جستجوی استاد راهنمای دانشگاهی

Shaon Bhatta Shuvoمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Shaon Bhatta Shuvo is an Assistant Professor at the University of Windsor's School of Computer Science. He holds a Ph.D. from the University of Windsor (2020–2024), an M.Sc. from South Asian University (2013–2015), and a B.Sc. from Noakhali Science and Technology University, Bangladesh. Research Interests: Deep Learning & Reinforcement Learning: Development and application of advanced algorithms for computer vision and natural language processing. AI-Based Decision Support Systems: Designing AI-driven systems for healthcare and social network analysis. Modelling and Simulation: Multi-agent simulations and mathematical modeling for complex systems, including pandemic preparedness and healthcare optimization. Research Trends in Publications: His work focuses on AI-driven solutions for healthcare (e.g., pandemic modeling, PPE demand prediction), social network analysis (link/node classification, knowledge graphs), and computational epidemiology. He emphasizes hybrid simulation models and data-driven approaches for real-world challenges. Awards: No scientific awards explicitly mentioned. Advising & Grants: No student advisees or grants listed. Labs/Teams: Not specified in available information.

• Evolutionary Computation
• Multi- and Many-Objective Optimization
• High-Dimensional Visualization
• +۳ مورد دیگر

Dr. Amin Ibrahim is an Associate Teaching Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in evolutionary computation, optimization, and machine learning. He holds a PhD and MASc in Computer Engineering from Ontario Tech University and a BASc in Computer Engineering from the University of Toronto. His research focuses on multi- and many-objective evolutionary algorithms, high-dimensional visualization, and applying optimization techniques to big data challenges. He has won two Teaching Excellence Awards at Ontario Tech University (2008 and 2018) and contributed to peer-reviewed journals, book chapters, and conferences. Education: PhD in Computer Engineering, Ontario Tech University MASc in Computer Engineering, Ontario Tech University BASc in Computer Engineering, University of Toronto Research interests span evolutionary algorithms, machine learning applications, and optimization-driven solutions for complex systems. Recent work includes electric vehicle infrastructure planning and resilient supply chain design. His articles address multi-objective optimization, risk management, and sustainable transportation. Awards and Honors: Teaching Excellence Award (2008, 2018) Graduate Continuing Scholarship (2008) Graduate Entrance Scholarship (2007) Professional Involvement: Session chair at IEEE World Congress on Computational Intelligence (2016), workshops on cybersecurity and supply chain management, and contributions to IBM CASCON (2008). Active in the Business Analytics & AI Research Group, focusing on transforming data into actionable insights for business decision-making. Teaches courses ranging from cryptography to discrete mathematics and business analytics.

Ali Elkamelمشاهده پروفایل

استاد مدعو

• Process Systems Engineering
• Energy Systems
• Environmental Engineering
• +۵ مورد دیگر

Ali Elkamel is an Adjunct Professor Emeritus at the University of Waterloo's Department of Chemical Engineering, Faculty of Engineering. His research focuses on Process Systems Engineering, Energy Systems, Environmental Engineering, and Carbon Management. He specializes in applying machine learning to optimize complex systems such as energy production, waste management, and material science. His work spans sustainability initiatives, including hydrogen production methodologies, municipal waste valorization, and eco-friendly grid modernization. He has also contributed to advancements in CO2 capture technologies and renewable energy integration. Elkamel's research often intersects with industrial applications, addressing challenges in petrochemical operations, membrane technology, and process modeling. Elkamel has published extensively on topics ranging from AI-driven education tools to novel materials for gas adsorption. His recent articles highlight trends in machine learning applications for process optimization, energy-water nexus solutions, and sustainable infrastructure design. Despite his prolific output, no formal awards or student advisement records are noted. His academic profile reflects a commitment to bridging theoretical research with practical industrial solutions, particularly in the energy and environmental sectors. Collaborations likely extend to interdisciplinary teams given his diverse research portfolio.

