportation Networks
• Computational Social Simulation
• +۴ مورد دیگر

Dr. Chun Wang is a Professor and Director of the Concordia Institute for Information Systems Engineering at Concordia University. His research focuses on Sustainable, Smart, and Socially-Oriented Mobility Systems, emphasizing eco-friendly solutions that enhance community well-being and reduce environmental impact. His work integrates computational social simulation, game theory, and multiagent systems to address challenges in electrification, decarbonization, and supply chain optimization. Key research areas include mobility systems design, scheduling algorithms, two-sided matching mechanisms, and e-supply chain management. His interdisciplinary team develops frameworks for ride-hailing systems, crowd-sourced delivery, and electric vehicle charging networks, with an emphasis on privacy preservation and uncertainty management. Recent publications (2021–2023) highlight advancements in auction-based scheduling, data-driven optimization for car-sharing, and privacy-preserving matching mechanisms. The lab’s work bridges theoretical models with practical applications in smart cities and sustainable infrastructure. Dr. Wang leads the Sustainable, Smart, and Socially-Oriented Mobility Systems Lab, fostering collaborations across engineering, computer science, and urban planning disciplines. His contributions span transportation networks, energy systems, and healthcare operations, with a focus on scalable and socially responsible solutions.

• Microwave Imaging
• Computational Electromagnetics
• Inverse Problems
• +۳ مورد دیگر

Dr. Ian Jeffrey is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Manitoba and leads research in the Electromagnetic Imaging Lab (EIL), focusing on wavefield imaging algorithms for agricultural and biomedical applications. His work integrates computational electromagnetics, optimization, and machine learning. Research interests include time-domain imaging, generative adversarial networks for calibration, and Bayesian machine learning for uncertainty quantification. Dr. Jeffrey's publications emphasize machine learning-enhanced microwave imaging, computational methods, and industrial applications like grain storage safety. He is actively seeking MSc and PhD students with C++ experience. No scientific awards were listed. Graduate opportunities focus on microwave imaging, computational electromagnetics, and GPU computing.

Boris Levitمشاهده پروفایل

استاد

• Non-parametric estimation
• Semi-parametric models
• Optimal design in non-parametric regression
• +۵ مورد دیگر

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

• Financial Risk Management
• Climate Science and Modeling
• Actuarial Science
• +۵ مورد دیگر

Samuel Gyamerah is a Postdoctoral Fellow at the University of Waterloo under the supervision of Ruodu Wang and Alex Schied, active from September 2023 to 2025. His research focuses on interdisciplinary applications of quantitative methods in finance, climate science, and actuarial science. Key areas include stochastic modeling of weather derivatives, systemic risk analysis in banking networks, and machine learning-driven predictive analytics in healthcare and agriculture. Education: Samuel holds prior academic affiliations with Kwame Nkrumah University of Science and Technology, as indicated by his 2014 research contributions. His work bridges theoretical advancements with practical solutions, such as optimizing crop yield forecasts and mitigating financial risks through innovative modeling techniques. Research Trends: His publications highlight a strong emphasis on climate-related financial instruments, geopolitical risk transmission, and the integration of advanced statistical methods (e.g., wavelet coherence, hybrid machine learning models) into diverse fields. Notable contributions include frameworks for longevity bond pricing under geopolitical volatility and state-space models for long-term bond valuation. Advising & Grants: While no formal advisees are listed, his postdoctoral work suggests active collaboration in grant-funded projects exploring systemic risk, green bonds, and agricultural resilience. His research has been applied to real-world challenges like Ghana’s National Health Insurance Scheme and SME digital transformation strategies. Labs/Teams: Engaged in collaborative projects at the intersection of finance, climate science, and data science, likely contributing to interdisciplinary research groups at the University of Waterloo.

Martin Lysyمشاهده پروفایل

دانشیار

• Statistics
• Quantitative Finance
• Biostatistics
• +۳ مورد دیگر

Martin Lysy is an Associate Professor and Director of the Statistical Consulting and Survey Research Unit at the University of Waterloo. His primary affiliation is with the Department of Statistics, and he is actively involved in the fields of Statistics and Quantitative Finance. His research focuses on developing advanced statistical methodologies with applications in biostatistics, environmental science, neuroscience, and machine learning. Lysy’s work often involves creating robust computational tools and software packages to address complex problems in these domains. Key research interests include Bayesian inference, spatial statistics, stochastic processes, and the statistical analysis of biological systems such as microrheology and neural spike trains. He has contributed to the development of R packages like SpatialGEV for spatial extremes and nichROVER for ecological niche modeling. His recent work emphasizes methodological innovations in capture-recapture methods for population estimation, environmental data analysis, and computational efficiency in Bayesian frameworks. Notable contributions include studies on the lockdown effects on air quality in Ontario, Canada, and interdisciplinary projects integrating machine learning with systems pharmacology. Lysy’s research bridges theoretical statistics with real-world applications, ensuring practical relevance across diverse scientific disciplines.

