جست‌وجوی استاد و پژوهشگر

Pierre-Majorique Légerمشاهده پروفایل

استاد

• User Experience (UX)
• Human-Computer Interaction (HCI)
• Human-Centered AI (HCAI)
• +۲ مورد دیگر

Pierre-Majorique Léger is a Full Professor at the Department of Information Technology , HEC Montréal. He serves as Director of the ERPsim Laboratory , Co-Director of Tech3Lab , and holds the Chair in User Experience IVADO . His research focuses on User Experience (UX) , Human-Computer Interaction , Human-Centered AI , NeuroIS , and Serious Games for Learning . M.Sc.A. (Industrial Engineering), Polytechnique Montréal Ph.D. (Electrical Engineering), Polytechnique Montréal Postdoctoral Fellowship (Information Technology), HEC Montréal His research interests combine neuroscience with information systems, examining: Cognitive load measurement in digital environments Human-AI collaboration dynamics Neuroadaptive interfaces User behavior in e-commerce Technostress mitigation UX evaluation in VR/AR Recent research trends show: Systematic reviews on AI ethics in industry/education Neurophysiological studies using EEG/fNIRS Experiments on multimodal interfaces Gamification effectiveness analysis Scientific Awards : IVADO Chair in User Experience Grand Prix de recherche (2024) Multiple best paper nominations (2020-2022) Advising & Grants : Directed 3 PhD theses and 74 MSc theses (2020-2025) Secured funding for ERPsim Lab's $2M research grant Collaborated with Tech3Lab on international projects Laboratories : Co-Director, Tech3Lab Director, ERPsim Laboratory IVADO-affiliated researcher

Naimul Khanمشاهده پروفایل

دانشیار

• Machine Learning
• Medical Imaging
• Multimedia
• +۵ مورد دیگر

Naimul Khan is an Associate Professor and Associate Chair in Graduate Studies at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering. His research focuses on practical applications of machine learning, medical imaging, computer vision, and augmented/virtual reality technologies to solve real-world healthcare and multimedia challenges. PhD (Toronto Metropolitan University, 2014) MSc (University of Windsor, 2010) BSc (Bangladesh University of Engineering and Technology, 2008) His research interests span medical imaging automation, virtual reality systems for healthcare applications, and machine learning techniques for biomedical signal processing. He has developed innovative algorithms for ultrasound analysis, emotion recognition systems using EEG data, and segmentation approaches for diabetic foot ulcers. Publications demonstrate consistent focus on practical implementations across computer vision, medical diagnostics, and extended reality (XR) technologies. Scientific contributions include a Best Paper Award at the IEEE International Symposium on Multimedia (2017) and prestigious postdoctoral and graduate scholarships. He actively collaborates with industry partners through the TMU Multimedia Research Laboratory, teaching graduate courses like ELE 725 (Basics of Multimedia Systems) and COE 318 (Software Systems). Best Paper Award, IEEE International Symposium on Multimedia (2017) OCE TalentEdge Postdoctoral Fellowship (2014-2016) Ontario Graduate Scholarship (2013-2014) Queen Elizabeth II Scholarship in Science & Technology (2012-2013) As co-director of the TMU Multimedia Research Laboratory, Khan bridges academic research with industry applications through practical implementations of machine learning and extended reality technologies in healthcare domains.

Jia Huمشاهده پروفایل

استادیار بالینی

• Social Determinants of Health
• Housing and Health
• Climate Change and Health
• +۵ مورد دیگر

