جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Cancer Research
• Model Organism
• +۷ مورد دیگر

Edwin Wang is a Professor at the University of Calgary holding concurrent appointments across three key departments: the Department of Medical Genetics, Department of Biochemistry & Molecular Biology, and Department of Oncology. His research program bridges computational and experimental biology to advance precision medicine applications. His primary research interests include: Bioinformatics and genomic data analysis Cancer biomarker discovery through machine learning/deep learning Predictive modeling of diseases using molecular network algorithms Model organism studies for translational insights Development of novel data interpretation frameworks for hypothesis generation Professor Wang emphasizes creating innovative computational methods that transform genomic data into clinically actionable knowledge for personalized treatment strategies. While specific scientific awards aren't documented in available sources, his active research program in computational oncology and systems biology indicates ongoing grant support. Prospective students should contact him directly regarding current advising capacity and research opportunities. Professor Wang leads an integrated research group developing computational tools for precision medicine, though the specific lab name isn't provided in source materials. His team's work focuses on translating molecular network analyses into clinical applications for cancer and other complex diseases.

Tri Nhu Doمشاهده پروفایل

استادیار

• Wireless Communications
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Dr. Tri Nhu Do is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, where he conducts cutting-edge research at the intersection of wireless communications and artificial intelligence. His academic journey spans institutions across Vietnam, South Korea, the United States, and Canada, bringing a global perspective to his work. Dr. Do is affiliated with the Advanced Microwave and Space Electronics Research Center (POLY-GRAMES) and contributes to the 'New Frontiers in Information and Communications Technologies' center of excellence. Dr. Do's research focuses on wireless communications systems, artificial intelligence applications in telecommunications, and integrated sensing and communication technologies. His work addresses critical challenges in next-generation wireless networks, particularly in security, resource allocation, and performance optimization. Recent research demonstrates a strong emphasis on applying deep learning, generative AI, and federated learning techniques to solve longstanding problems in wireless communications. His publication record shows remarkable productivity and impact, with numerous articles in top IEEE journals including IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Letters. The research trends indicate a strategic shift toward integrating AI with traditional communication theory, particularly in security applications, reconfigurable intelligent surfaces, and UAV communications. Dr. Do teaches advanced courses in signal detection and estimation, communication theory, and digital transmission, sharing his expertise with the next generation of electrical engineers. His teaching reflects his research interests, providing students with both theoretical foundations and exposure to cutting-edge developments in the field.

Mohamed-Salah Oualiمشاهده پروفایل

استاد

• Reliability of industrial systems
• Statistical learning models
• Bayesian learning models
• +۹ مورد دیگر

Mohamed-Salah Ouali is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where he leads research at the intersection of industrial engineering, data science, and reliability engineering. He maintains active affiliations with the Institute for Data Valorization (IVADO), Poly-Industries 4.0 Laboratory, and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Professor Ouali's research focuses on reliability of industrial systems, statistical and Bayesian learning models, remaining life prediction, failure cause analysis, and maintenance optimization. His work bridges theoretical advances in machine learning with practical industrial applications, with particular expertise in developing interpretable models for predictive maintenance and risk assessment. Recent research has expanded into polygon generation techniques, causal reinforcement learning, and heterogeneous data fusion for industrial applications. His publication record shows consistent output with 55 publications through 2025, including significant contributions in Reliability Engineering and System Safety, Journal of Intelligent Manufacturing, and Engineering Applications of Artificial Intelligence. The research trends demonstrate an evolution from traditional reliability models to increasingly sophisticated AI-driven approaches for industrial systems. As an educator, Professor Ouali has supervised 10 PhD students and 13 Master's students, with the most recent completions in 2024 (PhD) and 2021 (Master's). His supervision spans topics in predictive maintenance, failure analysis, and industrial data analytics, with students frequently co-authoring publications in high-impact journals. His laboratory work through the Poly-Industries 4.0 Laboratory focuses on developing practical tools for industrial reliability and maintenance optimization, with strong connections to real-world industrial applications and partnerships.

