جست‌وجوی استاد و پژوهشگر

Marc St-Hilaireمشاهده پروفایل

استاد

• Telecommunication Network Planning
• Wired and Wireless Communication Networks
• Mobile Computing
• +۷ مورد دیگر

Marc St-Hilaire is a Professor at Carleton University, cross-appointed between the Carleton School of Information Technology and the Department of Systems and Computer Engineering. He holds a Ph.D. from École Polytechnique de Montréal and is a Senior Member of IEEE. His research focuses on telecommunication networks, including network planning, optimization, mobile computing, and emerging technologies like fog/edge computing and software-defined networking (SDN). He leads the NET Program and actively contributes to research through his team and collaborations with labs such as the Broadband Communications and Wireless Systems (BCWS) and Real-Time and Distributed Systems Group (RADS). Dr. St-Hilaire has supervised numerous graduate students, with current advisees in Ph.D. and M.Sc. programs. His work has garnered multiple awards, including Best Paper Awards at conferences like ADHOCNETS and iThings, as well as the Carleton Faculty Graduate Mentoring Award. He is deeply involved in academic service, serving as editor for journals like JSAN and MONET, and organizing committees for major conferences such as IEEE ICC and IEEE Globecom. His research explores cutting-edge areas like vehicular cloud networks, blockchain-based edge intelligence, and resource allocation in distributed systems. He actively seeks to mentor talented students and collaborate on projects addressing challenges in future networks, including 5G/6G integration and industrial IoT reliability.

Derek Eagerمشاهده پروفایل

استاد

• Computer System Performance Evaluation
• Distributed Computer Systems
• Computer Networks
• +۵ مورد دیگر

Dr. Derek Eager is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on computer system performance evaluation, distributed systems, and internet multimedia applications. He holds a Ph.D. from the University of Toronto (1984) and has held academic positions since 1984. His work includes contributions to content delivery systems, video streaming optimization, and cache algorithms. He has served in leadership roles at ACM SIGMETRICS and has authored numerous influential publications. Education: B.Sc. (Hons.) in Computer Science from the University of Regina (1979), M.Sc. and Ph.D. from the University of Toronto (1981, 1984). Research Interests: Design and performance evaluation of internet applications, particularly in content distribution and multimedia systems. Recent work includes optimizing cache strategies for 360° video and analyzing user behavior patterns. Professional Contributions: Served as SIGMETRICS Chair (2001-2003), program chair for SIGMETRICS 1987, and co-chair for SIGMETRICS 2005. Current roles include program committee memberships in ACM SIGMETRICS, MAMA, and MASCOTS (2025). Awards: Best Paper Award at ACM SIGCOMM 2013 Workshop on Future Human-Centric Multimedia Networking (FhMN 2013). Labs/Teams: Active in distributed analytics and multimedia systems research, collaborating on projects such as Spark resource management and agricultural IoT systems.

• Big Data Management
• Scalable Data Science
• Spatial and Spatio-Temporal Data Processing
• +۵ مورد دیگر

Suprio Ray is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick, Canada, leading the Big Data Systems and Analytics Lab. He holds a PhD from the University of Toronto, an M.Sc. from the University of British Columbia, and a B.E. from NIT Trichy. Previously, he worked in industry roles at Oracle, Bell Labs, and Webtech Wireless, and completed a PhD internship at SAP. His research focuses on scalable data systems, spatial/spatio-temporal data management, and modern hardware utilization. Key projects include the Jackpine spatial database benchmark , DaskDB (a scalable data science system), and NUMA-aware query processing . He collaborates across disciplines with ECE and GGE departments, supported by grants from NBIF, NSERC, and industry partners. Research interests span Big Data systems, privacy/security in databases, blockchain analytics, and parallel/distributed computing. He has pioneered techniques like STILT multi-dimensional indexes , privacy-preserving spatial queries (Pystin) , and learned spatial indexes . Over 60 publications appear in top venues like SIGMOD, ICDE, and IEEE BigData. Education: PhD (Computer Science), University of Toronto (2015) M.Sc. (Computer Science), University of British Columbia (2003) B.E. (Computer Science & Engineering), NIT Trichy (2000) Teaching includes courses on big data systems (CS4545/6545), database foundations (CS6585), and data science (CS2545). He advises graduate students in scalable analytics and spatial systems, emphasizing industry-relevant software skills. Key awards include the 2024 ACM SIGSPATIAL best poster award , IBM Best Student Paper (2014) , and Harrison McCain Foundation award (2016) . His work has been recognized with 7 best paper awards and 3 patents. Current projects explore FPGA-accelerated joins , serverless data analytics , and privacy-preserving blockchain queries . Past contributions include the GEMM mobility model (2003) and foundational spatial benchmarks.

