جستجوی استاد راهنمای دانشگاهی

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.

Matthew Gardnerمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Behavioral Neuroscience
• Computational Neuroscience
• +۴ مورد دیگر

Matthew Gardner is an Assistant Professor in the Department of Psychology at Concordia University, conducting cutting-edge research on neural prediction mechanisms. His work bridges cognitive neuroscience and behavioral neuroscience through experimental and computational approaches to understand how the brain generates predictions that guide decision-making and behavior. His primary research investigates how sensory and cognitive systems interact to create specific predictions of future events, employing electrophysiology and optogenetic manipulations in rats performing behavioral tasks alongside computational modeling. A critical focus is examining how these predictive mechanisms become disrupted in eating disorders and addiction, with implications for understanding the neural basis of psychiatric conditions. His work reveals fundamental principles of how predictive information guides behavior under both healthy and pathological conditions. Professor Gardner maintains a robust publication record in premier neuroscience journals including Nature, Nature Neuroscience, and Journal of Neuroscience. Analysis of his 15 most recent publications shows consistent emphasis on orbitofrontal cortex function, value-based decision-making, and prediction error signaling. His research demonstrates evolutionary progression from basic neural representation studies to disease mechanism investigations, with increasing focus on computational modeling approaches to integrate findings. While specific grant details aren't provided, his extensive publication output in high-impact journals indicates successful funding and active research programs. His frequent collaborations, particularly with Dr. Geoffrey Schoenbaum, suggest strong laboratory operations and likely supervision of graduate students in experimental neuroscience techniques. The lab appears focused on training researchers in both wet-lab neuroscience methods and computational analysis skills.

Jean-François Côtéمشاهده پروفایل

استاد

• Combinatorial optimization
• Stochastic programming
• Mathematical programming
• +۵ مورد دیگر

Jean-François Côté is a Full Professor at the Department of Operations and Decision Systems within the Faculty of Business Administration at Laval University. His research focuses on combinatorial optimization, stochastic programming, and mathematical programming with applications in vehicle routing, cutting and loading problems, and logistics optimization. Dr. Côté holds a Doctorate in Computer Science, Operations Research from the University of Montreal, along with Master's and Bachelor's degrees in the same field from the same institution. His academic credentials provide a strong foundation for his interdisciplinary research at the intersection of computer science and operations research. His research spans several key areas in operations research including: Combinatorial optimization techniques for complex routing problems Stochastic programming approaches for handling uncertainty in logistics Mathematical programming models for integrated decision making Vehicle routing and delivery optimization in various contexts Cutting and loading problems in transportation and manufacturing Professor Côté's recent publication record demonstrates a strong focus on solving complex logistics and transportation problems with mathematical rigor. His work addresses challenging real-world problems including same-day delivery optimization, bike sharing systems, home healthcare routing, and automotive manufacturing sequencing. His methodological contributions include novel algorithmic approaches such as logic-based Benders decomposition, disaggregated integer L-shaped methods, and branch-and-regret algorithms. Dr. Côté has published extensively in top-tier operations research journals including: INFORMS Journal on Computing European Journal of Operational Research Transportation Science Computers & Operations Research Operations Research He actively collaborates with researchers across North America and Europe through the CIRRELT research center. His work addresses practical problems faced by industries including transportation, healthcare, manufacturing, and e-commerce. Professor Côté supervises graduate students working on cutting-edge optimization problems and contributes to advancing both theoretical and applied aspects of operations research. His laboratory, part of the broader research ecosystem at Laval University, focuses on developing innovative mathematical models and algorithms to solve complex decision-making problems in logistics and supply chain management.

• 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.

• Extractive Metallurgy
• Sustainable Mineral Processing
• Environmental Impact Reduction in Metallurgy
• +۴ مورد دیگر

Dr. Eduard Guerra serves as Associate Professor in the Bharti School of Engineering and Computational Science at Laurentian University, where he leverages his dual industry-academic background to advance sustainable extractive metallurgy. A Laurentian alumnus (B.Eng. and B.Sc. in Extractive Metallurgical Engineering, 1993), he holds advanced degrees from the University of British Columbia (M.A.Sc. 1997, Ph.D. 2003) and brings industrial research experience to his academic role. His educational foundation includes: Ph.D. in Metals and Materials Engineering, University of British Columbia (2003) M.A.Sc. in Metals and Materials Engineering, University of British Columbia (1997) B.Eng. in Extractive Metallurgical Engineering, Laurentian University (1993) B.Sc., Laurentian University (1993) Dr. Guerra's research concentrates on reducing energy consumption and environmental footprints across mineral processing operations. His current flagship project develops an electrolytic cyanide destruction method to replace chemical-intensive treatment in gold mills, while his recently patented energy-efficient ore comminution technology simultaneously minimizes waste generation. Past breakthroughs include activated carbon alternatives for gold adsorption and catalyst-enhanced sulfide mineral processing. Analysis of his 15 most recent publications reveals dominant themes in gold processing sustainability (60% of works), particularly cyanide management and alternative leaching methods, alongside significant contributions to electrochemical engineering (25%) and process optimization (15%). His work consistently bridges fundamental electrochemistry with industrial applicability, targeting measurable environmental improvements in mining operations. Key achievements include: Patented activated carbon coated polystyrene bead technology for gold cyanidation Invention of energy-efficient ore comminution process with waste reduction Development of electrolytic cyanide destruction alternative to chemical treatment Innovative research on thiosulfate leaching with non-toxic additives Dr. Guerra maintains active research funding through government and industry partnerships, supervising graduate students on environmentally critical projects. His COM conference publications demonstrate strong industry engagement, while his undergraduate laboratory development work highlights commitment to metallurgical education. The research group provides students with hands-on experience in sustainable process design, preparing them for careers addressing mining's environmental challenges.