Network Function Virtualization (NFV) Professors and Supervisors in Canada | ApplySharif

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Raouf BoutabaView profile

Professor

University of WaterlooCanada

Raouf Boutaba is a Professor at the University of Waterloo , serving as Director of the David R. Cheriton School of Computer Science since July 2020. He holds prestigious fellowships including FRSC , FIEEE , FIEC , and FCAE . 2024: Inaugural Rogers Chair in Network Automation 2024: Ontario Research Fund–Research Excellence (ORF–RE) $2M grant for next-gen mobile networks 2021: University Professor title, University of Waterloo Research Interests span network automation, resource management in wired/wireless networks, network function virtualization (NFV), software-defined networking (SDN), cloud computing, blockchain, future Internet architecture, and cybersecurity. His work focuses on zero-touch networks, 5G/B5G slicing, and AI-driven orchestration. Scientific Contributions include 15+ recent publications on topics like reinforcement learning for RAN slicing, encrypted traffic classification, quantum network optimization, and self-driving infrastructure. His projects 5G LEAP and 5G ELITE explore network isolation and Open RAN principles. 2024: IFIP/IEEE CNOM Test of Time Paper Award 2024: Graduate Supervision Excellence Award 2021: Kenneth C. Sevcik Outstanding Student Paper Award (advisor) Teaching includes co-developing the NSERC CREATE Network Softwarization program, offering courses like Network Softwarization: Principles and Foundations (Winter 2024) and Technologies and Enablers since 2018. He emphasizes hands-on training in SDN, NFV, Open RAN, and 5G. Students and Collaborations : Supervised PhD students such as Shihabur R. Chowdhury (2021), Nashid Shahriar (2020), and undergrad Leni Aniva (2022 Gov. Gen. Silver Medal ). His team includes researchers working on 5G, blockchain, and AI-driven network management. Professional Leadership : Organized Rogers TEP Workshops (2024-2025), delivered keynotes at IEEE Globecom, ColCom, and BalkanCom, and served on expert panels for AI orchestration and 5G cybersecurity at major symposia.

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Azadeh TabibanView profile

Assistant Professor

University of ManitobaCanada

Azadeh Tabiban is an Assistant Professor in the Department of Computer Science at the University of Manitoba, leading the FOCUS research lab. She specializes in cybersecurity with a focus on cloud/edge security, network security, and applying machine learning to security challenges. Her work emphasizes practical solutions for real-world systems, including provenance analysis, forensics, and securing smart grids and 5G networks. Education: PhD from Concordia University (supervised by Prof. Lingyu Wang and Prof. Makan Pourzandi), followed by a postdoctoral fellowship at the University of Waterloo collaborating with Ericsson Montreal. Previously involved in the NSERC/Ericsson IRC in SDN/NFV Security Project. Research Interests: Building scalable security technologies for transparency and trustworthiness in computing systems. Key areas include provenance systems, cloud/NFV security, smart grid cybersecurity, and AI-driven security solutions. Recent projects include URGP-funded work on AI-based intrusion detection and NCC-supported 5G security collaborations with Ericsson and other universities. Awards and Grants: NSERC Discovery Grant (2024), University Research Grants Program (URGP) (2025), National Cybersecurity Consortium (NCC) Grant (2023), and Best Paper Candidate at CNS'20. Active in securing industrial partnerships and government-funded initiatives. Advising and Training: Supervises PhD and MSc students in system security and machine learning applications. Offers funded positions prioritizing underrepresented groups. Mentors undergraduate students interested in programming and practical cybersecurity solutions. Labs/Teams: Leads the FOCUS lab focused on foundational and operational cybersecurity research. Collaborates with industry partners like Ericsson and academic institutions including Waterloo and Concordia.

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Hyejin KuView profile

Professor

York UniversityCanada

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

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Suryadipta MajumdarView profile

Associate Professor

Concordia UniversityCanada

Suryadipta Majumdar is an Associate Professor at the Concordia Institute for Information Systems Engineering (CIISE), part of Concordia University. His primary research interests focus on Cloud Computing Security and Privacy, Internet of Things (IoT) Security and Privacy, and Software-Defined Network (SDN) Security. He has contributed extensively to proactive security measures in containerized systems and Kubernetes environments, alongside developing tools like ACE-WARP and PerfSPEC to address real-time threats. In terms of education, he holds a PhD in a relevant field, though specific details about his academic background (e.g., institutions, thesis topics) are not explicitly mentioned in the provided text. His work bridges theoretical cybersecurity frameworks with practical implementations, emphasizing automated translation, differential privacy, and compliance auditing across cloud and IoT ecosystems. Majumdar’s research trends highlight a focus on layered security analysis, anomaly detection in IoT networks, and mitigating vulnerabilities in network functions virtualization (NFV). He has explored topics such as resilient in-band OpenFlow networks, runtime security policy enforcement in OpenStack, and privacy-preserving network data anonymization via tools like SegGuard. His recent publications reflect collaboration with international conferences and workshops, including contributions to Digital Forensics and Applied Cryptography. No scientific awards are explicitly mentioned in the text. His advising activities and grant history remain unlisted, though he has developed notable security frameworks and tools. He is affiliated with CIISE and likely contributes to its research initiatives in emerging technologies like 5G and edge-core environments.

