Distributed Cloud/Edge Computing Professors and Supervisors in Canada | ApplySharif

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Benoit ChampagneView profile

Professor

McGill UniversityCanada

Benoit Champagne is a Full Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal. His research focuses on statistical signal processing, with applications in wireless communications, multi-antenna systems, and adaptive filtering. He has held academic positions since 1990, including roles at INRS-Telecom before joining McGill in 1999. He teaches graduate and undergraduate courses such as ECSE 305 (Probability and Random Signals), ECSE 512 (Digital Signal Processing), and ECSE 617 (Array Signal Processing). Education: B.Eng. (Electrical Engineering) and M.Sc. (Physics) from Université de Montréal (1983, 1985), Ph.D. in Electrical Engineering from University of Toronto (1990). His research spans signal detection/estimation, speech enhancement, MIMO systems, and physical layer security, with over 150+ publications in top journals and conferences. He has supervised numerous graduate students and holds grants from NSERC, CFI, and industry partners like Nortel and Bell Canada. His work emphasizes practical implementations, including hybrid analog/digital beamforming for mmWave systems and energy-efficient resource allocation in D2D communications. He has contributed to IEEE standards through editorial roles (e.g., IEEE Transactions on Signal Processing) and conference organization (e.g., IEEE VTC 2016). Current research explores machine learning integration with signal processing for next-generation wireless systems. Notable contributions include advancements in subspace tracking, cognitive radar systems, and distributed adaptive filtering. His lab collaborates internationally, addressing challenges in 5G/6G networks, massive MIMO, and secure communications.

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Wei ShiView profile

Professor

Carleton UniversityCanada

Professor Wei Shi is a faculty member at the School of Computer Science, Carleton University. His research focuses on distributed computing, cloud networks, algorithm design for sensor/actuator systems, big data analytics, and data privacy. Notable projects include federated learning optimizations, blockchain-enabled edge intelligence for IoT/vehicle networks, and AI-driven cybersecurity solutions. His work addresses challenges in dynamic resource allocation, anomaly detection, and privacy-preserving techniques. Research Interests: Distributed Computing: Optimizing federated learning and client selection algorithms for wireless networks. Blockchain & Edge Intelligence: Developing decentralized systems for IoT and vehicular networks using AI large models. Security & Privacy: Innovating methods to detect AI-generated content, combat cyber-physical attacks, and protect user data privacy. Publications: Recent works emphasize federated learning applications, blockchain integration with edge computing, and cybersecurity for vehicular/IoT ecosystems. Key themes include energy-efficient algorithms, dynamic resource allocation, and intrusion detection in constrained environments. Email: wei.shi@carleton.ca

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Baochun LiView profile

Professor

Queen's University at KingstonCanada

Baochun Li is a Professor and Associate Chair, Research at the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto , with a cross-appointment to the Department of Computer Science. He holds the Bell Canada Endowed Chair in Computer Engineering since 2005. Education: B.Engr. from Tsinghua University (1995), M.S. and Ph.D. from University of Illinois at Urbana-Champaign (1997, 2000) His research interests span cloud computing , distributed systems (including federated learning), security and privacy , and networking , often integrating control theory , game theory , and network coding into practical systems. Recent work focuses on asynchronous federated learning , secure mechanisms for UAV teams , and transformer-based distributed inference . His 15 most recent publications emphasize federated learning , security in distributed systems , and transformer optimization , reflecting trends in large language models and edge computing . Awards and Recognitions: IEEE Fellow (2014) IEEE INFOCOM Achievement Award (2024) Fellow of the Canadian Academy of Engineering (2023) University of Toronto McLean Award (2009) He has contributed extensively to teaching , including launching the first Canadian university course on Rust programming in Fall 2024 and receiving a Departmental Teaching Award (2011). As a professional service leader , he has chaired major conferences like IEEE INFOCOM and IEEE ICDCS.