Marc St.-Hilaireمشاهده پروفایل

استاد

• Telecommunication network planning
• Wired and wireless communication networks
• Mobile computing
• +۱۲ مورد دیگر

Marc St.-Hilaire is a Professor at the School of Information Technology and cross-appointed to the Department of Systems and Computer Engineering at Carleton University within the Faculty of Engineering and Design . He holds a Ph.D. from École Polytechnique de Montréal and serves as the NET Program Coordinator. He is a Senior Member of IEEE and has received multiple awards, including the Carleton Faculty Graduate Mentoring Award and the Teaching Achievement Award. Education: Ph.D., École Polytechnique de Montréal His research centers on telecommunication network planning, mobile computing, and network optimization , with strong emphasis on wireless and vehicular networks, fog/edge computing, blockchain integration, and AI-driven network protocols . His recent work applies reinforcement learning, genetic algorithms, and fuzzy logic to solve challenges in dynamic and distributed environments. The trend in his recent publications shows a strong focus on smart infrastructure , including Internet of Vehicles (IoV), smart grids, and cloud/edge resource management . He frequently collaborates with students and researchers on topics such as virtual network embedding, SLA-aware provisioning, and cooperative positioning , often leveraging emerging technologies like blockchain and deep learning. Scientific Awards and Honors: Senior Member, IEEE Best Industry Paper Award, WF-IoT 2024 Best Paper Award, ADHOCNETS 2019 Best Paper Award, iThings 2018 IEEE WIE Best Paper Award, CCECE 2018 Best Paper Award, ADHOCNETS 2017 Carleton Faculty Graduate Mentoring Award, 2014 Carleton Teaching Achievement Award, 2014–2015 Dr. St.-Hilaire actively supervises a large team of graduate students and has mentored over 40 Ph.D., Master’s, and postdoctoral researchers to completion. He has secured significant research funding through industry and government grants, enabling extensive experimental testbeds in SDN, fog computing, and vehicular networks. His work bridges theoretical optimization with practical implementation, often releasing tools and simulators (e.g., NetAnalyzer, DEVS fog simulator). He leads a vibrant research team focused on network intelligence, edge-based complex event processing, and sustainable computing . His lab collaborates with industry partners on projects involving 5G/6G integration, TSN in cloud environments, and smart city applications such as cloud-based waste management and smart grid simulation.

Dhirendra Shuklaمشاهده پروفایل

استاد

• Entrepreneurial Finance
• Corporate Governance
• Venture Capital
• +۷ مورد دیگر

Dr. Dhirendra Shukla is the Dr. J. Herbert Smith ACOA Chair at the University of New Brunswick's J Herbert Smith Centre for TME. He holds a PhD in Entrepreneurial Finance from King's College London, an MBA from the Telfer School of Management (University of Ottawa), and advanced engineering degrees from the University of Bradford. His research focuses on venture capital, corporate governance, entrepreneurial finance, and sustainability in renewable energy and technology-driven industries. Education: BEng/MSc in Chemical Engineering & Computing (University of Bradford) MBA (University of Ottawa/Telfer School) PhD in Entrepreneurial Finance (King's College London) His teaching portfolio includes courses on entrepreneurial finance, global engineering, and leadership in technology management. Research interests span venture capital dynamics, IPO governance, blockchain applications, and smart energy systems. He has presented at conferences like the Strategic Management Society and Academy of International Business, contributing to cross-national studies on corporate governance and IPO performance. Shukla's work bridges academia and industry, with projects addressing renewable energy partnerships in Atlantic Canada and federated learning systems for privacy-preserving data analytics. His recent studies explore AI-driven digital twins, cyber-physical systems in smart cities, and blockchain adoption in higher education.

Ursula Eickerمشاهده پروفایل

استاد

• Urban energy systems
• 3D City modeling
• Urban data and simulation platform development
• +۷ مورد دیگر

Prof. Ursula Eicker is the Canada Excellence Research Chair in Smart, Sustainable and Resilient Cities and Communities at Concordia University , leading cutting-edge research in urban energy systems. Her work integrates 3D city modeling, renewable energy systems, and sustainable transport to develop zero-carbon city strategies. PhD in Solid State Physics (Heriot-Watt University) Habilitation in Renewable Energy Systems (Technische Universitat Berlin) Research Interests focus on urban simulation platforms, district energy networks, and climate-resilient infrastructure. The INSEL4Cities platform enables holistic urban modeling for building demands, transportation, and greenery. Her Residential Densification studies demonstrate 65% energy reduction through retrofits and solar integration. Recent publications explore urban solar shading , transactive energy systems , green infrastructure equity , and decentralized hydrogen production . Awards include the German-African Innovation Incentive and recognition for photovoltaic research in Egypt. Over 50 graduate students in her lab examine zero-carbon pathways. Teaches ENCS 691 on urban energy systems. Secured 10M CAD for the CERC chair and multiple grants. Co-Director of Concordia's Next Generation Cities Institute .

