جستجوی استاد راهنمای دانشگاهی

Dr. Khurram Aziz is a Senior Instructor in the Faculty of Computer Science at Dalhousie University , Halifax, Canada. He is actively engaged in teaching and research, with a focus on optical networks, data center interconnects, and network performance modeling. Education: PhD in Electrical Engineering, Vienna University of Technology, Austria (2008) MSc in Electrical Engineering, National University of Singapore (2003) BSc (Hons) in Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan (1998) His research interests include optical packet and burst switched networks , optical interconnects for data centers , analytical modeling and simulation , and network routing and switching . He has contributed extensively to the design and performance evaluation of scalable optical switches and hybrid switching systems. The recent publications reflect a strong trend in data center optical networks , focusing on performance, blocking probability, signal degradation, and architectural classification. His work bridges theoretical modeling with practical simulation frameworks, such as CloudNetSim++ in OMNeT++, contributing to cloud and high-capacity network research. Dr. Aziz has no listed scientific awards in the provided text. He teaches several core computer science courses including CSCI 2141: Intro to Database Systems , CSCI 3171: Network Computing , CSCI 3132: Object Orientation and Generic Programming , and CSCI 3120: Operating Systems . There is no mention of graduate student supervision or external research grants. He has co-authored book chapters in major handbooks on data centers and switched systems. Dr. Aziz has not listed any formal lab or research team affiliations in the provided content.

• Optical Networks
• Data Center Networks
• Optical Interconnects
• +۷ مورد دیگر

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

• Operations Management
• Supply Chain Management
• Sharing Economy
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Philipp Afeche is a Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. His research bridges operations and marketing/economics, focusing on revenue management, pricing strategies, and service design in congestion-prone systems like healthcare and transportation. He holds a BA from the University of St. Gallen and MS/PhD degrees from Stanford University. Afeche has been recognized with the 2014 Best Paper Award (MSOM) and the 2018 Roger Martin Teaching Award. Education: BA, University of St. Gallen, Switzerland MS, Stanford University, USA PhD, Stanford University, USA Research Interests: Afeche explores optimization challenges in dynamic service systems, including pricing under uncertainty, strategic customer behavior in queues, and platform design for shared mobility systems. His work integrates queueing theory, game theory, and empirical analysis to address real-world operational inefficiencies in healthcare delivery and transportation networks. Recent studies focus on ride-hailing market mechanisms and bipartite matching systems. Awards: 2014 Best Paper Award, Manufacturing & Service Operations Management 2018 Roger Martin Award for Excellence in Teaching Grants & Editorial Roles: Editor for Management Science and Operations Research, with funding reviews for agencies in Canada, Hong Kong, Israel, and the US. Past chair of the Service Management SIG for MSOM Society. Labs/Teams: Active in Rotman's Operations Management group and collaborates with industry partners on supply chain optimization and revenue management projects.

• Operations Management
• Revenue Management
• Supply Chain Management
• +۵ مورد دیگر

Yuanzhu Chen is a Professor in the School of Computing at Queen’s University, affiliated with the Faculty of Arts and Science. He previously served as Professor and Department Head at Memorial University of Newfoundland (2005–2021). His research focuses on computer networking, mobile computing, complex networks, and applied machine learning, emphasizing wireless innovation beyond traditional wired systems. He holds a PhD from Simon Fraser University (2004) and a B.Sc. from Peking University (1999). Education: PhD in Computing Science (Simon Fraser University, 2004); B.Sc. in Computer Science (Peking University, 1999). Earlier roles include Post-doctoral Researcher at Simon Fraser University (2004–2005) and leadership positions at Memorial University, including Department Head (2019–2021). Research Interests: Network Coding and Opportunistic Routing Mobile and Wireless Network Protocols Complex Network Analysis Machine Learning Applications Indoor Positioning Systems Social Network Dynamics Selected Awards: Recipient of Queen’s University President's Award for Distinguished Teaching. Lab Affiliation: Director of the Wireless Networking and Mobile Computing Lab (WineMocol). Active in collaborative projects involving smartphone sensors, community-based environmental monitoring, and stock market prediction using web data.

• Computer Networking
• Mobile Computing
• Complex Networks
• +۵ مورد دیگر

Dr. Saiedeh Razavi is an Associate Professor and the inaugural Chair in Heavy Construction at McMaster University's Department of Civil Engineering, directing the McMaster Institute for Transportation and Logistics (MITL). She holds a multidisciplinary background with degrees in Computer Engineering (B.Sc., Sharif University), Artificial Intelligence (M.Sc., Iran), and Civil Engineering (Ph.D., Waterloo). Her research focuses on smart infrastructure, connected mobility, and construction safety, funded by NSERC and the Ontario Ministry of Transportation. Key areas include transforming construction management through AI, autonomous vehicles, and smart work zones. Education: B.Sc. (Sharif), M.Sc. (Iran), Ph.D. (Waterloo) Research Interests: Smart cities, connected vehicle systems, data fusion, risk analysis, and sustainable logistics Leadership Roles: Director of MITL, Associate Chair (Research), and lead of national/international multidisciplinary projects Her work bridges academia, government, and industry to enhance mobility and safety. Notable grants include NSERC funding for transformative transportation systems. Awards include teaching excellence and innovation in team-based projects. Grants & Projects: NSERC, Ontario Ministry of Transportation, and industry collaborations Labs/Teams: MITL, CPS-based construction safety initiatives, and autonomous vehicle research groups