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Hatem Abou-ZeidView profile

Assistant Professor

Queen's University at KingstonCanada

Hatem Abou-Zeid is an Assistant Professor at the Department of Electrical and Software Engineering in the Schulich School of Engineering , University of Calgary. He holds Adjunct Professor appointments at Queen’s University, Carleton University, and Ontario Tech University, Canada. With a Ph.D. in Electrical and Computer Engineering from Queen’s University (2014), his academic journey includes 7 years of industry research at Ericsson and Cisco , where he led R&D projects resulting in 15+ patents. Queen's University (Ph.D., Electrical and Computer Engineering) Arab Academy for Science, Technology and Maritime Transport (B.Sc. and M.Sc., Electronics and Communications Engineering) His research focuses on 5G/6G wireless networking , immersive communications , and robust machine learning for networks. Recent projects explore trustworthy AI , joint sensing and communication , and pediatric brain-computer interfaces (BCI) . He has published extensively in top venues like IEEE JSAC , GLOBECOM , and IEEE Transactions on Networking , with over 60 publications and 19 patent filings. His scientific awards include the Research Excellence Award 2023 (UCalgary), Early Research Excellence Award 2023 (Schulich), and Best Paper Awards at EMBC 2024 (as advisor) and IEEE ICC 2022 . He leads the WAVES Research Group , mentoring 10+ graduate students and postdocs. Collaborations span institutions like the Hotchkiss Brain Institute and industry partners such as Ericsson and European Space Agency .

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Jie GaoView profile

Assistant Professor

Carleton UniversityCanada

Dr. Jie Gao is an Assistant Professor at the School of Information Technology , Carleton University, with cross-appointments at Dalhousie University (Adjunct Faculty, 2024) and Carleton University (2025). He holds a Ph.D. in Electrical and Computer Engineering from the University of Alberta (2014) and has held postdoctoral and research associate positions at Ryerson University (2017-2019), University of Waterloo (2019-2020), and Marquette University (2020-2022). His research focuses on machine learning for communications/networking , 6G wireless networks , cloud/multi-access edge computing , IoT/industrial IoT solutions , and network virtualization/digital twins . Research Leadership: Co-investigator on projects related to AI-assisted network slicing, digital twin-driven resource allocation, and integrated satellite-terrestrial networks Led work on energy-efficient UAV-assisted edge computing (IEEE Best Land Transportation Paper Award 2024) Professional Roles: Senior Member, IEEE Lead Associate Editor, IEEE Access Vehicular Technology Society Section (2020-present) Associate Editor, Springer Peer-to-Peer Networking and Applications (2020-present) IEEE Vehicular Technology Society Young Professional Ambassador (2024) Notable Contributions: Authored/co-authored books on Intelligent Computing and Communication for the Internet of Vehicles (Springer, 2023) and Connectivity and Edge Computing in IoT (Springer, 2021) Holds patents on medium access control methods (US Patents 2022, 2024) Received multiple IEEE service awards (2018-2024)

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Diala NaboulsiView profile

Professor

École de Technologie SupérieureCanada

Diala Naboulsi is a Professor at the École de technologie supérieure (ÉTS) in the Department of Software Engineering and IT. Her research focuses on mobile networks, wireless systems, and cybersecurity, with a strong emphasis on machine learning applications in network optimization. She holds an M.Eng. from the Lebanese University and M.Sc. and Ph.D. degrees from INSA Lyon. Research Units: Summit Tech Research Chair, LASI Lab, Imagin Lab Expertise: Network virtualization, resource allocation, mobility management, UAV-based computing Her work spans resilience in wireless backhaul networks, energy-efficient frameworks in RAN slicing, and federated learning for privacy-aware traffic forecasting. She has advised numerous doctoral students, including Ahmed Abdelmoaty, Hnin Pann Phyu, and Philippe Lavoie. Key contributions include deep reinforcement learning approaches for network topology optimization and UAV-assisted MEC systems for Industry 5.0. Recent publications highlight advancements in 6G networks, network slicing, and edge computing. Her research aligns with strategic initiatives in sustainable and secure communication systems.

