Find Academic Supervisors

• Machine Learning
• Inference Acceleration
• World Models
• +4 more

Hongyang Zhang is an Assistant Professor at the University of Waterloo's David R. Cheriton School of Computer Science (part of the Faculty of Mathematics) and a faculty member of Vector Institute for AI. His research focuses on machine learning theory and applications, including inference acceleration for large language models (e.g., EAGLE series), world models for robotics and autonomous systems, and AI security. He leads the SafeAI Lab and is affiliated with the AI Institute and Cybersecurity and Privacy Institute. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2019) Postdoc at Toyota Technological Institute at Chicago (2019–2021) Bachelor's degree from Peking University (2015) Research Interests: Inference acceleration (e.g., EAGLE-3 achieving 5× speedup) World models for infinite-horizon video generation (The Matrix) AI security, adversarial robustness, and watermarking System-2 LLMs for alignment and reasoning Awards & Recognition: 1st place in multiple adversarial vision challenges (NeurIPS 2018, CVPR 2021) AAAI New Faculty Highlights (2023) IEEE Senior Member (2024) Amazon Research Award and WAIC Yunfan Award Academic Leadership: Area Chair for ICML, NeurIPS, ACL, and ICLR Action Editor for Data-centric Machine Learning Research (DMLR) Teaching: Introduction to ML, Robustness of ML, and AI Security courses Labs & Teams: Leads the SafeAI Lab, collaborating on projects like EAGLE, The Matrix, and zkLLM.

Luis RuedaView profile

Professor

• Machine Learning
• Computational Biology
• Bioinformatics
• +3 more

Luis Rueda is a Professor in the School of Computer Science at the University of Windsor, specializing in machine learning applications in bioinformatics and computational biology. His research develops algorithms for cancer biomarker discovery, transcriptomic data analysis, and secure computing frameworks. Rueda's work integrates machine learning with biological data science, creating novel methods for single-cell RNA-seq analysis, cancer subtype classification, and medical image interpretation. Current projects focus on graph neural networks for spatial transcriptomics, federated learning for genomic privacy, and deep learning models for lung nodule detection in CT scans. Recent publications demonstrate emphasis on developing privacy-preserving frameworks for genomic data, advanced neural network architectures for medical image analysis, and computational methods for identifying therapeutic targets in cancer. Articles frequently address multi-modal data integration in oncology diagnostics. Student Mentoring Award, Faculty of Science (2022) Mitacs Accelerate Award (2021, 2020) Ontario Centres of Excellence - TalentEdge Award (2018) Collaborative Interdisciplinary Research Award (2016) Professional affiliations include Senior Member of IEEE and membership in ACM, IAPR, and ISCB. Rueda serves as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics and leads the Machine Learning in Bioinformatics Lab. Research has been supported by Mitacs, Ontario Centres of Excellence, and NSERC grants.

Mohsin JamilView profile

Associate Professor

• Power Electronics
• Control Systems
• Renewable Energy Systems
• +3 more

Mohsin Jamil is an Associate Professor in the Department of Electrical and Computer Engineering within the Faculty of Engineering and Applied Science at Memorial University of Newfoundland. His research specializes in power electronics, control systems, renewable energy integration, and smart grid technologies. Current projects focus on developing innovative power converters for electric vehicles, grid-forming converters for renewable integration, and AI-based control techniques for energy systems. Research spans several key areas: Advanced power electronic converters for energy storage and renewables Control strategies for community EV charging infrastructure Hybrid power systems for remote applications Neural network applications in power management Funded by NSERC, Mitacs, and university grants, his work has resulted in over 150 publications. Recognized as a Senior Member of IEEE and named to Stanford's World's Top 2% Scientists list in 2024, Jamil serves as Associate Editor for IEEE Access and other journals. Courses taught include Power Electronics, Power Systems Protection, and Grid Integration of Energy Systems. Research supervision includes 12+ graduate students working on projects in power converters, microgrid control, and renewable energy systems. Awards include the Best Teacher Award (2016) and Sidney D. Drell Academic Award (2021).

Parimala ThulasiramanView profile

Professor

• High-Performance Computing
• Graph Analytics
• Network Science
• +7 more

Parimala Thulasiraman is a Professor in the Department of Computer Science at the University of Manitoba, affiliated with the Faculty of Science. Her research focuses on high-performance computing, graph analytics, and network science, leveraging bio-inspired algorithms and machine learning. She leads the IDEAS (InterDisciplinary Evolving Algorithmic Science) Lab, which develops innovative optimization techniques for complex systems and scalable algorithms for multicore architectures. Her work spans applications in finance, genomics, and vehicular networks, supported by grants from NSERC, Research Manitoba, and MITACS. She holds senior membership in the ACM and IEEE and has been recognized with the University of Manitoba Teaching Recognition Award. Research interests emphasize evolutionary computation, parallel algorithms, and their real-world applications. Recent projects include GPU-accelerated multi-objective optimization, blockchain-based VANET security, and genomic data analysis using parallel suffix tree methods. Her publications explore cutting-edge topics like coevolutionary systems, ant brood clustering for portfolio management, and knowledge distillation in sentiment analysis. Grants & Awards: NSERC, Research Manitoba, MITACS ACCELERATE; Teaching Recognition Award Labs/Teams: IDEAS Lab (InterDisciplinary Evolving Algorithmic Sciences) Advising and grant activities involve interdisciplinary collaborations, with a focus on training the next generation of computational scientists. Current research trends include post-cloud computing models (dew-blockcloud systems), AI bot optimization, and blockchain applications in finance and transportation.

