جستجوی استاد راهنمای دانشگاهی

Dr. Ousmane Seidou is a Full Professor in the Department of Civil Engineering at the University of Ottawa, where he has served since 2007. He leads the Hydraulics Lab and holds affiliations with the Faculty of Engineering's Centre for Indigenous Community Infrastructure. His academic journey includes a PhD from École Polytechnique de Montréal (2002) and postdoctoral research at the Institut National de la Recherche Scientifique, Quebec. Dr. Seidou specializes in climate change impacts on water resources, hydrological modeling, and transboundary water management. Education: Ph.D. in Civil Engineering (Hydraulics), École Polytechnique de Montréal (2002) M.Sc. in Water Resources Engineering, École Polytechnique de Montréal (2002) Postgraduate Diploma in Hydroinformatics, École Inter-États des Ingénieurs de l'Équipement Rural (1998) Undergraduate Degree in Civil Engineering, École Mohammadia d'Ingénieurs (1996) Research Focus: Dr. Seidou’s work integrates hydrological modeling with climate adaptation strategies, emphasizing Africa’s water security. Key areas include: Climate change impacts on river basins (e.g., Niger, Congo) Development of flood early warning systems in West Africa Water-Energy-Food-Environment (WEFE) nexus frameworks Environmental flow estimation in wetland ecosystems He leads international projects involving multi-country collaborations and agencies like the United Nations Development Programme and the World Bank. Recent Projects: Principal Investigator for the Niger Basin Authority’s WEFE Nexus initiative Technical leadership in the Dutch-funded BAM-GIRE project (Mali/Guinea wetlands) Development of flood early warning systems for Niamey and Gaya (Niger) Teaching: Courses include Climate Change Impacts on Water Resources, Advanced Hydrological Modeling, and Water Resources Management. He mentors PhD students in hydrology and climate adaptation. Grants & Funding: Secured multi-million-dollar projects with organizations like Wetlands International and the UAE-BELEM Programme. Recent funding includes $135,000 for hydrological modeling tools in the Niger Basin. Labs/Teams: Director of the Hydraulics Lab at the University of Ottawa. Active in global initiatives like the Global Goal on Adaptation (GGA) indicator development under the Paris Agreement.

• Hydrological Modeling
• Climate Change Impacts
• Water Resources Management
• +۴ مورد دیگر

Dr. Weihua Zhuang is a University Professor and University Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. She holds prestigious fellowships from IEEE, Royal Society of Canada, and other organizations. Her research focuses on future communication networks, including 6G, network virtualization, autonomous vehicles, and smart grids. She has led groundbreaking work on MAC protocols like VeMAC for vehicular networks and has contributed extensively to AI-driven network management. Education : Doctorate in Electrical Engineering, University of New Brunswick, Canada (1993) M.Sc. and B.Sc. in Electrical Engineering, Dalian Maritime University, China Research Interests : Dr. Zhuang's work spans wireless networking, IoT, autonomous systems, and smart infrastructure. She explores solutions for network architecture evolution, machine learning applications in communication systems, and service customization for dynamic environments. Her recent projects include digital twin-driven networks, cross-modal transmission strategies, and AI-native slicing for 6G. Awards : Women's Distinguished Career Award (IEEE VTS, 2021) R.A. Fessenden Award (IEEE Canada, 2021) Fellowships from IEEE, RSC, CAE, EIC Grants & Professional Activities : She led the Tier I Canada Research Chair in Wireless Communication Networks (2010–2024) and has held roles such as IEEE VTS President (2023–2024). Her grants include the NSERC Discovery Accelerator Supplements and PREA awards. She edits journals like IEEE Transactions on Vehicular Technology and co-chairs major conferences. Labs & Teams : Her research group focuses on network architecture, AI-driven protocols, and vehicular communication. Collaborations include projects on 6G, satellite-terrestrial integration, and edge computing for autonomous systems.

• Wireless Communications
• Networking Algorithms
• Internet of Things (IoT)
• +۷ مورد دیگر

Hamish van der Ven is an Assistant Professor of Sustainable Business Management of Natural Resources at the University of British Columbia (UBC) within the Department of Wood Science and Faculty of Forestry. He leads the Business, Sustainability and Technology Lab and maintains affiliations with the Environmental Governance Lab at the University of Toronto, the Earth System Governance Project, and the United Nations Forum on Sustainability Standards. PhD in Political Science from University of Toronto Previous roles at McGill University and Yale University Research focuses on sustainable supply chain governance, eco-labeling, transnational environmental governance, corporate social responsibility, and digital technology impacts His recent work analyzes indirect climate impacts of generative AI and social media, transparency mechanisms in fast fashion, stakeholder influence on sustainability standards, and governance dynamics in buyer-driven supply chains. His 2019 book Beyond Greenwash? Explaining Credibility in Transnational Eco-Labeling (Oxford University Press) critiques eco-labeling efficacy. Van der Ven's research has been funded by grants including SSHRC Insight Grant (2025-2030), SSHRC Explore Grant (2023), SSHRC IDG Grant (2021-2023), FRQSC Collaborative Grant (2018-2021), and SSHRC Postdoctoral Fellowship (2016-2017). He examines both broad trends in global environmental governance and specific case studies across agriculture, aquaculture, and retail sectors.