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Wei SongView profile

Professor

University of New BrunswickCanada

Dr. Wei Song is a Professor and the Coordinator of Software Engineering at the Faculty of Computer Science, University of New Brunswick (UNB) in Fredericton, New Brunswick, Canada. She has been with UNB since 2009, after completing her postdoctoral studies at UC Berkeley, and has established herself as a leading researcher in mobile networking and wireless communications. Her office is located in room ID419 and she can be reached at wsong@unb.ca. Education Ph.D. in Electrical and Computer Engineering, University of Waterloo (2003-2007) Postdoctoral Fellow, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2008-2009) Research Focus Dr. Song's research spans multiple cutting-edge areas in mobile and wireless networking, with a strong emphasis on integrating artificial intelligence and machine learning techniques. Her work addresses fundamental problems in mobile social networks, Internet of Things, vehicular networks, and mobile cloud computing. She explores how cooperative intelligence and distributed AI can enhance network performance while addressing practical constraints such as energy efficiency and user incentives. Her recent work particularly focuses on intelligent edge computing, mobile crowdsensing with deep reinforcement learning, and social-aware data dissemination through device-to-device communications. She investigates how to turn decentralized mobile "crowds" into coherent working groups and how social connections can be leveraged to improve data dissemination efficiency. Publication Trends Dr. Song's recent publications (2016-2023) demonstrate a clear evolution from traditional wireless networking to AI-driven approaches. While her earlier work focused on fundamental problems in device-to-device communications and resource allocation, her recent publications increasingly incorporate deep reinforcement learning, graph neural networks, and other AI techniques to solve complex optimization problems in mobile crowdsensing and edge computing. This shift reflects broader trends in the field toward intelligent, adaptive networking solutions. Scientific Recognition Best Paper Award from IEEE ICC (2018) UNB Merit Award (2014) Best Student Paper Award from IEEE CCNC (2013) Top 10% Award from IEEE MMSP (2009) NSERC postdoctoral fellowship (2008) Best Paper Award from IEEE WCNC (2007) Professional Service and Mentoring Dr. Song serves as Senior Member of IEEE and has held significant leadership roles, including Chair of the Joint Computer and Communications Chapter of IEEE New Brunswick Section (2014-2020). She has chaired symposia at major conferences including IEEE VTC Fall 2023, 2017, and 2016. As a supervisor, she mentors graduate students in areas including intelligent edge computing and deep learning for networking, and is currently recruiting students for Winter 2024 and Fall 2025.

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Ralph DetersView profile

Professor

University of SaskatchewanCanada

Ralph Deters is a Professor in the Department of Computer Science at the University of Saskatchewan , where he co-leads the MADMUC (Multi-Agent Distributed Mobile and Ubiquitous Computing) Lab . He has taught courses including CMPT 298, CMPT 350, CMPT 436, CMPT 842, and CMPT 898, and served as co-coordinator for CMPT 400/CMPT 405. Education Ph.D. in Computer Science, Federal Armed Forces University Munich (1998) MS (Diploma) in Computer Science, Technical University Berlin (1992) BS in Computer Science, Technical University Berlin (1988) Research Interests focus on multi-agent systems , scalable distributed systems , mobile/ubiquitous computing , and privacy-preserving technologies . Current lab projects include: Middleware infrastructures for decentralized systems (cloud/fog/mobile) Social software for sustainable user collaboration Decentralized trust-based data storage Persuasive technologies for health, physical activity, and learning Ethical AI and dark patterns in UI design Blockchain applications in IoT and access control Publications highlight trends in digital twins , blockchain integration , synthetic data for privacy , and machine learning for social impact , with recent work spanning health analytics, agricultural AI, and smart city applications. Lab Impact : The MADMUC Lab, established in 2000 with a $895,687 CFI grant, has graduated 120 researchers (17 PhD, 103 MSc), including two Vanier Scholars. The lab emphasizes awards, scholarships, and interdisciplinary collaboration with 152 total alumni.