Son Vuongمشاهده پروفایل

دانشیار

• Computer Security
• Distributed Systems
• Mobile Computing
• +۳ مورد دیگر

Son Vuong is an Associate Professor Emeritus in the Department of Computer Science at the University of British Columbia, affiliated with the Faculty of Science. His research spans cybersecurity, distributed systems, and mobile computing, with a focus on malware detection, mobile agent systems, and peer-to-peer network architectures. He has contributed to advancements in context-aware security frameworks, wireless sensor networks, and healthcare IT solutions. His work emphasizes practical applications of distributed computing, including scalable content delivery systems, QoS verification in networks, and intrusion detection methodologies. Notable projects include frameworks for Android malware classification using machine learning and innovative approaches to MANET service discovery. Publications highlight trends in mobile computing security, P2P scalability, and healthcare system integration. His research bridges theoretical foundations with real-world implementations, addressing challenges in both academic and industrial contexts.

Mohammad Shahradمشاهده پروفایل

استادیار

• Cloud Computing
• Sustainability
• Large-Scale Computing Systems
• +۳ مورد دیگر

Mohammad Shahrad is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) and an Associate Faculty Member of UBC's Computer Science Department. He leads the UBC Cloud Infrastructure Research for Reliability, Usability, and Sustainability (CIRRUS) Lab. His research focuses on sustainable and efficient large-scale computing systems, particularly in cloud resource management and serverless systems. Education: Ph.D. in Electrical Engineering, Princeton University (2020) M.A. in Electrical Engineering, Princeton University (2016) B.Sc. in Electrical Engineering, Sharif University of Technology, Tehran (2014) Research Interests: Shahrad's work emphasizes energy-efficient cloud computing, serverless architecture optimization, and sustainability in distributed systems. His projects address challenges like carbon footprint reduction, resource allocation, and cold-start delays in serverless platforms. He employs interdisciplinary approaches, combining system design, algorithm development, and empirical analysis to tackle real-world cloud infrastructure problems. Recent Contributions: His research trends include carbon-aware scheduling, geospatial shifting for sustainability, and developer-centric compliance tools for serverless applications. These efforts aim to balance performance, cost, and environmental impact in modern cloud ecosystems. Scientific Awards: Community Award at USENIX ATC '20 for Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider Advising & Grants: As CIRRUS Lab lead, Shahrad mentors researchers in cloud infrastructure and sustainability. While specific grant details are not listed, his work reflects substantial industry collaboration (e.g., Microsoft Research, Azure). Labs/Teams: The CIRRUS Lab focuses on creating sustainable cloud solutions through open-source frameworks like OpenPiton and collaborative industry partnerships.

Mea Wangمشاهده پروفایل

استاد

• Networking systems and algorithms
• Multimedia networking
• Cloud computing
• +۴ مورد دیگر

Professor Mea Wang is a faculty member in the Department of Computer Science at the University of Calgary, affiliated with the Faculty of Science. She leads the NetMedia.Sys lab, focusing on bridging computing and networking in systems like multimedia and cloud environments. Her work emphasizes enhancing Quality-of-Service (QoS) for providers and Quality-of-Experience (QoE) for users. Education: Bachelor of Computer Science (Honours), University of Manitoba (2002) Master of Applied Science (MASc), University of Toronto (2004) Doctor of Philosophy (PhD), University of Toronto (2008) Research Interests: Networking systems, multimedia streaming, cloud computing, distributed systems, extended reality (XR), data science, and accessibility technologies for neurodivergent populations. Her lab develops open-source tools like iStream and collaborates with industry partners such as Cybera and AT&T. Recent Research Directions: Digital accessibility for physical spaces, AR/VR systems for nonspeaking autistic individuals, and optimization of multimedia distribution in heterogeneous networks. Her work spans over 60 publications in top conferences/journals, with a focus on practical implementations and industry impact. Awards: University of Calgary Research Excellence Chair (2024) Parex Innovation Fellowship (2023) Order of the University of Calgary (2022) PEAK Scholar Award (2019) Labs & Teams: Director of the NetMedia.Sys lab, which houses advanced infrastructure including server clusters, IoT devices, and extended reality gear. Current projects include digital twins, immersive education tools, and SDN-based streaming optimizations.