Marcelin Joanisمشاهده پروفایل

استاد

• Public Infrastructure
• Public Finance
• Economic Development
• +۷ مورد دیگر

Marcelin Joanis is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where he applies his Ph.D. in Economics from the University of Toronto to critical issues in public infrastructure and economic policy. His research bridges economics, engineering, and public policy to address practical challenges in infrastructure financing, governance, and sustainable development. Professor Joanis maintains extensive affiliations across the research ecosystem: Principal Investigator and Fellow at CIRANO (Interuniversity Research Centre for Organizational Analysis) Director of the Michael D. Penner Institute on ESG Issues Manager of International Projects Thematic Focus Member of CÉRIUM (Centre for International Studies and Research of the University of Montreal) Member of CRIB (Concrete Infrastructure Research Center) Member of Poly-Industries 4.0 Laboratory Member of GMT (Research Group on Management and Globalization of Technology) His research portfolio spans public infrastructure financing, multi-level governance, economic geography, and international economic development. Professor Joanis has published extensively on fiscal federalism, infrastructure investment impacts, and energy transition, with recent work examining decarbonization of remote microgrids and the economic impacts of road connections in Northern Canada. His interdisciplinary approach combines rigorous economic analysis with practical engineering applications to inform policy decisions. Professor Joanis has supervised 13 graduate students to completion (3 doctoral and 10 master's theses), with one doctoral student currently in progress. His students have addressed diverse topics including algorithmic coordination, infrastructure development in Northern Canada, and sustainable technologies, demonstrating the breadth of his research influence. He is regularly engaged in public policy debates, contributing to media discussions on Quebec's budgetary framework, tax policy impacts, and sustainable infrastructure investment. His expertise is frequently sought by government bodies and research institutes, reflecting the practical relevance of his work to contemporary economic and infrastructure challenges.

Serge Prudhommeمشاهده پروفایل

استاد

• Scientific Computing
• Finite Element Method
• Error Estimation
• +۷ مورد دیگر

Serge Prudhomme is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a background including a Dipl. Eng. from École Centrale de Lille, an M.Sc. from the University of Virginia, and a Ph.D. from UT Austin, he has established himself as a leading researcher in computational mathematics and mechanics. His primary research interests span scientific computing, finite element methods, error estimation and adaptive methods, multi-scale modeling, verification and validation of numerical calculations, uncertainty quantification, and model reduction. Professor Prudhomme's work demonstrates a consistent focus on developing mathematically rigorous computational frameworks with practical engineering applications. His recent publications show an expanding interest in integrating machine learning techniques with traditional numerical methods. Analysis of his recent publications reveals a strong trend toward interdisciplinary research that bridges computational mathematics with mechanics and increasingly machine learning. His work on peridynamics, proper generalized decomposition, and neural network applications to scientific computing represents cutting-edge developments in computational methods. The publications demonstrate a consistent focus on improving the accuracy, efficiency, and reliability of computational models. Professor Prudhomme has successfully supervised 10 graduate students to completion (4 doctoral theses and 6 master's theses), indicating a strong commitment to student mentorship. His supervision record spans topics including wave equation approximations, goal-oriented calibration, contact detection for ellipsoids, and PGD reduced-order modeling. His research is supported by active collaborations with institutions worldwide, as evidenced by his co-authorship patterns and conference participation across North America and Europe. The breadth of his work, from fundamental mathematical developments to practical engineering applications, positions him as a significant contributor to the field of computational science and engineering.

Marc Laforestمشاهده پروفایل

دانشیار

• Continuum mechanics
• Hyperbolic conservation laws
• Kinetic equations
• +۶ مورد دیگر

Marc Laforest is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Sc. from UQAM and M.Sc. and Ph.D. degrees from SUNY at Stony Brook. His academic career at Polytechnique Montréal spans multiple decades, during which he has established himself as a researcher in computational mathematics and numerical analysis. Professor Laforest's research interests focus on continuum mechanics, hyperbolic conservation laws, kinetic equations, numerical analysis, multi-scale analysis, a posteriori error estimation, and mesh adaptivity . His work bridges theoretical mathematics with practical applications, particularly in wave propagation and gas mechanics. Recent publications demonstrate increasing integration of machine learning techniques with traditional numerical methods, reflecting evolving trends in computational science. Analysis of his 15 most recent publications reveals a strong trajectory in computational mathematics with growing emphasis on machine learning applications. His research spans pure mathematical theory to practical engineering applications, particularly in superconductivity modeling. The consistent publication rate (2 papers in 2025, 2 in 2024, 2 in 2023, and 5 in 2022) indicates active research programs and likely multiple ongoing grants. Professor Laforest has supervised 5 doctoral students to completion (most recent in 2024) and 9 master's students (most recent in 2023). His students' thesis topics reflect the breadth of his research interests, ranging from wave equation approximation to contact detection methods and superconductivity modeling. This extensive supervision record demonstrates his commitment to graduate education and mentorship. His teaching responsibilities include courses in differential equations, analysis, and numerical analysis , connecting his research expertise with classroom instruction. While specific grant information isn't provided in the available materials, his consistent publication record across multiple high-impact journals suggests successful grant acquisition and management of research funding.

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.

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.