• Sustainable Business Management
• Eco-Labeling
• Transnational Environmental Governance
• +۲ مورد دیگر

Akhtar Hussain serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Laval University, Quebec. His research centers on AI-driven optimization of power and energy systems, with emphasis on microgrid resilience, distributed energy resource integration, and electric vehicle-grid interactions. He actively contributes to advancing grid reliability through innovative resource allocation and consumer satisfaction frameworks. Ph.D. in Electrical Engineering, Incheon National University, South Korea (2019) M.Sc. in Electrical Engineering, Myongji University, South Korea (2014) B.Sc. in Electrical Engineering, National University of Sciences and Technology, Pakistan (2011) Dr. Hussain's research spans power systems resilience, smart grid technologies, and equitable energy access. His work integrates artificial intelligence with traditional power engineering to address challenges in microgrid operation, electric vehicle integration, and renewable energy management. Key focus areas include developing algorithms for optimal resource utilization, enhancing grid stability during contingencies, and designing frameworks for fair energy distribution in diverse communities. His recent publications (2023-2025) reveal a strong trajectory toward AI-enhanced grid management, with recurring themes of resilience optimization, equity-focused resource allocation, and electric vehicle-grid synergies. The research demonstrates increasing sophistication in handling uncertainty through machine learning while addressing socio-technical dimensions of energy transition. Dr. Hussain currently supervises one Master's student and has guided five Ph.D. candidates to completion. His research is funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant ($160,000/year) for the project 'Grid Condition and Resilience-Aware Incentivization and Deployment of Distributed Energy Resources' (2024-2029), supplemented by a Springboard to Discovery award ($40,000) for 2024-2025. As an IEEE member, he collaborates with industry partners on real-world grid modernization initiatives, focusing on practical implementation of resilience strategies through microgrids and mobile energy resources.

• Power Systems
• Smart Grids
• Energy Storage
• +۶ مورد دیگر

Maha Ben Ali is an Associate Professor at the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. She is also an Adjunct Professor at Université Laval and a member of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Laboratoire Poly-Industries 4.0 . Education: Bachelor’s in Industrial Engineering, École Nationale des Ingénieurs de Tunis M.Sc. in Industrial Systems Engineering, École Nationale des Ingénieurs de Tunis M.Sc./Ph.D. in Mechanical Engineering (Industrial concentration), Université Laval Research Interests focus on Industrial Engineering , with emphasis on Demand Management , Supply Chain Optimization , Simulation Modeling , Industry 4.0 , and Data Valorization . Her work combines Operations Research with Machine Learning to solve complex production and logistics challenges, particularly in softwood lumber and bioethanol supply chains . Publications (34 total) include studies on reinforcement learning for demand-driven systems, hybrid recommendation systems in B2B contexts, and symbiotic bioethanol network design . She has presented at conferences like the Winter Simulation Conference and IISE Annual Conference . Scientific Awards: Best paper – CIGI-QUALITA-MOSIM 2023 David Martell Student Paper Prize in Forestry – CORS 2018 NSERC Alexander Graham Bell Canada Graduate Scholarship FRQNT Doctoral scholarship Fast-tracking scholarship Teaching includes courses like Production and Inventory Planning , Simulation of Production Systems , and Logistic Networks . She has supervised 9 completed Master’s theses on topics ranging from multi-project scheduling to CO₂ emission reduction in freight transportation .

