Find Academic Supervisors

Damian VicinoView profile

Academic

• Discrete-Event Modeling and Simulation
• Uncertainty Theory
• Peer-to-peer Networks
• +3 more

Dr. Damian Vicino is an Adjunct Research Professor at the Faculty of Engineering and Design , Carleton University. He holds a Ph.D. from both Carleton University (Canada) and Université de Nice-Sophia Antipolis (France). With over 20 years of industry experience, he has held senior roles at companies like Alexa Communications and AWS, specializing in C++ SDK development and distributed systems. Affiliations: Carleton University (2023–present), Industry roles (1999–2023) Education: Ph.D. in Computer Science (Cotutelle), Carleton University & Université de Nice-Sophia Antipolis (France) Research Interests His work focuses on Discrete-Event Modeling and Simulation , Uncertainty Theory , Peer-to-peer Networks , and Distributed Systems . He has pioneered advancements in time representation for simulation frameworks and explored SDN's impact on P2P systems. Research Trends His articles emphasize precision in simulation models , uncertainty quantification , and hybrid network protocols . Recent work integrates SDN for reactive routing optimizations and evaluates advanced data types for temporal modeling. Awards & Grants No explicit awards or grants are listed, though his industry collaborations suggest applied research funding. Labs & Teams Not explicitly mentioned, but his work aligns with simulation and distributed systems research groups at Carleton.

Alex ThomoView profile

Professor

• Databases
• Algorithms for Big Graphs
• Distributed Data
• +4 more

Alex Thomo is a Professor in the Department of Computer Science at the University of Victoria, Canada. He holds a PhD from Concordia University (2003) and has expertise in databases, graph algorithms, and formal languages. His research focuses on database systems, big data algorithms, social network analysis, and privacy-preserving techniques. Education: B.Sc. (University of Piraeus, 1996), M.Sc. and PhD (Concordia University, 2001–2003). Research interests include distributed data processing, graph theory applications, and scalable algorithms for large networks. He has contributed to projects like the k-core decomposition algorithm and importance-based community detection in web-scale networks. Teaching includes courses on database systems, data mining, and artificial intelligence. Active in academic service as an associate editor of Social Network Analysis and Mining .

Todd EavisView profile

Associate Professor

• Programming Languages
• Code Generation
• Continuous Integration and Delivery
• +3 more

Todd Eavis is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. He received his PhD from Dalhousie University (2003) and completed an NSERC postdoctoral fellowship at Carleton University (Ottawa). His research focuses on programming language design, software development environments, and reducing complexity in build systems. Teaching includes courses on programming languages, operating systems, databases, and parallel programming at both undergraduate and graduate levels, such as COMP 228, COMP 248, COMP 6411, and COMP 6521. Research themes emphasize approachable software frameworks, 90/10 syntax, code generation, platform-agnostic development, and continuous integration/delivery. Key areas include query optimization in parallel DBMS, OLAP systems, and efficient data cube computation. Publications highlight contributions to database systems, parallel processing, and cloud-oriented software design. Notable works include work on real-time OLAP systems, NOX OLAP algebra optimization, and scalable data cube generation.

Gösta GrahneView profile

Professor

• Database Theory
• Data Mining
• Graph Databases
• +4 more

Gösta Grahne is a Professor in the Department of Computer Science at Concordia University, Montreal, Canada. He holds a Ph.D. from the University of Helsinki and completed a postdoctoral fellowship at the University of Toronto. His research focuses on database theory, data mining, and systems for managing incomplete information and uncertainty. He is affiliated with the Concordia Database Systems Research Group. Education: Ph.D., University of Helsinki (1989) Postdoctoral Fellow, University of Toronto (1990–1992) Research Interests Dr. Grahne's work spans database theory , data integration , and uncertainty management . He has contributed to foundational areas such as regular path queries, XML processing, and probabilistic databases. His recent projects explore provenance tracking and formal methods for data exchange. His publications span over three decades, with notable contributions to conferences like PODS and ICDT. He maintains an active research group and collaborates internationally on theoretical and applied database challenges.