Jia Hu is a public health physician and medical lead of the prevention and health promotion team within Population and Public Health at the British Columbia Centre for Disease Control (BCCDC). He also holds a position as a Clinical Assistant Professor at the University of British Columbia's School of Population & Public Health, where he contributes to academic training and research in public health disciplines. Dr. Hu's educational background includes medical training at the University of Alberta, followed by residency training in family medicine and public health and preventive medicine at the University of Toronto. During his residency, he earned a Master of Science in Health Policy, Planning, and Finance from the London School of Economics and the London School of Hygiene and Tropical Medicine. His research interests span multiple critical areas in modern public health, with particular focus on social determinants of health, housing and health, climate change and health, food security, mental wellness, and cancer prevention and screening. Dr. Hu has also developed significant expertise in behavior change and marketing strategies specifically tailored for public health interventions such as vaccination programs and cancer screening initiatives. His work bridges clinical practice with population-level health promotion strategies. Analysis of Dr. Hu's recent publications reveals a strong interdisciplinary research profile that spans public health, epidemiology, and increasingly, computational approaches to health data. While his earlier work focused on traditional public health domains, his recent publications show a growing engagement with artificial intelligence, natural language processing, and speech technology applications in healthcare contexts. This evolution demonstrates his ability to adapt to emerging technological paradigms while maintaining focus on core public health challenges. Dr. Hu's professional journey includes diverse experiences that have shaped his interdisciplinary approach: working at McKinsey advising large organizations on health-related issues, serving as a Medical Officer of Health in Alberta where he contributed to the COVID-19 response, and founding and leading a public health non-profit organization focused on increasing uptake of preventive health behaviors. As medical lead of the prevention and health promotion team at BCCDC, Dr. Hu oversees initiatives that address a wide range of public health concerns across British Columbia. His work integrates evidence-based approaches with practical implementation strategies to improve population health outcomes through prevention-focused interventions and health promotion activities.

Michael Rainbowمشاهده پروفایل

دانشیار

• Biomechanics
• Imaging
• Computational Modeling
• +۵ مورد دیگر

Michael Rainbow is an Associate Professor in the Department of Mechanical and Materials Engineering at Queen's University, Faculty of Engineering and Applied Science. His research focuses on biomechanics, imaging, and computational modeling of musculoskeletal systems. Research Interests : Biomechanics, Imaging, Computational Modeling Email : michael.rainbow@queensu.ca His recent work (2025) examines scapular kinematics , foot-ankle adaptation , and joint power dynamics using advanced imaging techniques like biplanar videoradiography and 4D CT . Key themes include load direction effects , muscle-tendon interactions , and standardization of imaging protocols . He contributes to the Centre for Health Innovation and develops tools for model-based pose estimation and kinematic validation . His studies span shoulder pathology , foot mechanics , and human locomotion , with applications in arthritis research and wearable technology.

Youcef Derbalمشاهده پروفایل

دانشیار

• Complex Adaptive Systems
• Cancer Modeling
• Machine Learning in Oncology
• +۴ مورد دیگر

Dr. Youcef Derbal is an Associate Professor in the Department of Information Technology Management at Toronto Metropolitan University's Ted Rogers School of Information Technology Management. With a Ph.D. in Electrical and Computer Engineering from Queen's University, he brings extensive industry experience from robotics, telecommunications, and software sectors to his academic work. His research focuses on Complex Adaptive Systems, particularly in modeling cancer dynamics through agent-based simulations, machine learning applications for oncology, and computational frameworks for biological processes. Key research areas include tumor dynamics, cell signaling pathways, and tumor-infiltrating lymphocyte recruitment. Dr. Derbal teaches undergraduate courses in business information systems, programming, system analysis, database design, and client-server applications, along with graduate-level advanced project management. His publication record shows consistent output in computational biology, cancer modeling, and grid computing since 2006. Professional affiliations include: Association for Computing Machinery (ACM) Institute of Electrical and Electronics Engineers (IEEE) International Society for Computational Biology (ISCB) Administrative contributions include service on University Senate, chairing multiple departmental committees, leading the Business Technology Management curriculum development (2009-2014), and serving as School Director during 2013/2014.