• Industrial Engineering
• Operations Management
• Supply Chain Optimization
• +۵ مورد دیگر

Christopher Thomas Ryan is an Associate Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds a PhD and BA from UBC in Management Science and Mathematics, respectively. His research spans optimization theory, game theory, theoretical economics, video game design, and pedagogy for business education. He has held positions at the University of Chicago Booth School of Business and is affiliated with the Colibri Learning Foundation. Education: BA (Mathematics) and PhD (Management Science) from UBC; BA (Sociology and Economics) from University of Guelph. He has authored numerous textbooks and case studies, including works on research careers, operations management, and teaching methodologies. His work integrates human-centric approaches to operations and education systems. Research Interests include small business operations, education operations, theoretical economics, and the design of video games. He has published in top journals like Management Science, Econometrica, and Mathematical Programming. His grants include SSHRC and NSERC awards. Ryan advises students across multiple institutions and has mentored prominent faculty in marketing, operations, and MIS fields. Teaching focuses on core operations management for MBA programs and pedagogical innovation. He has developed cases on AI in education, early childhood development, and small business sustainability. Ryan’s work bridges academic research with practical applications in business and education sectors.

• Optimization
• Game Theory
• Theoretical Economics
• +۷ مورد دیگر

Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Com. from McGill University, and both an M.Sc. and Ph.D. from the University of Montreal. His research focuses on operational research with applications in logistics, transportation, energy systems, and telecommunications. He is affiliated with several prestigious research centers including the Institute for Data Valorization (IVADO), the Trottier Energy Institute (IET), and the Interuniversity Research Center on Enterprise Networks, Logistics and Transport (CIRRELT). Professor Gendreau's research interests span operational research, with particular emphasis on stochastic optimization methods applied to transportation and logistics problems, energy systems management, and telecommunications. His work often addresses real-world challenges through mathematical modeling and algorithm development, with applications ranging from bike-sharing systems to emergency response planning and electricity grid management. The analysis of his recent publications reveals a strong focus on vehicle routing problems under uncertainty, maintenance optimization, and the integration of stochastic programming with machine learning techniques for improved decision making. Professor Gendreau has received numerous prestigious awards recognizing his contributions to the field of operations research. In 2022, he was named a Fellow of the International Federation of Operational Research Societies (IFORS). In 2010, he was awarded Fellow status by INFORMS (Institute for Operations Research and the Management Sciences). Most notably, in November 2015, he received the Robert M. Herman Lifetime Achievement Award from the Transportation Science and Logistics Society of INFORMS, which is considered the most prestigious distinction for operational researchers working in logistics and transportation. Throughout his career, Professor Gendreau has supervised 25 doctoral students and 18 master's students, contributing significantly to the development of the next generation of operations research experts. His research has been supported by numerous grants from organizations including NSERC (Natural Sciences and Engineering Research Council of Canada), with expertise recognized in Operational Research and Management Science (NSERC subject 1601) and Logistics (NSERC subject 1603). Professor Gendreau is actively involved in several research teams and laboratories, particularly those focused on data valorization, energy systems, and transportation logistics. His current work continues to push the boundaries of stochastic optimization and its applications to complex real-world problems, with recent publications addressing challenges in urban transportation, energy management, and emergency response systems.

• Operational Research
• Logistics
• Stochastic Optimization
• +۳ مورد دیگر

Dr. Amirali Abari is an Associate Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in Artificial Intelligence, Machine Learning, and Computational Decision Making. His research focuses on decision-making under uncertainty, including preference learning, group recommendation systems, and probabilistic inference. He holds a PhD in Computer Science from the University of Toronto (2016) and was an NSERC Postdoctoral Fellow at the University of Toronto and University of Waterloo. Dr. Abari's research has been recognized with awards including the NSERC Postdoctoral Fellowship and a U.S. patent. He serves on program committees for top AI conferences (e.g., AAAI, IJCAI) and reviews for journals like IEEE Transactions and ACM Transactions. His work bridges computational methods with real-world applications in supply chain optimization, risk management, and sustainable infrastructure planning. Key contributions include integrating machine learning with queueing theory for EV charging stations, stochastic optimization for resilient supply chains, and decision support systems for industrial processes. His research group collaborates with industry partners to apply AI-driven solutions to business analytics and operations challenges. Education: PhD in Computer Science, University of Toronto (2016) Awards: NSERC Postdoctoral Fellowship, Bell Scholarship, Student Best Paper Award Service: Program committees for AAAI, IJCAI; reviewer for IEEE, ACM, and Algorithmica

• Artificial Intelligence
• Machine Learning
• Computational Decision Making
• +۷ مورد دیگر

Dr. Javad Fattahi is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds roles as an IEEE Senior Member, Professional Engineer (P.Eng.), and member of the Standards Council of Canada. He leads the IEEE 2030.5 smart-grid interoperability verification initiative as its VP and chief editor. Dr. Fattahi earned his BSc in Electrical Engineering from Iran University of Science and Technology (IUT), followed by graduate studies at Sharif University of Technology (SUT). He completed his PhD and postdoctoral research at the University of Ottawa, specializing in energy systems and optimization. His research focuses on energy equity, distributed energy resources (DERs), software-defined nanogrids, game theory, smart mobility, and nonlinear systems. His recent work emphasizes data-driven digital twins for grid stability, V2G integration in residential complexes, and federated Byzantine agreement models for EV infrastructure. Key contributions include transactive energy systems under IEEE standards and energy disaggregation in institutional buildings. Dr. Fattahi has received accolades including IEEE Senior Membership and P.Eng. certification. His lab develops solutions for grid-edge technologies, smart mobility, and resilient energy systems. Graduate opportunities are available via his UniWeb page.