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Shurui ZhouView profile

Assistant Professor

University of TorontoCanada

Shurui Zhou is an Assistant Professor at the University of Toronto , with primary appointment in the Department of Electrical & Computer Engineering and cross-appointments in the Department of Computer Science and the Department of Mechanical & Industrial Engineering . She is also affiliated with the Schwartz Reisman Institute and a member of the Data Sciences Institute . Zhou directs the FORCOLAB , focusing on enhancing collaboration in distributed software teams, especially in open-source and AI-enabled systems development. Her research integrates software engineering principles with human collaboration insights from organizational behavior, aiming to improve collaboration efficiency through tooling and interdisciplinary methods. Recent publications (2023-2025) explore LLM integration in software development , sustainability in scientific open-source communities , and collaborative challenges in computer-aided design . Education: PhD (2020) from Carnegie Mellon University's Software and Societal Systems Department , MSc from Peking University , BSc from Xi'an Jiaotong University Scientific Awards: Gordon Slemon Teaching of Design Award (2022) Grants: Collaborator on NSERC Alliance-Mitacs Accelerate Grant (2025) for LLM analysis in political data Her lab, FORCOLAB, develops tools like forks-insight.com to analyze GitHub forks and Redundant PR detection bots . Zhou organizes events like the Responsible LLM-Human Collaboration Hackathon (2024) and actively serves on conference committees including ICSE 2025 and ICSME 2024 .

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Fei ChiangView profile

Associate Professor

McMaster UniversityCanada

Dr. Fei Chiang is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her research focuses on data management , with emphasis on data quality, data privacy, information extraction , and contextual data cleaning . She has collaborated with IBM Global Services and Microsoft Research on improving data quality in enterprise systems. Key research themes include graph databases , temporal data analysis , and privacy-aware data processing Recent publications explore federated learning , SQL understanding in LLMs , and temporal graph constraints Industry collaborations with IBM Toronto Lab and Microsoft Research have led to innovations in data cleaning automation and semantic analysis. Her work bridges database theory with machine learning applications in healthcare inventory optimization and flight reliability prediction.

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Abdelhakim HAFIDView profile

Professor

University of MontrealCanada

Abdelhakim HAFID is a Full Professor at the University of Montreal , affiliated with the Faculty of Arts and Science and the Department of Computer Science and Operations Research . He leads the LRC — Laboratoire de recherche en réseaux de communication and is a member of several research centers including CIRRELT (interuniversity research center on enterprise networks, logistics, and transportation), the Cyberjustice Lab , and talents (AI for cybersecurity lab). His research focuses on Blockchain security , IoT , edge and fog computing , intelligent transport systems , and network resource management . He has supervised over 30 graduate students and led/co-led numerous research projects funded by agencies like NSERC , FRQNT , and MITACS . Notable projects include Blockchain Evolution: Quantum-Resistant Security (2025–2031) and Design and Management of Fog Networks (2019–2026). His work spans vehicular networks , mobile cloud computing , and machine learning for network optimization . He actively contributes to cybersecurity initiatives, including IoT authentication frameworks and AI-driven solutions for judicial systems (e.g., Cyberjustice ). Key Projects : Quantum-Resistant Blockchain Security (2025–2031) IoT Authentication via Blockchain and Intelligent SIM (2022–2026) Fog Network Design (2019–2026) Grants : NSERC Discovery Grants MITACS Acceleration Fellowships FRQNT Team Research Grants His lab collaborations include CIRRELT (logistics & transport) and Cyberjustice (digital law), reflecting interdisciplinary strengths in both technical and societal domains.