Samer Lahoudمشاهده پروفایل

دانشیار

• Wireless Communications
• Internet of Things (IoT)
• Low Power Wide Area Networks (LPWAN)
• +۱۲ مورد دیگر

Samer Lahoud is an Associate Professor and University Research Chair at the Faculty of Computer Science, Dalhousie University, where he leads research in wireless networks, IoT, and resource optimization. He joined Dalhousie in 2023 after holding academic positions at the University of Rennes and Saint Joseph University in Lebanon, and working as a researcher at IRISA laboratory and as a research engineer at Nokia Bell Labs Europe. His educational background includes a PhD in Computer Science and Networks from IMT Atlantique, Rennes (2006), and a Habilitation (HDR) from Université Paris-Saclay (2023), both in France. Dr. Lahoud's research focuses on enhancing wireless network performance through adaptive and energy-efficient solutions. Key areas include radio resource allocation in cellular and LPWAN, full-duplex communications, and game-theoretic models for multi-operator networks. His work bridges theoretical models with real-world applications in smart agriculture, smart cities, and industrial IoT. He has made significant contributions to LoRaWAN propagation modeling and developed open-source simulators for RL-based resource allocation. His recent publications reflect a strong trend toward intelligent, learning-based, and game-theoretic approaches for optimizing LPWANs, 5G RAN slicing, and full-duplex networks. The articles emphasize energy efficiency, reliability, and scalability in IoT systems, with increasing integration of machine learning and federated learning paradigms. University Research Chair - Established Scholar, Dalhousie University Dr. Lahoud has co-supervised over 10 students, including postdoctoral researchers, PhD, and master's candidates. He has led multidisciplinary research projects funded by national and international agencies, particularly in LPWAN for smart agriculture. He also played a key role in establishing a joint IoT master's program between Saint Joseph University and Paris-Saclay University. He leads a research team focused on wireless systems and has developed open-source tools such as the LoRa-MAB simulator and FDOFDMA-Simulator. His lab fosters international collaboration between institutions in Canada, France, and Lebanon, promoting student mobility and knowledge transfer.

Abdelkader Oudaمشاهده پروفایل

دانشیار

• Information Security
• Cryptography
• Network Security
• +۹ مورد دیگر

Dr. Abdelkader Ouda is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Western Ontario, with over three decades of experience in academia and the software industry. He holds a Ph.D. in Computer Science from Western University (2005), an M.Sc. in Computer Science and Pure Mathematics from Ain Shams University (1993), and a B.Sc. (Hons) in the same disciplines from Ain Shams University (1986), graduating with the highest honor (96.80%). His research spans information security, cryptography, network security, big data security and privacy, and data analytics . He leads the Information and Computer Networks Security (iCNS) Research Laboratory, focusing on innovative solutions for IoT, fog–cloud computing, and behavioral biometrics. His recent work includes developing the ADG Engine for synthetic data generation and HSM4SSL for intra-domain security. Dr. Ouda’s publications highlight advancements in authentication protocols, lightweight IoT security frameworks, database benchmarking, and machine translation . His industry experience includes 14 years in software development, project management, and system analysis, which he integrates into his teaching of courses like Software Design, Algorithms & Data Structures, and Information Security. He also contributes to engineering accreditation through the CEAMS application. Key trends in his articles include secure communication in hybrid architectures, energy-efficient IoT protocols, and reinforcement learning for continuous authentication . His work bridges academic theory with practical applications, emphasizing efficiency and scalability in modern computing environments.