Sébastien Le Digabelمشاهده پروفایل

استاد

• Operations Research
• Mathematical Optimization
• Black-box Optimization
• +۵ مورد دیگر

Sébastien Le Digabel is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds affiliations with GERAD (Groupe d'études et de recherche en analyse des décisions) and IVADO (Institut de valorisation des données), where he contributes to research in optimization and artificial intelligence. Professor Le Digabel's research focuses on black-box optimization and derivative-free methods, particularly the Mesh Adaptive Direct Search (MADS) algorithm. His work spans three main areas: development of optimization methods, application to engineering problems, and development of optimization software. He has made significant contributions to extending MADS for constrained optimization, mixed-variable problems, multiobjective optimization, and stochastic optimization. His recent publications (2023-2025) demonstrate continued innovation in handling complex optimization challenges including categorical variables, hierarchical constraints, and multi-fidelity approaches. These works appear in top journals like Computational Optimization and Applications, SIAM Journal on Optimization, and Optimization and Engineering. Professor Le Digabel actively supervises graduate students, having completed 8 PhD theses and 10 Master's theses with topics spanning multiobjective optimization, hyperparameter tuning, smart grid applications, and power systems optimization. His research is supported by diverse funding sources including NSERC, FRQNT, InnovÉÉ, IVADO, Hydro-Québec, Rio Tinto, and Huawei-Canada, reflecting the broad applicability of his work across academic and industrial domains. Professor Le Digabel leads the NOMAD Research Group and is the primary developer of the NOMAD optimization software, which implements the MADS algorithm and serves as a standard tool in the derivative-free optimization community. The software is available at www.gerad.ca/nomad and continues to evolve with new capabilities for challenging optimization problems.

Dominique Orbanمشاهده پروفایل

استاد

• Numerical Optimization
• Nonlinear Programming
• Numerical Linear Algebra
• +۴ مورد دیگر

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

Olivier Gendreauمشاهده پروفایل

مدرس

• Software engineering
• Virtual reality and related simulations
• Software and development
• +۳ مورد دیگر

Olivier Gendreau serves as a Lecturer in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has established himself as an accomplished educator and researcher in software engineering and virtual reality applications. His work spans both theoretical research in knowledge management within software development processes and practical applications in engineering education. Department of Computer Engineering and Software Engineering, Polytechnique Montréal Primary research center: New Frontiers in Information and Communications Technologies Secondary research centers: Modeling and Artificial Intelligence; Industry of the Future and Digital Society Professor Gendreau's research focuses on the intersection of software engineering practices, knowledge management, and virtual reality applications. His work examines how knowledge flows and is converted during software development processes, with particular attention to quality control practices, documentation validation, and the application of virtual reality technologies. He has published extensively on integrative learning approaches that bridge academic theory with professional practice in software engineering. Analysis of Professor Gendreau's publication record reveals a consistent focus on knowledge management in software development, with significant contributions to understanding knowledge acquisition, flow, and conversion processes. His work frequently employs case study methodology to examine practical applications in quality control, component reuse, and verification/validation techniques. More recently, his research has expanded into virtual reality applications and ecological identity in workplace contexts, showing an evolving research trajectory that maintains its core focus while expanding into new application areas. 2020 - Excellence in Teaching Award from Polytechnique Montréal ($10,000) Professor Gendreau has developed and supervised numerous integrative projects for computer and software engineering students at Polytechnique Montréal, including both second-year and third-year projects designed to facilitate students' transition to the professional workforce. His teaching emphasizes practical, industry-relevant experiences that prepare students for real-world challenges in software development. His work with student teams participating in competitions like Laval Virtual demonstrates his commitment to providing students with opportunities to apply their knowledge in competitive, real-world settings.

Guillaume-Alexandre Bilodeauمشاهده پروفایل

استاد

• Computer vision
• Artificial intelligence
• Image and video processing
• +۶ مورد دیگر