• Structural loads and safety
• Concrete materials and structures
• Structural analysis
• +۳ مورد دیگر

Pierre Léger is a Full Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. He holds a B.Sc.A. from the University of Ottawa, M.Sc. from Université Laval, D.S.A. from McGill University, and Ph.D. from UC Berkeley. His primary research focuses on structural safety assessment of concrete dams and hydraulic infrastructure, earthquake engineering, and computational mechanics. His research interests include: Methodologies for structural safety evaluation and rehabilitation of concrete dams Nonlinear static and dynamic analysis of hydraulic structures Constitutive modeling of mass concrete cracking Seismic strengthening techniques for buildings and infrastructure Development of computational tools for civil engineering structures Analysis of his recent publications reveals strong emphasis on: Advanced finite element methods for dam safety assessment Seismic vulnerability of hydraulic structures 3D modeling of shear keys and joint behavior Computational fluid dynamics applications in structural stability Innovations in damping identification and energy dissipation This reflects his leadership in integrating computational mechanics with practical structural engineering challenges. Major scientific awards include: 2023 : Horst Leipholz Medal (CSCE), Inge Anderson Award (CDA) 2019 : Canadian Academy of Engineering Fellow 2018 : Engineering Institute of Canada Fellow 2011 : Excellence in Teaching Award 2008 : E. Whitman Wright Award He has supervised 41 graduate students (6 PhD, 35 Master's) with thesis topics spanning dam safety, seismic analysis, and structural rehabilitation. Research affiliations include: Centre d’études interuniversitaire des structures sous charges extrêmes (CEISCE) Groupe de recherche en génie des structures (GRS)

Yuvin Adnarain Chinniahمشاهده پروفایل

استاد

• Occupational Health and Safety
• Industrial Safety
• Machine Safety
• +۵ مورد دیگر

Dr. Yuvin Adnarain Chinniah serves as a Full Professor and Department Director in the Department of Mathematical and Industrial Engineering at Polytechnique Montreal. His expertise spans occupational health and safety, industrial safety engineering, and machinery risk assessment. He actively contributes to the Research Group on Globalisation and Management of Technology (GMT) and Laboratoire Poly-Industries 4.0. Dr. Chinniah's research focuses on occupational health and safety with particular emphasis on machinery safety, risk assessment methodologies, human-robot collaboration, and safety standards implementation. His work bridges theoretical frameworks with practical applications in industrial settings, addressing critical issues like confined space safety, lockout/tagout procedures, and safety in Industry 4.0 environments. His research integrates ISO and CSA standards into practical safety solutions for industrial machinery. His recent publications demonstrate a clear trajectory from traditional machine safety toward advanced applications involving robotics, digital transformation, and human-robot collaboration. The research consistently addresses safety optimization in manufacturing processes, with increasing focus on cyber-physical systems, AI integration for hazard detection, and safety-driven production planning. His work maintains strong connections to industrial standards while pushing boundaries in collaborative robotics safety. Dr. Chinniah has successfully supervised 9 Ph.D. students and 10 Master's students, with thesis topics covering confined space safety, lockout/tagout procedures, human-robot collaboration safety, and machinery risk assessment. His teaching portfolio includes key courses in safety engineering, ergonomics, and maintenance engineering. He leads significant research projects related to occupational safety in industrial settings, with particular focus on machinery safety standards implementation and risk assessment methodologies.

• Process Systems Engineering
• Chemical Process Optimization
• Hybrid Modeling
• +۷ مورد دیگر

Vladimir Mahalec is a Professor in the Department of Chemical Engineering within the Faculty of Engineering at McMaster University. With a career spanning over four decades, his scholarly activity shows consistent publication output from 1977 through 2025, with particular intensity in recent years. He maintains an active teaching schedule, offering graduate courses including Systems Modeling and Optimization (CHEMENG 753, SEP 752), Leadership for Innovation (SEP 773), and M.Eng. Project courses. Professor Mahalec's research interests focus on the intersection of chemical engineering process optimization, hybrid modeling techniques, and sustainable energy systems. His work bridges traditional chemical engineering with modern computational approaches, particularly in refinery planning, distillation tower modeling, and carbon footprint reduction. He has developed innovative methodologies such as inventory pinch algorithms for gasoline blend planning and hybrid models combining first-principles with data-driven approaches for process monitoring and optimization. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional process engineering. His work increasingly focuses on sustainability challenges, particularly zero-emission energy systems and greenhouse gas reduction strategies. The publications show a progression from purely process engineering topics toward interdisciplinary work combining chemical engineering, computer science, and environmental science. Professor Mahalec has maintained a productive research program with consistent publication output across multiple high-impact journals including Computers and Chemical Engineering , AIChE Journal , and Energy . His co-author network includes notable researchers such as Prashant Mhaskar (8 publications), Robert Fleisig, and Christopher Swartz. In the classroom, Professor Mahalec teaches advanced courses focused on systems modeling, optimization, and innovation leadership. His teaching portfolio demonstrates commitment to both technical depth in chemical engineering systems and broader skills development for engineering leadership. He has supervised numerous capstone design projects and M.Eng. projects, guiding students through complex process engineering challenges.