Ange Adrienne Nyamen Tatoمشاهده پروفایل

استادیار

• Artificial Intelligence in Education
• Generative AI for Education
• Machine Learning Algorithms
• +۷ مورد دیگر

Ange Adrienne Nyamen Tato serves as an Assistant Professor in the Department of Teaching and Learning Studies at Laval University's Faculty of Education. Her academic journey spans multiple institutions across Canada and Morocco, with a strong interdisciplinary background combining computer science, artificial intelligence, and educational theory. PhD in Computer Science (Artificial Intelligence) from University of Quebec at Montreal (UQAM), 2020 Master's degree in Computer Science from University of Quebec at Montreal (UQAM), 2015 Engineering degree in Information Systems from Mohammedia School of Engineers in Morocco, 2014 DEUST in Mathematics, Computer Science and Physics from Hassan II University, Morocco, 2011 Professor Tato's research focuses on the intersection of artificial intelligence and education, with particular expertise in generative AI applications for educational contexts, machine learning algorithms, intelligent tutoring systems, educational data mining, and serious game design. Her work addresses critical challenges in educational technology including transparency, assessment practices, and engagement issues that have limited the adoption of AI-powered learning tools. She is particularly interested in the ethical implications, environmental impact, and potential biases of AI in educational settings. Her recent publications demonstrate a consistent focus on developing sophisticated models for user behavior prediction, adaptive learning systems, and integrating pedagogical knowledge into AI frameworks. The research spans multiple domains including logical reasoning development, socio-moral reasoning, piloting training, and general educational applications of deep learning and knowledge tracing techniques. Professor Tato has secured significant research funding, including a $192,500 NSERC Discovery Grant for her project 'Optimizing Generative Artificial Intelligence for Education: Towards a Holistic Approach Integrating Teachers and Learners.' This five-year project aims to develop pedagogically aware large language models (PA-LLMs) that better serve educational purposes by integrating educational theories, human learning factors, and bias correction mechanisms. Principal Investigator for NSERC Discovery Grant ($192,500 over 5 years) PC Member for ICCE 2023 and 2024 conferences PC Member for EDM 2024 conference PC Member for AIED 2024 conference Professor Tato teaches graduate courses including TEN-7028 Games and Learning and TEN-7030 Digital Intelligence in Education: Opportunities and Challenges. She is currently accepting Master's students interested in AI applied to education. Her previous professional experience includes work as an Artificial Intelligence Specialist at Beam Me Up Augmented Intelligence (2018-2022) and a postdoctoral fellowship in Deep Learning applied to aeronautics in partnership with Bombardier and CAE (2020-2022).

• Machine Learning
• Operations Research
• Supply Chain Management
• +۷ مورد دیگر

Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors. Dr. Osman's research spans three interconnected domains: Machine Learning & Data Analytics : Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications Operations Research : Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics Supply Chain Management : Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors. Professional credentials include: IBM Mastery Certificate in Predictive Data Analytics Professional Engineer (PEng) license from Ontario Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.

• Computational Hydraulics
• River Ice Engineering
• Freeze-up and Breakup Processes
• +۵ مورد دیگر

Yuntong She is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering, holding this position since 2024 after progressing from Assistant Professor (2012-2019) to Associate Professor (2019-2024). His research focuses on computational hydraulics with specialization in cold regions engineering and infrastructure resilience. His educational background includes: Ph.D. in Civil & Environmental Engineering, University of Alberta (2004-2008) M.Sc. in Hydraulic Engineering, Tsinghua University (2000-2003) B.Sc. in Hydraulic Engineering, Tsinghua University (1996-2000) Dr. She's research centers on river ice engineering, freeze-up/breakup processes, and ice jam flood forecasting, with additional expertise in sediment transport, urban drainage systems, and oil pipeline integrity. His work combines advanced computational modeling with field data analysis to address hydraulic challenges in cold climates, particularly focusing on infrastructure protection and climate change adaptation. Recent projects demonstrate strong integration of machine learning techniques for ice quantification and transient analysis for pipeline monitoring. Analysis of his 15 most recent publications (2019-2023) reveals three dominant research thrusts: (1) river ice dynamics and flood forecasting (40% of publications), particularly ice jam formation and breakup initiation; (2) pipeline safety and leak detection using transient analysis methods (25%); and (3) renewable energy system performance in cold climates, especially snow impacts on solar panels (15%). His work consistently bridges theoretical modeling with practical engineering applications in Canadian river systems. His scientific recognition includes: Editor's Choice award from Science of the Total Environment (2020) CGU-HS CRIPE Gerard Medal for river ice research (2009) Dr. She actively supervises graduate students, as evidenced by multiple publications with student first authors across river ice, pipeline, and renewable energy topics. His teaching portfolio includes core courses CIV E 431 Water Resources Engineering, CIV E 395 Civil Engineering Analysis III, and specialized CIV E 636 River Ice Engineering. Research collaborations span multiple Canadian institutions with field studies concentrated in the Mackenzie Delta and North Saskatchewan River systems. His laboratory work involves computational modeling of ice processes and pipeline systems, utilizing both proprietary simulation tools and public-domain models like River1D. Current projects focus on climate change impacts on river ice regimes and machine learning applications for real-time infrastructure monitoring in cold regions.