Guillaume-Alexandre Bilodeau is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He serves as Head of the Laboratory for Image and Video Interpretation and Processing (LITIV) and is a member of the Institute for Data Valorization (IVADO). His research is centered within the 'New frontiers in information and communications technologies' center of excellence, with additional affiliations in 'Modeling and Artificial Intelligence' and 'Transport and Sustainable Infrastructure' centers. Professor Bilodeau's research focuses on computer vision and artificial intelligence, with particular expertise in: Developing methods for interpreting human actions and interactions within scenes Integrating visible and infrared camera data for improved object and human detection Object tracking using pan-tilt-zoom camera systems Medical applications of thermal imaging and thermography, including Parkinson's disease diagnosis from gait analysis His recent publications demonstrate continued strong activity in video prediction, medical applications of computer vision, and multi-object tracking. Professor Bilodeau maintains a robust publication record with multiple 2025 publications in top venues, reflecting ongoing research productivity and relevance in the computer vision community. Professor Bilodeau has received significant recognition for his work, including: 2023: Fonds de recherche du Québec research grant award 2008: Best vision paper award at Canadian Conference on Robot Vision 2002: Honorable mention for Pattern Recognition Society award As an educator and mentor, Professor Bilodeau has supervised 12 PhD students and 26 Master's students to completion. He teaches courses in multimedia technologies and computer vision. His laboratory, LITIV, provides a research environment focused on image and video interpretation with applications spanning surveillance systems, medical diagnostics, and transportation infrastructure monitoring.

Matthew Joseph Kusnerمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +۵ مورد دیگر

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

Ettore Merloمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Software Engineering
• +۵ مورد دیگر

Ettore Merlo is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads research in cybersecurity, artificial intelligence, and software systems. He holds an M.Sc. from the University of Turin and a Ph.D. from McGill University. His affiliations include membership in the Institute for Data Valorization (IVADO), focusing on data science and AI innovation. His research integrates software engineering with AI, emphasizing: Software artifact analysis (static/dynamic/symbolic) AI-driven security solutions for clone detection, malware analysis, and access control Fairness and robustness in machine learning systems Evolutionary analysis of software vulnerabilities Recent publications (2022-2025) demonstrate a strong focus on ethical AI, including bias mitigation in neural networks, automated anomaly detection, and certification of safety-critical ML systems. His work frequently applies graph neural networks, unsupervised learning, and formal verification methods to industrial and cybersecurity challenges. Professor Merlo has supervised 25 graduate students (10 PhD, 15 Master's), with projects ranging from avionics software to phishing kit analysis. While no scientific awards are listed, his extensive publication record includes 136 works spanning journals, conferences, and technical reports. Collaborations include partnerships with industrial telecommunication firms and international academia. No dedicated lab is specified, but his research aligns with Polytechnique Montréal's 'New Frontiers in Information and Communications Technologies' center.

Éric Germainمشاهده پروفایل

استاد آموزشی

• Software Engineering
• Software Development
• Engineering Education
• +۳ مورد دیگر

Éric Germain is a Teaching Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds a B.Eng. in Industrial Engineering (1992), an M.Sc.A. in Computer Engineering (2004), and an M.Sc. in Business Analytics (2019). His professional affiliations include being a Fellow of Engineers Canada, a member of the Order of Engineers of Quebec, and a member of the Order of Chartered Administrators of Quebec. His educational background includes: M.Sc. Management – Business Analytics, HEC Montréal, 2019 M.Sc.A. – Research Master's in Computer Engineering, Polytechnique Montréal, 2004 B.Ing. – Industrial Engineering, Polytechnique Montréal, 1992 Professor Germain's research focuses on software engineering education, software requirements analysis, and agile methodologies. His work examines how student teams behave in software development projects and how to improve software engineering education through practical applications. His publication history shows a progression from empirical studies of student software teams to broader analyses of software processes and methodologies, with his most recent work expanding into higher education policy. Professor Germain has received significant professional recognition as a Fellow of Engineers Canada. His professional affiliations include: Fellow of Engineers Canada Member of Scientific Committee of the Case Learning Laboratory (CLL) Member of Order of Chartered Administrators of Quebec Member of Order of Engineers of Quebec Member of Canadian Engineering Education Association (CEEA) Member of Francophone Association for Knowledge (ACFAS) As an educator, Professor Germain supervises multiple master's students and teaches core software engineering courses. His teaching experience spans multiple institutions including Polytechnique Montréal, ÉTS, and CeFTI Université de Sherbrooke. His industry background as a data scientist and consultant in digital transformation provides practical context to his academic work. He has also contributed to university administration as Head of Programs of Study Development and Evaluation at ÉTS, bringing administrative experience to his academic role.