• Transfusion Medicine
• Hematology
• Biostatistics
• +۴ مورد دیگر

Na Li is an Adjunct Assistant Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her interdisciplinary research bridges computational methods with clinical hematology through extensive collaborations with transfusion medicine experts. Key contributions in biostatistical modeling for immune thrombocytopenia diagnosis Innovations in healthcare logistics for blood product management Pioneering AI-human collaboration frameworks for electronic health record analysis Her scholarly work spans machine learning applications in disease detection, operational research for blood inventory optimization, and clinical decision support systems development. Recent publications demonstrate integration of large language models with medical expertise for improved diagnostic accuracy. Notable research themes include: Pandemic resource allocation fairness Perioperative anticoagulant management Platelet transfusion analytics Blood product demand forecasting Privacy-preserving federated learning Her work has been cited extensively across transfusion medicine , critical care , and healthcare technology domains, with implementations in major clinical trials and policy documents.

Elena Dragomirescuمشاهده پروفایل

دانشیار

• Wind Engineering
• Structural Reliability
• Bridge Engineering
• +۵ مورد دیگر

Elena Dragomirescu is an Associate Professor in the Department of Civil Engineering at the University of Ottawa's Faculty of Engineering. She joined the department in 2008 after research positions at Yokohama National University (2004) and Nagoya University (2006), establishing expertise in structural reliability and wind engineering for bridges and infrastructure. Her educational background includes: Ph.D. in Civil Engineering from Yokohama National University, Japan M.A.Sc. in Civil Engineering from University of Bucharest, Romania (UNESCO Chair Program) B.Sc. in Civil Engineering from University of Bucharest, Romania Dr. Dragomirescu's research focuses on wind effects on structures , particularly aerodynamics of suspension bridges and structural reliability . She develops probabilistic methods for predicting wind-induced fatigue in slender structures using computational wind engineering (CFD) and aerodynamic formulations. Current projects address moisture ingress in building envelopes and machine learning applications for structural health monitoring, reflecting adaptation to climate resilience challenges. Analysis of her 2021-2025 publications reveals dual research thrusts: (1) moisture dynamics in building envelopes across Canadian climates, and (2) deep learning (LSTM/transformers) for structural assessment of bridges/beams. This strategic integration of data-driven methods with core wind engineering expertise demonstrates responsiveness to infrastructure resilience demands while maintaining theoretical rigor in probabilistic structural dynamics. She has received numerous scholarships and awards throughout her career, though specific honors are not detailed in available sources. Professional service includes membership in the Steering Committee for Japanese National Wind Engineering Guidelines (2008-2010) and active roles in JAWE, JSCE, CSCE, and IAWE. As coordinator of the International Exchange Program for Students, Dr. Dragomirescu facilitates global academic partnerships. Her research has been supported through international collaborations with Ecoland and UNESCO, and she regularly serves as a journal reviewer. Future work appears directed at machine learning applications for real-time structural integrity assessment under extreme winds, with implications for bridge safety standards and climate-adaptive building design. Her laboratory work involves wind tunnel testing and computational simulations for aerodynamic studies of bridge components (cable stays, multi-box decks) and building envelope systems, complemented by experimental validation of structural dynamics phenomena.