Wenbo Heمشاهده پروفایل

استاد

• Machine Learning
• Privacy-Preserving Machine Learning
• Wireless Networks
• +۷ مورد دیگر

Wenbo He is a Professor in the Department of Computing and Software at McMaster University 's Faculty of Engineering. His research bridges Machine Learning , Privacy-Preserving Technologies , and Networked Systems , with a focus on secure federated learning , data anonymization , and wireless network optimization . Contact: hew11@mcmaster.ca Research Interests include: Machine Learning : Robustness under label noise, ensemble models, and 2D/3D classification. Privacy : Differentially private feature operations, encrypted classification, and location privacy. Networking : Software Defined Networking (SDN), wireless ad hoc networks, and crowdsensing. Recent Publications span IEEE Transactions on Mobile Computing , IEEE Infocom , and NeurIPS , with themes in: Security : Data poisoning, web shell obfuscation, and correlation attacks. AI/ML : Noise-robust models, face anonymization, and video action recognition. Systems : RFID optimization, cloud storage, and energy-efficient data centers. Teaching highlights include courses like Real-Time Systems , Computer Networks and Security , and Big Data Systems since 2017.

M. Omair Shafiqمشاهده پروفایل

دانشیار

• Big Data Analytics
• Machine Learning
• Data Modeling
• +۴ مورد دیگر

M. Omair Shafiq serves as an Associate Professor at the School of Information Technology, Carleton University, Ottawa, Canada, where his research focuses on data-intensive computing systems and analytics. His primary research domains include Big Data Analytics (parallel/distributed processing, streaming data, social media analytics), Machine Learning (prediction models, recommendation systems, deep learning), Data Modeling (semantic reasoning, natural language processing), and Cloud Computing (services computing, cloud management). Additional expertise spans Internet of Things, Data Science, and Cybersecurity through his work on Cloud/Fog/Edge systems. Dr. Shafiq has received significant recognition including: NSERC Postdoctoral Fellowship Award Mitacs Elevate Postdoctoral Fellowship (2015-2016) NSERC Vanier Canada Graduate Scholarship (2012) Alberta Innovates Scholarships (2010-2011) University of Calgary Research/Teaching Awards (2009-2012) He actively contributes to the academic community through extensive program committee service at major conferences including ACM SIGMOD, IEEE IRI, and IEEE Big Data, while maintaining rigorous peer-review commitments for journals such as IEEE Transactions on Services Computing and Springer KAIS.

Tasneem Darwishمشاهده پروفایل

استادیار

• Intelligent Network Management
• Satellite Networks
• Software Defined Networks (SDNs)
• +۶ مورد دیگر

Tasneem Darwish is an Assistant Professor in the Department of Computer Science at Saint Francis Xavier University. Their research focuses on intelligent and autonomous network management, including software-defined networks (SDNs), network function virtualization (NFV), satellite networks, 5G/6G technologies, vehicular ad hoc networks (VANETs), IoT, blockchain, and network security. Their work emphasizes future network architectures and protocols for emerging technologies like LEO satellites and edge computing. Research interests include optimizing network performance through graph-based frameworks, latency reduction in distributed systems, and secure communication protocols. They have contributed to interdisciplinary areas such as smart transportation systems via vehicular networks and high-altitude platform stations (HAPS) for enhanced connectivity. Publications highlight trends in satellite-based communication infrastructure, self-evolving network management systems, and challenges in integrating emerging technologies into existing frameworks. Their work bridges theoretical advancements with practical implementations in next-generation networks. No scientific awards or grants are explicitly mentioned in the provided texts. Advising responsibilities and lab affiliations remain unspecified.

• Wireless Communication Systems
• Real-Time Scheduling
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Ning Lu is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada, and holds a Canada Research Chair Tier II in Future Communication Networks. He specializes in real-time scheduling, distributed algorithms, and reinforcement learning for wireless communication networks, with expertise in vehicular networks, IoT, and autonomous systems. Education : PhD in Electrical Engineering (2015), University of Waterloo MEng in Electrical Engineering (2010), Tongji University BEng in Electrical Engineering (2007), Tongji University Research Focus : Dr. Lu's work bridges theoretical foundations and practical applications in wireless networks, including vehicular communication protocols, energy-efficient systems, and AI-driven network optimization. His recent efforts emphasize autonomous vehicle motion forecasting, secure federated learning, and smart infrastructure for 6G/IoT. Publications Trends : His articles focus on cutting-edge topics like vehicular networks, reinforcement learning for edge computing, and robust AI models against adversarial attacks. Notable themes include multi-agent systems, dynamic scheduling, and hybrid communication architectures. Awards : Canada Research Chair Tier II (202X) NSERC Postdoctoral Fellowship (2015) Best Paper Award at IEEE GLOBECOM 2014 2nd Place, Valeo Innovation Challenge 2014 Contributions : He leads research on network slicing, emergency communication systems, and AI-driven traffic prediction. His work has been applied to disaster response frameworks and smart city infrastructure projects. He serves on the editorial board of Springer's Encyclopedia of Wireless Networks.