Lévis Thériaultمشاهده پروفایل

مدرس

• Artificial Intelligence
• Learning and Inference Theories
• Knowledge Representation
• +۷ مورد دیگر

Lévis Thériault serves as a Lecturer in the Department of Computer Engineering and Software Engineering within Polytechnique Montréal's Faculty of Engineering. He is also a member of the Institute for Data Valorization (IVADO), contributing to Montreal's AI research ecosystem. His academic credentials include a B.Eng., DESS from UQAC, M.Sc.A., and PhD coursework at Polytechnique Montréal. His research spans two dynamic domains: artificial intelligence applications in healthcare (notably the Marvin chatbot system for HIV treatment adherence) and innovative educational technologies for engineering education. Recent work focuses on conversational agents for patient self-management, digital learning environments, and active learning methodologies. His publication trend shows a strategic pivot toward health AI since 2020, with multiple presentations at International AIDS Conferences, while maintaining his educational technology research thread. Dr. Thériault actively supervises graduate students across multiple cohorts, with current advisees working on AI-driven clinical tools and educational software. His supervision record includes 14 professional Master's graduates between 2021-2024 and ongoing PhD and research Master's projects. Student theses demonstrate strong industry alignment, with implementations at organizations including Desjardins, Intact Assurance, and Criteo. Teaching responsibilities include core courses in operating systems, discrete structures, and software design, where he implements his research on active learning strategies. His 2020 publication on smartphone-based assessment for large engineering classes exemplifies his practical approach to educational innovation. The May 2022 press coverage of his AI solution for Alloprof's homework help platform highlights real-world impact of his educational technology research.

François-Raymond Boyerمشاهده پروفایل

دانشیار

• Computer Architecture
• VLSI Systems
• Microelectronics
• +۴ مورد دیگر

François-Raymond Boyer is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds a B.Sc. and Ph.D. from Université de Montréal and teaches courses in programming and digital audio. His research spans: Computer architecture and VLSI systems Network processing and traffic management Digital audio signal processing Hardware acceleration and optimization He is affiliated with ReSMiQ (Strategic Cluster for Microsystems) and OICRM (Music Research Observatory). Recent publications focus on 5G network processing, modular programming frameworks, and high-speed traffic management architectures, primarily in IEEE Access and IEEE Transactions on VLSI Systems. Professor Boyer has supervised 3 PhD and 6 Master's students, with research topics including network architecture, audio processing, and hardware design. No scientific awards are mentioned in the available records.

Michel C. Desmaraisمشاهده پروفایل

استاد

• Artificial Intelligence
• Cognitive Modeling
• Learning Environments
• +۵ مورد دیگر

Michel C. Desmarais is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been faculty since 2002. With a PhD in Psychology from Université de Montréal, his research bridges artificial intelligence, educational technology, and human-computer interaction. He holds affiliations with IVADO and LAMA-WeST research groups, and has held visiting positions at Sorbonne University, Eindhoven Technical University, and other European institutions. His research focuses on three interconnected pillars: 1) Cognitive modeling and educational data mining , developing algorithms for student knowledge assessment and adaptive learning systems; 2) AI-driven educational tools , including automated grading systems and peer instruction platforms; and 3) Recommendation systems and user modeling , particularly for personalized learning interfaces. His work consistently applies machine learning to solve practical challenges in technology-enhanced education. Analysis of his 150+ publications reveals strong trends in educational NLP (sentence similarity for short-answer grading), generative AI (LLM-generated code validation), and Bayesian modeling (Q-matrix refinement). Recent work increasingly focuses on transformer architectures and real-world educational datasets. He maintains an active supervision record, having graduated 35+ graduate students. Current PhD candidates work on NLP for educational applications (Bakhtiari, Kamdem) and AI for engineering (Wang). His teaching covers user interface design, recommender systems, and intelligent interfaces. Professional service includes editorial leadership (JEDM journal), conference co-chairing (UMAP 2017, EDM founding), and grant review panels for NSERC, MITACS, and EU programs. Industry experience includes prior roles as R&D Director at MVM Inc. and researcher at Montreal Computer Research Center.