• Energy Equity and Inclusion
• Distributed Energy Resources (DER)
• Software-Defined Nanogrids
• +۶ مورد دیگر

Douglas G Down is a Professor in the Department of Computing and Software at McMaster University . He leads the Resource Allocation and Stochastic Systems Lab (RASSL) and serves as co-PI of the Computing Infrastructure Research Centre (CIRC) . Education: Ph.D., University of Illinois at Urbana-Champaign His research focuses on stochastic modelling , scheduling , and performance evaluation of computer systems, with recent work on intelligent control of data centers , thermal-aware workload management , and data-driven resource allocation for healthcare systems. Publications span queueing theory , energy-aware scheduling , and machine learning applications in logistics. Recent scholarly trends include deep learning for pandemics (2023-2025), stochastic optimization of cooling systems (2024), and hybrid models combining queueing theory with AI (2023). He has pioneered frameworks like MGST for scheduling in heterogeneous grids and contributed to cache locality improvements in Hadoop systems. Scientific Awards: Nominated for Best Paper at MASCOTS 2013

• Stochastic Modelling
• Queueing Networks
• Scheduling Algorithms
• +۴ مورد دیگر

Michael Reimer is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC). His primary role involves advancing quantum photonic technologies through experimental and theoretical research. He is a full-time faculty member with expertise in semiconductor nanowire-based devices, quantum dot systems, and their integration with atomic ensembles for quantum communication applications. His research interests focus on developing high-efficiency single-photon sources and detectors, metamaterials for photonic applications, and leveraging cold atoms in hollow-core fibers to enhance quantum state manipulation. He also explores hybrid quantum-classical systems, particularly in optimizing signal-to-noise ratios (SNR) and mitigating nonlinear impairments in optical networks. His work bridges fundamental quantum optics with practical implementations in quantum computing and secure communication systems. Recent articles highlight trends toward achieving near-unity absorption in metamaterials, on-demand photon generation with quantum dots, and probabilistic constellation shaping for optical networks. While no scientific awards are explicitly mentioned, his contributions to quantum photonic devices are notable. No specific grants or advisees are detailed in the provided text, but his affiliation with IQC suggests involvement in collaborative research projects. He is affiliated with the Institute for Quantum Computing, a leading academic unit at the University of Waterloo.

• Quantum Optics
• Quantum Photonics
• Semiconductor Nanowires
• +۳ مورد دیگر

Pirathayini Srikantha serves as Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering, holding a Tier 2 Canada Research Chair in Reliable and Secure Power Grid Systems with critical national security implications for Canada's power infrastructure. Her academic foundation includes: B.A.Sc. in Systems Design Engineering from University of Waterloo (2009) M.A.Sc. in Electrical and Computer Engineering from University of Waterloo (2013) Ph.D. from University of Toronto's Edward S. Rogers Sr. Department of Electrical and Computer Engineering (2017) Professor Srikantha's research centers on game theory, large-scale optimization, and distributed control to enable adaptive power grid operations. Her work addresses cybersecurity vulnerabilities in smart grids through distributed coordination frameworks and develops transactive energy market mechanisms that incentivize grid support during congestion. By integrating AI breakthroughs, she focuses on creating systems that automatically infer, predict, and respond to power disturbances while enhancing infrastructure resilience against evolving threats. Her publication trajectory reveals consistent innovation in grid security and optimization, with increasing emphasis on AI-driven solutions for grid resilience since 2015. Key thematic evolution shows progression from foundational distributed control (2015-2017) to sophisticated cybersecurity applications (2018-2021), consistently appearing in premier venues like IEEE Transactions on Smart Grid. Her scholarly impact is recognized through: Best paper award at IEEE Smart Grid Communications Runner-up best poster award at ACM Women in Computing As Canada Research Chair holder, she directs a federally funded research program addressing critical grid vulnerabilities, while serving as Associate Editor for IEEE Transactions on Smart Grid. Her work bridges theoretical advances in game-theoretic security with practical grid applications, though specific student mentorship details aren't publicly enumerated in source materials. Her research group operates at the intersection of electrical engineering and computer science, developing next-generation tools for grid operators through collaborations with Canadian energy infrastructure stakeholders.