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Israat HaqueView profile

Associate Professor

Dalhousie UniversityCanada

Dr. Israat Haque is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, where she leads the Programmable and Intelligent Networking (PINet) research group. She also holds an adjunct professorship in the Department of Computing Science at the University of Alberta. PhD in Computer Science, University of Alberta NSERC Postdoctoral Fellow, University of California, Riverside MSc in Computer Science, Concordia University Her research focuses on developing high-performance, secure, and dependable distributed and emerging networking systems, with key interests in Software-Defined Networking (SDN), Cyber-physical Systems (CPS), Internet of Things (IoT), 5G/6G technologies, and AI/ML applications in networking. She applies data-driven approaches to solve real-world problems in network programmability, stream processing, and edge/cloud computing. The 15 most recent publications highlight a strong trend in in-network computing, IoT security, AI-driven network reliability, and stream processing. Her work spans from theoretical surveys to practical system implementations, often leveraging programmable data planes (P4), machine learning, and hardware acceleration to address challenges in performance, security, and fairness in distributed systems. Dr. Haque has received numerous prestigious recognitions: IEEE/ACM N2Women Rising Star Award (2021) ACM FAccT Best Paper Award (2023) IEEE WICE Outstanding Mentoring Award (2025) University of Alberta Alumni Honour Award (2024) Digital Nova Scotia Thinking Forward Award (2022) Intel Fast Forward Initiative Winner (2022) President’s Research Excellence Award, Dalhousie (2021) She actively mentors PhD and MSc students and leads externally funded research projects, including a Canada First Research Excellence Fund (CFREF)-supported initiative on Securing Smart Environments. She has served on editorial boards of IEEE Transactions on Vehicular Technology and IEEE Communications Magazine, and on program committees of top conferences such as IEEE ICNP, IEEE NetSoft, and ACM SIGCOMM. Her lab, PINet, fosters innovation in networking systems and produces high-impact research with real-world applicability. Dr. Haque leads the PINet research group, which includes current PhD and MSc students working on cutting-edge topics like post-quantum cryptography, network programmability for security, and large ML model security. The group collaborates with institutions such as the University of Alberta, Laval University, and Concordia, and has strong industry ties with Intel, Meta, Amazon, and Facebook.

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Sheldon WilliamsonView profile

Professor

Ontario Tech UniversityCanada

Dr. Sheldon Williamson is a Professor and NSERC Canada Research Chair in Electric Energy Storage Systems for Transportation Electrification at Ontario Tech University's Department of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science. His research focuses on advanced energy storage technologies, power electronics, and their integration into transportation systems and smart grids. Education: Ph.D. (Electrical Engineering, Illinois Institute of Technology, 2006), M.S. (Electrical Engineering, Illinois Institute of Technology, 2002), B.E. (Electrical Engineering, University of Mumbai, 1999). Research Interests - Electric Energy Storage Systems for Transportation Electrification - Battery Management Systems (BMS) and Thermal Safety - Wireless Power Transfer and Charging Infrastructure - Cyber-Physical Security in EV Systems - Smart Grid Integration of Renewable Energy Publications : Over 50 peer-reviewed articles from 2021–2025, focusing on battery technologies, power electronics, and electrification challenges. Key themes include solid-state batteries, cloud-based BMS architectures, and dynamic wireless charging systems. No scientific awards explicitly listed in provided texts. Active in academic leadership and curriculum development within the department.

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Adel AbusittaView profile

Assistant Professor

Polytechnic School of MontrealCanada

Dr. Adel Abusitta is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he conducts research at the intersection of artificial intelligence and cybersecurity. With expertise in secure and resilient AI systems, IoT security, and malware analysis, Dr. Abusitta has established himself as a significant contributor to the field of AI-powered cybersecurity solutions. Education: PhD in Computer Engineering from Polytechnique Montréal Postdoctoral Fellow at University of Montréal Postdoctoral Fellow at McGill University Dr. Abusitta's research focuses on developing secure and trustworthy artificial intelligence systems with applications in cybersecurity. His work spans several critical areas including explainable AI for security applications, AI-powered malware analysis, intrusion detection systems, and IoT security. He has made significant contributions to the understanding of how AI can be both secured against attacks and used to enhance security systems. His research addresses the dual challenge of making AI systems resilient to adversarial manipulation while leveraging AI's capabilities to detect and prevent cyber threats in complex environments like cloud computing and IoT networks. Analysis of Dr. Abusitta's recent publications reveals a strong focus on the intersection of AI and cybersecurity, particularly in developing robust anomaly detection systems, explainable security solutions, and resilient architectures for IoT environments. His work demonstrates a consistent trajectory toward making AI systems both more secure and more useful for security applications, with increasing emphasis on practical implementations that can withstand real-world challenges. Dr. Abusitta has collaborated extensively with Defence Research and Development Canada (DRDC) on projects related to AI-powered data analytics for discerning malware intent. He has also worked with industrial partners through the Institute for Data Valorization (IVADO) to develop privacy-preserving machine learning techniques that maintain accuracy while protecting sensitive information. His research has practical applications in critical infrastructure protection and secure AI deployment.

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Dan IonescuView profile

Professor

University of OttawaCanada

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

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