Mohamad El-HajjView profile

Associate Professor

• Data Mining
• Databases
• Data Warehousing
• +5 more

Dr. Mohamad El-Hajj is an Associate Professor in the Department of Computer Science at MacEwan University. He holds a PhD from the University of Alberta (2006) and a Master's from the Lebanese American University (1998). His research focuses on applying data mining techniques to real-world domains such as healthcare, engineering, education, and sports analytics. His expertise spans databases, data warehousing, AI, parallel programming, and e-learning systems. Educational Background PhD in Computer Science, University of Alberta (2006) M.Sc. in Computer Science, Lebanese American University (1998) Bachelor's in Computer Science, Beirut Arab University (Lebanon) Research Interests Dr. El-Hajj specializes in leveraging data mining for practical applications. His work bridges computational methods with societal challenges, including health informatics (e.g., predicting user behavior in health apps), sports analytics (e.g., NBA performance modeling), and urban data analysis (e.g., traffic patterns and crime prediction). He also explores parallel computing and database optimization for large-scale datasets. Recent Trends in Publications His recent work emphasizes interdisciplinary applications: 2023–2025 articles address smart grid energy profiling, sports strategy optimization, and pandemic policy analysis. Earlier contributions (2003–2017) focus on foundational data mining algorithms, including parallel pattern mining and constraint-based techniques. Professional Engagement He actively participates in conferences like IEEE ICDM and ACS/IEEE AICCSA. His teaching includes courses on database systems and data mining methodologies. Advising & Collaboration Open to supervising senior student research projects, though no current advisees are listed. No grants or lab affiliations explicitly mentioned.

Omar Abdul WahabView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence
• Internet of Things
• +3 more

Omar Abdul Wahab is an Assistant Professor at the Department of Computer Engineering and Software Engineering, Polytechnique Montréal. He holds a PhD in Information Systems Engineering from Concordia University (2017) and completed postdoctoral research at École de technologie supérieure (2017-2018) in collaboration with Rogers and Ericsson. PhD: Concordia University, Montréal, Canada Master's: Lebanese American University, Beirut, Lebanon Bachelor's: Lebanese University, Tripoli, Lebanon His research focuses on Cybersecurity , Artificial Intelligence , and Internet of Things , with expertise in cloud computing security , federated learning , and game-theoretic security models . He has developed frameworks for threat intelligence coalitions , IoT intrusion detection , and trust-aware client selection in distributed systems. His recent publications emphasize federated learning security , IoT anomaly detection , and trust modeling in collaborative environments. Current supervised students include PhD candidates Sarhad Arisdakessian and Osama Wehbi, and Master's student Ranim Rahali.

Azzedine BoukercheView profile

Professor

• Wireless Communication
• Mobile Ad Hoc Networks
• Sensor Networks
• +5 more

Azzedine Boukerche is a Full Professor and holds a Canada Research Chair in Large-Scale Distributed Simulation Systems and Vehicular Networking (Tier 1) at the University of Ottawa. He is the Founding Director of the PARADISE Research Laboratory. Previously, he held faculty positions at the University of North Texas, McGill University, and Polytechnic of Montreal, and worked as a Senior Scientist at Metron Corporation. His research focuses on wireless communication, mobile ad hoc networks, sensor networks, distributed systems, and large-scale simulation. He has authored multiple books and numerous publications in top-tier conferences and journals. Research Interests: His work spans Wireless Communication , Mobile Ad Hoc Networks , Sensor Networks , Parallel and Distributed Simulation , and Security in IoT/Underwater Systems . His contributions address challenges in network protocols, privacy, and efficient resource management in dynamic environments. Awards: Ontario Distinguished Researcher Award, Canada Research Chair, G. S. Glinski Award, Best Paper Awards (PADS'97, Telecommunication Software 1999). Leadership: Co-founder of QShine Conference, served as General Chair/Program Chair for IEEE/ACM events, and editorial roles in journals like Wireless Networks and Journal of Parallel and Distributed Computing . Labs & Teams: He leads the PARADISE Lab, focusing on innovations in vehicular networks, underwater systems, and distributed simulation. His team collaborates on projects involving autonomous vehicles, smart cities, and secure communication protocols.

Bijan RaahemiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Professor Bijan Raahemi is a Full Professor at the Telfer School of Management, University of Ottawa, and Director of the Digital Transformation and Innovation (DTI) Graduate Program. He holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (1997), an M.Sc. from Sharif University of Technology, and a B.Sc. (Hon.) from Isfahan University of Technology. His academic roles include cross-appointment to the School of Electrical Engineering and Computer Science. Research interests span Artificial Intelligence, Machine Learning, Data Mining, Big Data Analytics, and their applications in Business, Healthcare, and Engineering. He leads the KDD Research Lab, focusing on data-driven solutions for business and technical challenges. His work has been funded by NSERC, MITACS, and industry partners like Bell Canada and Ciena. Awarded the Telfer School of Management Research Excellence Award (2018), Raahemi has published over 85 peer-reviewed articles and holds eight patents in data communications. He teaches courses such as Business Data Mining, Business Intelligence Technologies, and Management Information Systems. Industrial experience includes roles at Nortel, Alcatel-Lucent, and Tropic Networks, focusing on telecommunications and network architecture. Professional memberships include IEEE (Senior Member), ACM, and Professional Engineers of Ontario (PEO).