• Smart Infrastructure
• Intelligent Transportation Systems
• Construction Management Automation
• +۵ مورد دیگر

Dr. Indratmo is an Associate Professor and Chair of the Department of Computer Science at MacEwan University. He holds a PhD from the University of Saskatchewan, an M.Sc. from the University of Manitoba, and a B.Eng. from Petra Christian University. His research focuses on information visualization, human-computer interaction, and social computing, with a particular emphasis on developing tools for analyzing social media data. He has contributed to projects like a visual analytical tool for sentiment analysis in Edmonton's traffic-related social media data and studies on multimedia content effectiveness in communication strategies. Indratmo teaches a range of computer science courses, emphasizing student engagement through transparent pedagogical practices. His work bridges technical innovation with social impact, aiming to enhance communication strategies for organizations through data-driven insights. He has published extensively in journals like Big Data Research and Visual Informatics , and his research spans topics from educational visualization tools to smart mirror applications and geospatial heritage systems. Outside academia, he enjoys outdoor activities in the Canadian Rockies. Notable collaborations include work on stacked bar chart efficacy, web-based course registration models, and exploratory browsing frameworks. His research portfolio demonstrates a commitment to both theoretical advancement and practical applications in computing.

• Information Visualization
• Human-Computer Interaction
• Social Computing
• +۳ مورد دیگر

Professor Chris Lee is a faculty member in the Department of Transportation Science and Engineering at the University of Windsor's Faculty of Engineering. His research focuses on advancing transportation safety through the analysis of driver behavior, traffic flow dynamics, and the integration of emerging technologies like autonomous vehicles and machine learning. Key areas include collision risk prediction, driver vigilance assessment, and the development of advanced car-following models. He has contributed to initiatives such as the Transportation Science and Engineering scholarship program, supporting student research in innovative technologies like driving simulators for lane change behavior studies. His work bridges engineering and human factors, addressing challenges such as driver response to autonomous systems, heavy vehicle traffic management, and cross-cultural automotive design. Lee's interdisciplinary approach leverages data analytics, physiological signals, and machine learning to solve real-world transportation problems. His research has implications for policy-making, infrastructure design, and vehicle safety standards. Lee has collaborated extensively on projects analyzing crash precursors, variable speed limits, and the impact of ITS (Intelligent Transportation Systems) on safety. His publications span over two decades, demonstrating a commitment to both academic rigor and practical applications in transportation engineering. Notable contributions include refining car-following models, studying driver aggression, and evaluating the effectiveness of traffic management strategies.

• Transportation Engineering
• Traffic Safety
• Driver Behavior Analysis
• +۵ مورد دیگر

Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.

• Cloud Computing Resource Management
• Software Defined Networking (SDN)
• Traffic Management and Quality-of-Service
• +۳ مورد دیگر

J. Ian Munro is a University Professor and Canada Research Chair in Algorithm Design at the Cheriton School of Computer Science, University of Waterloo. He has been a faculty member at Waterloo since completing his PhD at the University of Toronto in 1971 and was appointed University Professor in 2006, one of the highest honors the university bestows on its faculty. Dr. Munro's research focuses on the design, analysis, and implementation of efficient algorithms and data structures, with particular emphasis on space-efficient solutions. His work spans several key areas including succinct data structures, tree algorithms, text search methods, priority queues, and memory management for data structures. His research has had significant impact on how we understand the theoretical limits of data representation while maintaining efficient query performance. The trajectory of his recent publications (2020-2024) reveals a continued focus on pushing the boundaries of space efficiency in data structures, particularly for graphs and trees. His work increasingly addresses dynamic scenarios where data structures must be updated efficiently while maintaining query performance. The research shows strong connections between information theory, combinatorics, and practical algorithm design, with applications spanning database systems, computational geometry, and text processing. Fellow of the Association for Computing Machinery (FACM) Elected Fellow of the Royal Society of Canada (FRSC) in 2003 Canada Research Chair in Algorithm Design Dr. Munro has supervised over 24 PhD students throughout his career and has been actively involved in teaching advanced courses on data structures and algorithms. His research has been consistently funded through competitive grants, including his Canada Research Chair position. He maintains active collaborations with researchers worldwide, as evidenced by his extensive co-authorship network across numerous publications. As a member of both the Algorithms and Complexity Group and the Data Systems Group at Waterloo, Dr. Munro contributes to interdisciplinary research that bridges theoretical computer science with practical systems challenges. His work continues to influence both theoretical understanding and practical implementations of efficient data management solutions.