• Game Theory
• Large-scale Optimization
• Distributed Control
• +۵ مورد دیگر

Dr. Fletcher Lu is an Associate Professor in the Department of Information Technology at the Faculty of Business and Information Technology, Ontario Tech University. His academic journey includes a PhD in Artificial Intelligence (specializing in Machine Learning) and a Master of Mathematics in Scientific Computation, both from the University of Waterloo. He also holds a Bachelor of Math in Computer Science from the same institution. His postdoctoral research at the Canadian Institute of Chartered Accountants (CIAC) focused on insurance fraud detection. His education includes: PhD in Artificial Intelligence, University of Waterloo Master of Mathematics in Scientific Computation, University of Waterloo Bachelor of Math in Computer Science, University of Waterloo Dr. Lu’s research interests span health informatics, fraud detection (including cybercrime and insurance fraud), and the application of machine learning and numerical analysis to business and health challenges. He has developed mobile technology applications to improve youth fitness and pioneered adaptive fraud pattern analysis in health insurance data. His work intersects with cybersecurity, data analytics, and gaming, emphasizing real-world collaborations with organizations such as Manulife Financial, the Canadian Department of National Defence, and Medavie Blue Cross. His recent publications highlight trends in supply chain resilience, optimization methodologies, and disruption risk analysis. These include studies on EV infrastructure planning, stochastic distribution networks, and Benders decomposition strategies for reliable systems design. Dr. Lu has received significant awards, including: First place at CORS 2014 Practice Prize Third place at INFORMS 2013 interactive poster sessions His research has been funded by the National Science Foundation, Ontario Partnership for Innovation and Commercialization, and NSERC. He is affiliated with the Business Analytics & AI Research Group, contributing to decision-making systems and data-driven solutions for business and health sectors.

• Health Informatics
• Fraud Detection
• Cybersecurity
• +۶ مورد دیگر

Erick Delage is a Professor in the Department of Decision Sciences at HEC Montréal, holding the Canada Research Chair in Decision Making Under Uncertainty. He is a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of MILA. His research focuses on optimization under uncertainty, robust and stochastic optimization, machine learning, and risk management, with applications in finance, energy systems, and transportation. Delage holds a Ph.D. in Electrical Engineering from Stanford University, where he worked with renowned scholars like Andrew Y. Ng and Yinyu Ye. His teaching includes courses on Quantitative Risk Management, Decision Analysis, and Robust Optimization at institutions like HEC Montréal, Politecnico di Milano, and EPFL. He has supervised numerous PhD and master's students, leading to impactful contributions in areas like distributionally robust optimization and deep reinforcement learning for financial engineering. Delage's work emphasizes bridging theory and practice, with notable contributions to contextual optimization methods, energy transition pathways, and risk-averse decision-making. His research has been recognized with awards such as the Nicholson Award (2008) and membership in the Royal Society of Canada's College of New Scholars (2020). His laboratories and collaborations include the Supply Chains and Mobility research cluster funded by IVADO, focusing on data-driven decision-making for resilient systems. Key grants include leadership in energy transition optimization and robust supply chain frameworks.

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +۷ مورد دیگر

Dr. Giancarlo Dalle Ave is an Assistant Professor in the Department of Chemical Engineering at McMaster University’s Faculty of Engineering. His research focuses on integrated energy systems, power systems engineering, and data-driven decision-making for industrial processes. He leads the Dalle Ave Lab , addressing challenges in energy sustainability, process optimization, and computational complexity. His work bridges academia and industry, with prior experience at ABB Corporate Research and Hitachi Energy Research. Education: Ph.D. (Technical University of Dortmund, 2022), M.A.Sc. (McMaster, 2016), B.Eng. (McMaster, 2014) Dr. Dalle Ave’s research integrates decision-making across process design, scheduling, and real-time control, emphasizing cost reduction and sustainability. Key areas include: Integrated Energy Systems: Design and optimization of multi-pathway energy systems (storage, biofuels, etc.) Power Systems Engineering: Economic dispatch, demand response, and grid-industry interactions Data-Driven Decision Making: IoT-enabled condition-based maintenance and alarm management His scientific contributions have been recognized through competitive awards, including the Marie Curie Doctorate Fellowship and Ontario Graduate Scholarship. He teaches CHEMENG 4A03/6A03 Energy Systems Engineering , covering energy resource systems, fuel cells, and alternative fuels.

• Energy systems
• Demand response
• Planning and scheduling
• +۶ مورد دیگر