• Machine Learning
• Neural Computing
• Computer Vision
• +۴ مورد دیگر

Hazem Abbas is a Continuing Adjunct Professor at Queen’s University’s School of Computing within the Faculty of Arts and Science. His research focuses on machine learning, neural computing, and their applications in computer vision, image processing, and natural language processing. He holds a Ph.D. in Electrical and Computer Engineering from Queen’s University and has held leadership roles, including Department Chair at Ain Shams University and Dean of Media and Engineering Technology at the German University in Cairo. Education: B.Sc. and M.Sc. in Computer Engineering, Ain Shams University, Cairo, Egypt Ph.D. in Electrical and Computer Engineering, Queen’s University, Canada Research Interests: Machine learning model development Neural computing for computer vision and NLP Evolutionary algorithms in neural network design Systems and networks optimization His recent articles explore topics like large language models, federated learning, 5G networks, and bioinformatics applications. Notable contributions include frameworks for robust visual tracking and secure vehicular networks. Scientific Awards: Senior Member, IEEE Prof. Abbas has collaborated on grants related to cloud computing optimization and neural cryptography. He chairs IEEE Signal Processing and IAPR Technical Committees, reflecting his leadership in pattern recognition research. His work spans lab initiatives in analog layout automation and cybersecurity for IoT systems.

Samer Al-Kiswanyمشاهده پروفایل

دانشیار

• Data Center Optimization
• Cloud Computing
• Serverless Computing
• +۳ مورد دیگر

Samer Al-Kiswany is an Associate Professor in the Department of Computer Science at the University of Waterloo. His research focuses on optimizing modern data center and cloud architectures, leveraging advancements in networking, storage, and processing to enhance system efficiency and performance. He holds a Ph.D. (2013) and M.Sc. (2008) from the University of British Columbia and a B.Sc. (2003) from Jordan University of Science and Technology. Research Interests: Dr. Al-Kiswany’s work emphasizes cloud-native paradigms (serverless, disaggregated architectures), data deduplication, network-accelerated scheduling, and fault-resilient systems. His projects include developing software stacks for efficient cloud applications and benchmarking frameworks for edge and serverless computing. Publications: His recent work spans innovations in data deduplication (e.g., VectorCDC, SeqCDC), network-accelerated scheduling (Draconis, Falcon), and fault tolerance (CASPR). Over 30 publications demonstrate expertise in storage systems, distributed computing, and networking. Awards: No awards explicitly listed in provided texts. Grants & Advising: No student advisees or grant details provided. His research is supported through university and collaborative projects. Labs/Teams: Involved in cloud systems and distributed computing research groups at the University of Waterloo.

Muthucumaru Maheswaranمشاهده پروفایل

دانشیار

• Computer Networking
• Information Security
• Distributed Systems
• +۴ مورد دیگر

Muthucumaru Maheswaran is an Associate Professor at McGill University's School of Computer Science and Department of Electrical and Computer Engineering since 2003. He holds dual affiliations and leads the Advanced Networking Research Lab (ANRL). His research focuses on computer networking, information security, and distributed systems, with specialties in edge computing, social accountability frameworks, and utility/grid computing. Key projects include the GINI educational toolkit for networking simulations and the Jade programming framework for IoT systems. Education BSc in Electrical and Electronic Engineering, University of Peradeniya (1990) MSEE in Electrical Engineering, Purdue University (1994) PhD in Computer Engineering, Purdue University (1998) His work integrates social factors into network security, such as game-theoretic models for authentication and community-centric integrity management. Current research is funded by NSERC, McGill, TRLabs, Ericsson, and Ciena. He teaches graduate courses in computer networks and undergraduate courses on operating systems and fault-tolerant computing. The ANRL Lab explores smart city technologies, vehicular crowdsensing, and pandemic impact simulations through agent-based modeling. His publications span edge computing optimizations, secure IoT deployments, and fog-driven autonomous systems.