• Computer Vision
• Machine Learning
• Sports Analytics
• +۳ مورد دیگر

David Lau is a Lab Instructor/Supervisor at the University of Waterloo, specializing in computer vision and machine learning applications across sports analytics and environmental monitoring. His work focuses on developing algorithms for hockey puck localization, player tracking, and sea ice classification using SAR imagery. He contributes to datasets like AI4Arctic and explores synthetic data augmentation techniques for improving model robustness. Research interests include deep learning architectures for multi-task learning, contextual cue utilization in vision systems, and the integration of remote sensing data with climate models. Key areas of exploration involve improving hockey video analysis through homographic projections and leveraging Bayesian methods for uncertainty quantification in environmental predictions. Recent work emphasizes domain-specific adaptations like sports field localization and ice hockey event detection. His contributions span from foundational algorithms (e.g., IceGCN for SAR imagery) to practical systems like PuckNet for puck location estimation in broadcast videos. Advising and grants: No specific grants or advisees listed in available information. Collaborates on projects involving hockey analytics teams and environmental monitoring initiatives, though specific team names are not disclosed.

Qinglong Tianمشاهده پروفایل

استادیار

• Statistical Methodology
• Reliability Engineering
• Machine Learning
• +۳ مورد دیگر

Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.

Kyle Daunمشاهده پروفایل

استاد

• Thermal Radiation Analysis
• Advanced Materials Processing
• Laser Diagnostics
• +۷ مورد دیگر

Kyle Daun is a Professor at the University of Waterloo, specializing in thermal sciences and advanced material characterization. His research focuses on inverse analysis methodologies, combustion efficiency quantification, and laser-based diagnostics. He leads the Waterloo Laboratory For Inverse Analysis and Thermal Sciences, advancing technologies for environmental monitoring and industrial processes. Dr. Daun’s work bridges applied physics, materials engineering, and statistical modeling, with a strong emphasis on practical applications such as steel processing and greenhouse gas emissions detection. His research interests include thermal radiation analysis, nanoparticle characterization via laser-induced incandescence, Bayesian statistical modeling for emission quantification, and the development of novel optical diagnostics. He has pioneered techniques for non-invasive measurement of material properties, such as interfacial heat transfer coefficients in hot stamping processes and spectral emissivity variations in advanced steels. Dr. Daun’s recent publications emphasize airborne hyperspectral detection of methane, Bayesian error modeling for greenhouse gas quantification, and real-time optical diagnostics for graphene synthesis. His work addresses critical challenges in environmental sustainability, industrial manufacturing, and energy systems. Collaborations span academia and industry, targeting innovations in sensor technology and thermal analysis methodologies.

Jangho Yangمشاهده پروفایل

استادیار

• Economics
• Econometrics
• Macroeconomics
• +۶ مورد دیگر

Jangho Yang is an Assistant Professor at the University of Waterloo, specializing in economics and econometrics. His research focuses on productivity dispersion, firm growth dynamics, structural change, and applications of Bayesian and information-theoretic models in economic analysis. He is affiliated with research groups including the Management of Innovation, Management of Technology, and Faculty contacts by research area. His work bridges macroeconomic theory with empirical methods, addressing topics such as secular stagnation, climate impacts on businesses, and China's economic development. Recent contributions include studies on parametric productivity measurement using Lévy alpha-stable distributions, complex system modeling via MaxEnt and agent-based frameworks, and the persistent statistical structures in economic systems. His interdisciplinary approach integrates econophysics and statistical mechanics principles into economic analysis.

Luis Lehnerمشاهده پروفایل

استاد پژوهشی

• Strong Gravity
• Gravitational Wave Astronomy
• General Relativity
• +۷ مورد دیگر

Luis Lehner holds the Carlo Fidani Rainer Weiss Chair in Theoretical Physics at the Perimeter Institute for Theoretical Physics and is an Associate Professor in the Department of Physics at the University of Guelph. He also holds adjunct roles at the University of Wisconsin-Milwaukee and Louisiana State University. His research focuses on strong gravity regimes, testing alternative theories of gravity using gravitational wave detectors, and exploring higher-dimensional scenarios and AdS/CFT correspondence. He is a prominent figure in gravitational wave astronomy, working on multimessenger observations and developing frameworks to probe General Relativity's validity. Lehner has held leadership positions including Deputy Faculty Chair at Perimeter Institute and advisory roles at KITP and ICTP-SAIFR. His work bridges theoretical physics with observational astrophysics, emphasizing black hole dynamics, relativistic hydrodynamics, and turbulence. Recent publications highlight advancements in gravitational wave signal analysis, black hole mimicker studies, and nonlinear gravity effects. Education : Not explicitly listed in text, but his roles imply PhD-level training in Physics. His awards include membership in the National Academy of Sciences of Argentina, the Buchalter Cosmology Prize, and fellowships from APS and the Institute of Physics. He actively participates in international collaborations like the Gravitational Wave International Committee and contributes to detector networks like LIGO-Virgo.