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +9 more

Ioannis Mitliagkas is an Associate Professor at the University of Montréal's Department of Computer Science and Operations Research, affiliated with the Faculty of Arts and Science. He is a core faculty member at Mila and a staff research scientist at Google DeepMind. His research focuses on machine learning, deep learning, optimization algorithms, and generative models. He holds the Canada CIFAR AI Chair and has led numerous projects on distributed training, causal discovery, and robust generalization. Education: While specific academic degrees are not explicitly listed in the text, his roles imply a PhD in Computer Science. His work bridges theoretical foundations and applied machine learning. Research Interests: Machine learning theory, optimization dynamics, generative models, out-of-distribution generalization, and causal inference. His work emphasizes scalable algorithms for deep learning and distributed systems. Awards: Canada CIFAR AI Chair (2018). Grants & Projects: Lead researcher on over 50 projects funded by CRSNG, MITACS, and others, including grants on optimization dynamics, causal discovery, and industrial applications (e.g., healthcare, energy, robotics). Students: Advised PhD and Master's students on topics like distributional shifts, generative models, and performative prediction.

Peter Allan BuhrView profile

Professor

• Concurrency
• Polymorphism
• Concurrent Monitoring
• +4 more

Peter Allan Buhr is a Professor at the School of Computer Science, University of Waterloo. His primary research focuses on programming languages, concurrency, polymorphism, and tools for monitoring and debugging concurrent systems. He is known for developing μC++ and C∀, extensions to C and C++ that enhance concurrency and type safety. Buhr's work includes contributions to memory-mapped databases (μDatabase), debugging tools (MVD), and high-performance web server architectures. His research interests include concurrency control, lock-free algorithms, type systems, and persistent data structures. He has advised numerous graduate students on topics like C∀ type systems, concurrency patterns, and real-time scheduling. Buhr has published extensively in top-tier journals such as ACM Transactions on Parallel Computing and Concurrency and Computation: Practice and Experience. He leads the C∀ open-source project, aiming to modernize the C language while maintaining backward compatibility. His work on μC++ has been used in undergraduate concurrency courses at Waterloo for decades. Buhr's contributions to concurrency education and research have had a global impact, with tools and frameworks adopted widely in academia and industry.

Tarek AbdelrahmanView profile

Professor

• Parallel Systems
• Parallelizing Compilers
• Optimizing Compilers
• +3 more

Professor Tarek Abdelrahman is affiliated with the University of Toronto as a faculty member in the Department of Electrical & Computer Engineering under the Faculty of Applied Science and Engineering . He earned his PhD in Computer Science and Engineering from the University of Michigan at Ann Arbor in 1989. Research Interests : Parallel Systems Parallelizing and Optimizing Compilers Parallel Programming Models and Support Software Engineering Computer Architecture Distributed Computing Scientific Awards and Recognitions : Jeffery P. Skoll Chair in Software Engineering (2002-2012) Best Paper Awards (2006, 2001) at Parallel and Distributed Computing and Systems Sustained Excellence in Teaching Award (2013) from the Faculty of Applied Science and Engineering ECE Departmental Teaching Awards (2004, 2006, 2007, 2010, 2011) Faculty Teaching Award (2008) from the Faculty of Applied Science and Engineering Professional Memberships : Senior Member of ACM and IEEE; Member of IEEE Computer Society, USENIX Computing Society, and Professional Engineers of Ontario (PEO).

Yuan DingView profile

Professor

• Software Systems
• Distributed Systems
• Large-scale Software Reliability
• +3 more