Doron NussbaumView profile

Associate Professor

• Algorithms
• Computational Geometry
• Computer Graphics
• +8 more

Doron Nussbaum is an Associate Professor at the School of Computer Science (SCS), Carleton University. He is affiliated with Herzberg Laboratories and holds offices in HP5378. His roles include teaching, research supervision, and academic administration. Education: Ph.D. Carleton (2001), M.C.S. Carleton University (1988), B.Sc. Tel Aviv (1985) Teaching: Taught COMP 3501 (Foundations of Game Programming & Computer Graphics) and COMP 5900 (Advanced Computer Gaming) in Fall 2012 Research Interests: Focus on algorithms, computational geometry, medical computing, spatial data modeling (GIS), computer graphics/visualization, parallel/distributed computing, robotics, and graph routing algorithms. Active in the PARADIGM Research Group for spatial data modeling. Office Hours: Previously held weekly hours (e.g., Fall 2011: Thursdays 16:00-17:00; Fall 2012: Mondays 13:00-15:00). Contact via email or phone (613-520-2600 x1390). Projects: Engaged in medical computing, computer gaming, robotics, and geographic information systems. Collaborates on game engine development, shader programming, and collision detection systems.

Nicola SantoroView profile

Research Professor

• Algorithms
• Communication Networks
• Data Structures
• +8 more

Nicola Santoro is a Distinguished Research Professor in the School of Computer Science at Carleton University, Ottawa, Canada. His academic career spans over four decades, with a focus on distributed computing, algorithms, and network systems. He holds a Ph.D. from the University of Waterloo (1979) and a D.Sc. from the University of Pisa (Italy). His research interests include algorithms, communication networks, distributed computing, robotics, graph theory, and wireless communication. He has contributed extensively to the theoretical foundations of distributed systems, mobile robotics, and fault-tolerant computing. Santoro's work emphasizes scalable and efficient solutions for dynamic networks and multi-agent systems. Key contributions include studies on black hole search in dynamic networks, distributed algorithms for autonomous robots, and network decontamination. His publications span over 400 articles in top-tier journals and conferences. He has also authored influential textbooks, such as Design and Analysis of Distributed Algorithms (Wiley, 2007) and Distributed Computing by Mobile Entities (Springer, 2019). Prof. Santoro has advised numerous graduate students and researchers, shaping the next generation of computer scientists. His work bridges theory and practice, with applications in robotics, sensor networks, and parallel computing.

Jean-Pierre CorriveauView profile

Associate Professor

• Object-Oriented Testing
• Text Understanding
• Behavior-Driven Development
• +3 more

Jean-Pierre Corriveau is an Associate Professor and Graduate Director at the School of Computer Science, Carleton University. He holds a Ph.D. in Natural Language Processing from the University of Toronto (1991), an M.C.S. from the University of Ottawa (1984), and a B.Sc. from the University of Ottawa. His research focuses on Object-Oriented Testing (scenario testing, test data generation) and Text Understanding (contextualized and generative comprehension), with recent emphasis on behavior-driven development and acceptance testing of systems with complex scenarios. Dr. Corriveau has extensive industry experience, including roles at Nortel where he contributed to the TELOS project, leading to the ObjecTime startup and subsequent IBM Rational Software Architect tools. His work bridges academia and industry, addressing challenges in software engineering, cybersecurity, and natural language processing. Notable contributions include frameworks for MapReduce optimization, secure localization in wireless sensor networks, and privacy-preserving data anonymization techniques. His publications span topics like AI detection in text, federated learning for NER, intrusion detection systems, and resilient network algorithms. He has advised on graduate studies as Director and contributed to teaching strategies for millennial students through innovative virtual learning environments in platforms like Second Life. His research trends reflect a blend of theoretical advancements in computational linguistics and practical software engineering solutions, with a recurring focus on system reliability, distributed computing, and privacy-preserving technologies. Grants and collaborations are implied through his tool development and industry partnerships, though specific grant details aren’t explicitly listed. Labs/teams associated with his work include the ObjecTime initiative and Carleton’s distributed systems research groups, though no dedicated lab names are mentioned. Future directions emphasize scalable testing methodologies and ethical AI integration in text analysis.

Mojtaba AhmadiView profile

Professor

• Robotic analysis, design, and control
• Machine and biological locomotion
• Mechatronics
• +5 more

Mojtaba Ahmadi is a Professor in the Department of Mechanical and Aerospace Engineering at Carleton University, cross-appointed within the Faculty of Engineering and Design. He holds a B.Sc. from Sharif University, an M.Sc. from the University of Tehran, and a Ph.D. from McGill University. His research focuses on robotics, mechatronics, and control systems with applications in rehabilitation, aerospace, and biologically inspired systems. Key areas include robotic locomotion, real-time control, and neuromorphic computing. He has contributed to advancements in FPGA-based neural network implementations, memristor circuits, and soft actuators. Dr. Ahmadi has served on technical committees for conferences such as CA2008, ICA2009, and CSME 2008, and organized special sessions on control applications. His work bridges theoretical models with practical hardware solutions, emphasizing interdisciplinary approaches. Publications span topics from spiking neural networks and neuromorphic vision systems to low-resource digital neuron implementations. He actively explores applications in healthcare robotics and energy-efficient computing architectures.

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.