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Weihua ZhuangView profile

Professor

University of WaterlooCanada

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.

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Ahmed KarmouchView profile

Professor

University of OttawaCanada

Professor Ahmed Karmouch is a faculty member at the University of Ottawa's School of Electrical Engineering and Computer Science. He holds a Ph.D. and specializes in advanced networking research, including Network Slicing, Software Defined Networks (SDN), Named Data Networking (NDN), and Cloud Computing. His IMAGINE Lab focuses on developing innovative solutions for autonomic and cognitive networks, emphasizing programmable data planes and in-network computing. Research Interests: Network Slicing Software Defined Networking Named Data Networking Programmable Data Plane Intelligence In-Network Computing Ambient Intelligence & IoT Publications reflect a focus on SDN, NDN, and cloud infrastructure optimization. His work often bridges theory and practical implementation, addressing challenges in network efficiency, reliability, and scalability. Supervised over 30 graduate students, contributing to advancements in edge computing, virtual networks, and autonomic systems. Labs/Teams: Leads the IMAGINE Lab, dedicated to research in mobile autonomic networks, context-aware systems, and future broadband infrastructure. Projects include WiMAX security, policy-based overlay networks, and semantic resource discovery.

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Changcheng HuangView profile

Professor

Carleton UniversityCanada

Changcheng Huang is a Professor at Carleton University's Department of Systems and Computer Engineering, part of the Faculty of Engineering and Design. He holds a Ph.D. from Carleton University and is licensed as P.Eng. His research focuses on Machine Learning, Network Architecture, and Optical Networks, emphasizing resource optimization and protocol design. Dr. Huang leads the Advanced Optical Network Laboratory (AONL), funded by CFI and OIT, which explores optical network technologies and interworking with electronic networks. His lab includes state-of-the-art equipment like Nortel switches and photonic switches. Recently, he advised PhD students Qiao Lu, Khoa Nguyen, and others, and completed postdoc Eslam G. AbdAllah. RA positions are available at both master's and PhD levels. His work spans publications in journals like IEEE Transactions and conferences such as Globecom and ICC. Research areas include intelligent network control mechanisms, wireless networks, and network protocol implementation. Education: Ph.D. (Carleton University). Research interests also include modeling/simulation techniques and reliability mechanisms for optical networks. He teaches courses like SYSC 5108 (Deep Learning) and SYSC 4602 (Computer Communications). Grants funded by CFI and OIT support his lab's optical networking projects. Over 150+ publications highlight his contributions to virtual network embedding, edge computing, and optical data center networks. Lab facilities include OMM photonic switches, Nortel routers, and Dell servers. Collaborative projects involve industry and academic partnerships, advancing interworking technologies between optical and electronic networks. His work bridges theoretical research with practical implementations, addressing challenges in network scalability, energy efficiency, and reliability.

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Liang XueView profile

Assistant Professor

York UniversityCanada

Liang Xue is an Assistant Professor in the School of Information Technology at York University. She holds a PhD in Electrical and Computer Engineering from the University of Waterloo (2022) and completed a postdoctoral fellowship at the University of Guelph’s School of Computer Science (2022–2024). Her research focuses on applied cryptography, blockchain security, privacy-preserving AI, and cybersecurity in cloud and IoT systems. She has published in top-tier journals like IEEE Transactions on Dependable and Secure Computing, and conferences such as IEEE International Conference on Communications. Her work addresses challenges in data privacy, secure authentication, and regulatory compliance in decentralized systems. Recent projects include privacy-enhancing technologies for access control, blockchain-based data trading frameworks, and federated learning with privacy guarantees. She actively contributes to standards for cybersecurity in smart cities and next-generation wireless networks.

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Mohammad Ali SalahuddinView profile

Academic

University of WaterlooCanada

Mohammad Ali Salahuddin is a Research Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo , specializing in networking and machine learning. He holds a Ph.D. in Computer Science from Western Michigan University (2014), with prior academic roles at Université du Québec à Montréal and Concordia University. His research spans 5G network slicing, vehicular networks, and secure content delivery systems. Education: Ph.D. (2014, Western Michigan University); M.S. (2003, Western Michigan University); M.S. (2001, SZABIST); B.S. (1999, FAST-NUCES) Dr. Salahuddin's research focuses on 5G/6G network softwarization , autonomous threat mitigation , and machine learning for network management . His work integrates reinforcement learning and federated learning for scalable solutions in SDN/NFV , IoT , and edge computing . Recent studies address data drift in encrypted traffic classification and DDoS detection using outlier exposure-based federated learning . He has received multiple best paper awards at IEEE/IFIP NOMS (2023, 2022), IEEE CNOM (2021), and Kenneth C. Sevcik Outstanding Student Paper Award (ACM SIGMETRICS, 2021). His NSF-funded projects include vehicular cloud resource management and localization techniques. Dr. Salahuddin actively contributes to academic service as Vice-Chair of IEEE KW Section's Communications Society and TPC member for top conferences.