Bala Venkateshمشاهده پروفایل

استاد

• Power Systems Analysis
• Optimization
• Fuzzy Optimization
• +۴ مورد دیگر

Dr. Bala Venkatesh is a Professor in the Department of Electrical and Computer Engineering and Academic Director of the Centre of Urban Energy at Toronto Metropolitan University. He has held academic positions at institutions including Ryerson University (2008–present), the University of New Brunswick (2005–2007), Multimedia University, Malaysia (2001–2004), and Anna University, India (1994–2000). His research focuses on Power Systems Analysis, Fuzzy Optimization, and renewable energy integration. He has authored over 100 refereed papers and secured research grants exceeding $10 million. Dr. Venkatesh is a Senior Member of IEEE and a registered Professional Engineer in Ontario. Education and Professional Experience: Postdoctoral work at Crompton Greaves, Mumbai (1993) Lecturer, Anna University, India (1994–2000) Associate Professor, Multimedia University, Malaysia (2001–2004) Associate Professor, University of New Brunswick (2005–2007) Research Interests: Dr. Venkatesh specializes in fuzzy optimization applications to power systems, distribution network analysis, and energy storage integration. His work emphasizes practical solutions for grid stability, renewable energy utilization, and transactive energy systems. Recent projects include DER aggregation models, smart grid optimization, and fault diagnosis in microgrids. Grants and Recognition: He leads the Power and Energy Analysis Research Lab at Toronto Met and has secured substantial funding for projects in smart grids and energy systems. Notable awards include IEEE Senior Membership and Professional Engineer licensure in Ontario. His work has been applied in consulting projects across India and Canada.

• Machine Learning
• Deep Learning
• Big Data Analytics
• +۳ مورد دیگر

Asma ul Husna is an Assistant Professor in Business Analytics at the Shannon School of Business, Cape Breton University. Her research focuses on integrating machine learning with optimization techniques to enhance supply chain management and logistics. Her work explores Machine Learning , Deep Learning , and Big Data Analytics to develop novel optimization models for supply chain processes. She has published extensively on supplier selection, order allocation, and demand forecasting. Her recent publications demonstrate expertise in applying clustering algorithms and multi-objective programming to supply chain challenges, with a focus on improving efficiency through data-driven decision-making in both retail and manufacturing contexts.

Sonja Ursula Katharina Rohmerمشاهده پروفایل

استادیار

• Operations Research
• Sustainable Supply Chains
• (Agri-)food systems
• +۳ مورد دیگر

Sonja Ursula Katharina Rohmer is an Assistant Professor in the Department of Logistics and Operations Management at HEC Montréal. She holds a Ph.D. in Operations Research and Logistics from Wageningen University, an M.Res. in Computer Aided Decision Support from Polytech Tours, and a B.A. in Management Science and Economics from the University of Strathclyde. Her research focuses on Operations Research applications in sustainable supply chains, agri-food systems, and logistics optimization. Key areas include food waste management, perishable product routing, stochastic inventory dynamics, and collaborative carrier strategies. Recent work emphasizes environmental and economic trade-offs in perishable goods distribution and food bank logistics. Rohmer has taught courses such as Operations Management, Sustainable Logistics, and Planning and Control of Logistics Systems since 2023. She has supervised three master's projects addressing challenges in Agriculture 4.0 adoption, aircraft parts supply chains, and rice manufacturing capacity planning. Her research collaborations span institutions like Wageningen University, KU Leuven, and Polytechnique Montréal. Publications highlight innovative approaches to inventory routing, vehicle synchronization, and cooperative game theory in logistics. Current projects involve optimizing agri-food supply chains using data-driven robust planning methods to reduce food waste.

Jinhyung Leeمشاهده پروفایل

استادیار

• Transport geography
• Spatial accessibility
• Urban resilience
• +۳ مورد دیگر

Jinhyung Lee is an Assistant Professor in the Department of Geography and Environment at Western University. His research focuses on transport geography, spatial accessibility, and urban resilience, leveraging GIScience, AI, and big data to address equity and sustainability in urban systems. He teaches courses such as Transportation Geography & GIS and Geographic Data Science with Python. His work integrates real-time transit data, climate change impacts, and generative AI to improve public transit systems and urban planning. He actively supervises graduate students in topics like equity-focused accessibility analysis and AI applications in transportation. Recent publications highlight innovations in measuring urban mobility, climate resilience, and the use of large language models for geographic analysis. Lee's research emphasizes social equity in transportation accessibility, particularly through projects analyzing extreme weather exposure, transit reliability, and Opioid Treatment Program accessibility. He collaborates on interdisciplinary initiatives, including the development of the Spnaf R package for flow data analysis and the OpenGPS platform for reproducible GPS studies. His work bridges methodological innovation with practical policy implications, aiming to create equitable and resilient urban environments.