Professor Yuan Ding is a Canada Research Chair in Systems Software at the University of Toronto , affiliated with the Faculty of Applied Science and Engineering and serving as Chair of the Computer Engineering Group . He joined the Department of Electrical and Computer Engineering in 2013 and was promoted to Professor in 2023. Education: PhD in Computer Science (2012) from University of Illinois at Urbana-Champaign, B.E. in Computer Science and Engineering and B.S. in Mathematics (2006) from Beihang University Research Interests focus on improving the reliability and performance of large-scale software systems , particularly distributed and systems software. His work addresses critical issues like upgrade failures , log compression , performance profiling , and non-intrusive debugging . Key methodologies include empirical studies of real-world failures, static analysis, and runtime optimization. Publication Trends show expertise in operating systems , distributed systems , and software engineering . Recent work includes μSlope (log compression) and Relational Debugging (root-cause analysis), while earlier studies investigated JVM performance and distributed failure patterns . Scientific Awards : Canada Research Chair (2023), McCharles Prize (2018), NetApp Faculty Fellowship (2013–2016), ACM SIGSOFT Distinguished Paper (2011), Saburo Muroga Fellowship (2006–2007), University Gold Medal (2005) Advising & Collaborations : Supervised 15+ PhD and Master’s students, including Yongle Zhang (Purdue), Xu Zhao (Facebook), and Xiang Ren (Northeastern). Founded startup YScope to commercialize log compression tools like CLP , now deployed at Uber.

Gennady PekhimenkoView profile

Associate Professor

• Computer Architecture
• Systems Research
• Applied Machine Learning
• +3 more

Dr. Gennady Pekhimenko is an Associate Professor in the Department of Computer Science and cross-appointed to the Electrical and Computer Engineering department at the University of Toronto. He leads the EcoSystem research group and serves as CEO/Co-Founder of CentML, with affiliations at CIFAR and Vector Institute. PhD in Computer Science from Carnegie Mellon University (2016) His research spans computer architecture, systems optimization, and applied machine learning, with a focus on memory hierarchy efficiency, hardware acceleration, and compiler design. Recent work emphasizes large language model training, GPU virtualization, and processing-in-memory systems. Key article trends include: 1) Memory optimization for transformers and sparse models (2024-2025), 2) GPU resource management (2022-2024), 3) Prompt programming languages (2025), and 4) Deep learning compilation frameworks (2021-2023). Major Awards: NVIDIA Graduate Fellowship, Microsoft Research PhD Fellowship, ISCA Hall of Fame, AWS/Google/VMware research grants Key Collaborations: Onur Mutlu (CMU), Todd Mowry (CMU), Vector Institute researchers

• Concurrent Systems
• Performance Analysis
• Parallel Algorithms
• +3 more

Naama Ben-David serves as an Assistant Professor at the Technion - Israel Institute of Technology, specializing in theoretical foundations for concurrent computing systems. Her work bridges theoretical rigor with practical performance optimization in parallel programming environments. Her academic journey includes: Undergraduate studies at the University of Toronto PhD in Computer Science from Carnegie Mellon University (advised by Guy Blelloch) Three-year postdoctoral research position at VMware Research Research focuses on identifying performance bottlenecks in concurrent programs through mathematical modeling and algorithmic analysis. She develops frameworks for quantifying synchronization costs while exploring novel concurrent data structures and distributed algorithm design. Her methodology emphasizes formal verification of performance guarantees in real-world systems. Major recognitions include: 2022 PODC Doctoral Dissertation Award CMU School of Computer Science Dissertation Award Honorable Mention NSERC PGSD scholarship during doctoral studies Microsoft Research PhD Fellowship Her doctoral research at Carnegie Mellon was fully funded through competitive fellowships, with dissertation work receiving dual institutional recognition. Current research continues this focus on performance modeling while expanding into practical implementations of theoretically sound concurrent systems.

• Microelectromechanical Systems (MEMS)
• Microfluidics
• Microfabrication
• +4 more

Dr. Ian Foulds is an Associate Professor at the School of Engineering under the Faculty of Applied Science at the University of British Columbia Okanagan. His primary research focuses on Microelectromechanical Systems (MEMS) , Microfluidics , and Microfabrication , with applications spanning biomedical diagnostics, geological dating, and terahertz technology. Degrees: PhD (2007), MASc (2004), BASc (2003) in Electrical Engineering from Simon Fraser University Courses Taught: ENGR 353 Semiconductor Devices, APSC 179 Linear Algebra for Engineers His research integrates microfluidic systems with advanced fabrication techniques , including 3D printing and laser ablation, to develop cost-effective diagnostic tools and explore geological processes through high-precision chronology. Recent publications highlight innovations in droplet generation , agglutination assays , and terahertz emitter design , often combining interdisciplinary approaches from engineering and geoscience. Dr. Foulds holds the Principal's Research Chair - Indigenous Reconciliation in Engineering (Tier 1) , emphasizing his commitment to integrating Indigenous knowledges in engineering practices. His work involves collaborations across materials science, biomedical engineering, and geology, supported by technical expertise in MEMS and microfabrication.