• Data Structures
• Space Efficient Data Structures
• Algorithm Design and Analysis
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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

Jorg Liebeherr is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, holding the Nortel Chair of Network Architecture and Services. His research focuses on computer networks , particularly network calculus , self-organizing networks , protocol design , and traffic scheduling . Education: Diplom-Informatiker (with distinction), University of Erlangen (Germany), 1988 PhD, Computer Science, Georgia Institute of Technology, 1991 His recent work includes low-cost LoRa mesh networks for environmental sensing and mathematical frameworks for traffic control in 5G and IoT systems. Publications span journals like IEEE Internet of Things Journal and conferences such as IEEE Infocom and ACM Sigmetrics . Scientific Awards: IEEE Fellow (2008) Outstanding Service Award, IEEE ComSoc TC on Computer Communications (2006) ACM Sigmetrics Best Student Paper Award (2005) NSF CAREER Award (1996) Advising and Grants: Supervised 15+ theses (MASc/PhD) and secured grants from NSF, Virginia Engineering Foundation, and industry partners. Labs: Leads the Network Research Lab and HyperCast projects, an open-source platform for application-layer internetworking.

• Computer Networks
• Network Calculus
• Internet of Things (IoT)
• +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

Professor Yiqiang Q. Zhao is a faculty member at the School of Mathematics and Statistics, Carleton University, where he has served as Associate Dean (Research and Graduate Studies) of the Faculty of Science since 2021. His research focuses on applied probability, stochastic processes, and their applications in telecommunication networks, queueing systems, and Gaussian processes. With over 130 peer-reviewed publications and 150+ students supervised since 2000, he has received the Carleton Faculty of Science Teaching Award (2002-2003) and twice been recommended as the 'Most Welcomed Teacher' by graduate students. He has also held editorial roles for journals like Stochastic Models and Queueing Systems . Dr. Zhao's research explores exact tail asymptotics in queueing systems, mean-field interaction models, copula constructions, and statistical inference for stochastic processes. His work spans wireless network modeling, resource allocation, and geometric methods in probability, with funding from NSERC and industry partners like Alcatel and MITACS. His recent publications highlight advancements in mean-field stability, retrial queue approximations, and kernel methods for multidimensional queueing analysis. Editorial board memberships and committee leadership roles underscore his contributions to academic governance. Scientific Awards: Carleton Faculty of Science Teaching Award (2002-2003) Most Welcomed Teacher recognition (twice) Dr. Zhao collaborates with international institutions and supervises a dynamic research team. His grants from NSERC and industry partnerships reflect his impact on applied probability research.

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +۶ 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

Dr. Amir Rastpour is an Associate Professor of Operations Management at the Faculty of Business and Information Technology (FBIT), Ontario Tech University. He holds a PhD in Operations Management from the University of Alberta School of Business (2015), followed by a postdoctoral fellowship at the Ivey Business School, Western University (2017). His research focuses on applying operations research methodologies to healthcare challenges, including queuing theory, stochastic models, and big data analytics for optimizing healthcare operations. Specific interests include demand prediction for medical facilities, emergency medical services optimization, and quasi-birth-death (QBD) process analysis in healthcare systems. Education: PhD in Operations Management, University of Alberta School of Business (2015) Postdoctoral Fellow, Ivey Business School, Western University (2017) Visiting PhD Student, University of Chicago Booth School of Business (2014) Research Interests: Dr. Rastpour’s work emphasizes healthcare applications, including demand forecasting for heart attack treatment facilities, emergency medical services optimization, and QBD process modeling. His methods include queuing theory, count regression models, and empirical analysis to address both strategic and operational challenges in healthcare delivery. Recent work explores machine learning for predicting patient wait times and dynamic programming for ambulance fleet management. Awards: Nominated for Ontario Tech University Student Choice Teaching Award (2019) INFORMS Health Applications Society Finalist for Best Student Paper (2015) CORS Best Student Paper in Open Category (2015) University of Alberta Scholarships and Awards (2009–2014) Teaching and Advising: Dr. Rastpour teaches statistics and operations management courses, emphasizing hands-on data analysis and decision-making skills. His teaching style includes real-world examples and interactive sessions to enhance student learning. He collaborates on grants focused on healthcare operations and supply chain resilience. Labs and Teams: He is affiliated with the Business Analytics & AI Research Group at FBIT, which bridges computational methods with business challenges in marketing and operations management. His work contributes to initiatives like AI in healthcare collaboration with Lakeridge Health.

• Operations Research
• Health Care Sector
• Stochastic Models
• +۵ مورد دیگر