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Nashid ShahriarView profile

Assistant Professor

University of ReginaCanada

Nashid Shahriar is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research addresses resource allocation challenges in next-generation networks including 5G, elastic optical networks, cloud infrastructures, and IoT systems. He holds a Ph.D. in Computer Science from the University of Waterloo, an M.Sc. from Bangladesh University of Engineering and Technology (BUET), and a B.Sc. from BUET. His work leverages optimization, machine learning, and AI for network management. Recent publications focus on 5G network slicing, intrusion detection, and NFV security. Research emphasizes practical AI-driven solutions for telecommunications and cloud systems.

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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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Dongmei ZhaoView profile

Professor

McMaster UniversityCanada

Dr. Dongmei Zhao is a Professor in the Department of Electrical & Computer Engineering at McMaster University, part of the Faculty of Engineering. She specializes in wireless networking, network resource management, mobile edge computing, mobile computation offloading, and digital twins. Her research clusters focus on Digital & Smart Systems. Dr. Zhao holds a Ph.D. from the University of Waterloo. She teaches courses such as COMPENG 4DK4 (Computer Communication Networks), COMPENG 4DN4 (Advanced Internet Communications), and graduate-level courses like ECE 729 (Resource Management in Wireless Networks). Her research interests span cutting-edge topics including UAV-enabled edge computing, digital twin migration, vehicular networks, and reinforcement learning applications in resource allocation. She actively contributes to advancing 6G networks, security redundancy in autonomous systems, and decentralized manufacturing platforms. Dr. Zhao's recent publications emphasize optimization techniques for dynamic networks, platooning systems, and multi-agent learning frameworks. She has been recognized for her work in vehicular edge computing and digital twin integration, though explicit awards are not listed here. She advises on graduate studies in networking and edge computing, though no specific student names are provided in the text. Her work often intersects with practical challenges in smart infrastructure and autonomous vehicle systems.

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Mohammad SalahuddinView profile

Research Professor

University of WaterlooCanada

Mohammad Salahuddin is a Research Assistant Professor at the University of Waterloo. He holds a Ph.D. in Computer Science from Western Michigan University (2014), an M.Sc. from Western Michigan University (2003), another M.Sc. from Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (2001), and a B.Sc. from University of Karachi (FAST-ICS) (1999). His research focuses on machine/deep learning applications in networking, network security, encrypted traffic analysis, and cognitive management of wired/wireless networks. He also explores network softwarization, including SDN and NFV. His work bridges theoretical advancements and practical network system optimization. Research Interests: Machine Learning-driven Network Security Encrypted Traffic Classification using Deep Learning Resource Allocation in 5G/6G and Edge Computing Software-Defined Networking (SDN) Architectures Cognitive Radio Networks and Dynamic Spectrum Management Dr. Salahuddin maintains an active research profile with publications tracked on Google Scholar . His academic webpage at https://cs.uwaterloo.ca/~m2salahu provides further details on ongoing projects and collaborations.

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Alan HuView profile

Professor

University of British ColumbiaCanada

Alan Hu is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on formal verification, algorithms, computer architecture, and electronic design automation. He teaches courses such as Intermediate Algorithm Design and Analysis (CPSC 320) and Introduction to Formal Verification and Analysis (CPSC 513). Dr. Hu has received notable awards, including the IEEE Council on Electronic Design Automation Outstanding Service Award and the IBM Faculty Award. His work emphasizes scalable verification techniques, SAT-based algorithms, and optimization in cloud computing and hardware systems. His research spans formal methods for hardware/software systems, including verification of embedded software, cache coherence, and network function virtualization. Notable contributions include advancements in SAT modulo theories, data race detection in heterogeneous systems, and cloud resource allocation frameworks like Cospot. Dr. Hu has been actively involved in teaching and curriculum development, consistently offering courses on algorithms, formal verification, and software design since 2000. His publications reflect a blend of theoretical foundations and practical applications in electronic design, cloud infrastructure, and verification tools. His scientific achievements include innovations in post-silicon validation, emulation-based coverage reduction, and formal analysis for debug trace optimization. He also contributes to the academic community through conference organization and editorial roles in formal verification and computer-aided design.

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Ioannis LambadarisView profile

Professor

Carleton UniversityCanada

Ioannis Lambadaris is a Full Professor and Chancellor’s Professor at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. Holding a Ph.D. from the University of Maryland, he has contributed extensively to network performance analysis over 25+ years. Specializes in stochastic processes, cloud computing, and wireless edge systems Led Ericsson 5G Chair initiatives Supervised over 70 graduate students His research spans QoS control , VNF placement optimization , and IoT indoor localization , with over 170 publications. Recent work focuses on reinforcement learning and deep learning in network resource allocation. Scientific Recognition: Chancellor’s Professor Ericsson 5G Chair Contact: ioannis@sce.carleton.ca | Office: Mackenzie 4448, Ottawa, ON

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