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Xiaodong LiangView profile

Professor

• Power systems dynamics
• Renewable energy integration
• Synchrophasor technology and smart grids
• +4 more

Dr. Xiaodong Liang is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan, holding the prestigious Canada Research Chair in Technology Solutions for Energy Security in Remote, Northern, and Indigenous Communities. His research focuses on critical power system challenges with direct applications to remote community energy security. Education: Bachelor of Engineering (BEng) Master of Engineering (MEng) Master of Science (MSc) Doctor of Philosophy (PhD) Dr. Liang's research spans power systems dynamics, renewable energy integration, synchrophasor technology, smart grids, power electronics applications, and protection systems. His work addresses grid stability in renewable-rich environments, microgrid operation in remote areas, and corrosion issues in transmission infrastructure. Recent publications emphasize practical solutions for northern and indigenous communities, including inverter-based microgrid protection and machine learning applications for fault detection. Analysis of his 2023-2025 publications reveals dominant themes in microgrid protection (particularly for inverter-based systems), renewable integration challenges, advanced control techniques, and corrosion modeling. His work consistently targets remote community applications, with strong emphasis on real-time simulation validation using OPAL-RT platforms and practical field implementation. Scientific Awards: Canada Research Chair in Technology Solutions for Energy Security in Remote, Northern, and Indigenous Communities As a Canada Research Chair holder, Dr. Liang leads significant federally funded research addressing energy security gaps in remote communities. His program involves collaborations with indigenous groups, northern communities, and industry partners to develop deployable technology solutions. His lab utilizes advanced real-time simulation capabilities for testing microgrid control strategies and protection schemes under realistic conditions. Dr. Liang's research group operates within the University of Saskatchewan's Engineering Building, utilizing specialized power systems laboratories equipped with OPAL-RT real-time simulators for hardware-in-the-loop testing of microgrid components and protection systems. Current work focuses on enhancing resilience of remote microgrids through AI-driven control and novel protection architectures.

Ulrike StegeView profile

Associate Professor

• Computational Biology
• Parameterized Complexity
• Design of Heuristics
• +4 more

Ulrike Stege is an Associate Professor and Director of the Master of Engineering in Applied Data Science (MADS) at the University of Victoria's Faculty of Engineering and Computer Science. She holds a PhD from the Swiss Federal Institute of Technology (ETH Zurich). Her research spans computational biology, parameterized complexity, algorithm design, graph theory, and cognitive psychology. She leads initiatives in quantum computing frameworks and educational tools, including projects like SCOOP (quantum optimization) and QGrover (quantum algorithm visualization). Her work also addresses RNA pseudoknot structure prediction and the integration of quantum computing into combinatorial optimization software. Key educational contributions include developing browser-based quantum learning tools (e.g., QNotation and QuantumCrypto) and promoting computational thinking in K-12 education. Her research bridges theoretical computer science with practical applications in biology and quantum systems, emphasizing algorithmic innovation and interdisciplinary collaboration. Her publications focus on advancing quantum computing frameworks, optimizing bioinformatics algorithms, and creating accessible educational resources. Recent work addresses quantum annealing for constrained optimization, structural biochemistry of viral RNA, and hybrid quantum-classical problem-solving methods.

David BristowView profile

Associate Professor

• Resilient Infrastructure
• Sustainable Cities
• Climate Change Adaptation
• +6 more

Dr. David Bristow is an Associate Professor and Director of the Master of Engineering in Industrial Ecology program at the University of Victoria's Department of Civil Engineering. He holds a MASc and PhD from the University of Toronto and is a Professional Engineer (P.Eng.). His research focuses on resilient infrastructure systems, sustainable cities, and climate change adaptation, integrating engineering principles with ecological and thermodynamic approaches. He teaches courses on resilient smart cities and sustainable engineering. Research interests include recovery planning for infrastructure resilience, multi-objective decision-making for sustainability, and the science of urban systems. He develops frameworks for assessing climate impacts on buildings, disaster risk reduction strategies, and hygrothermal performance analysis of building envelopes. His work emphasizes interdisciplinary methodologies like Bayesian networks, FMEA, and agent-based modeling. Recent publications address climate resilience of power grids, flood-risk management in Metro Vancouver, and circular economy financing for waste systems. His research contributes to both academic discourse and practical policy tools for urban resilience. Dr. Bristow collaborates on regional disaster planning initiatives and has advised on Canada’s energy transition strategies. Professional activities include leading the Industrial Ecology program and serving as a key figure in UVic's Civil Engineering department. His office is located in ECS 310, and he can be reached at dbristow@uvic.ca.

Youmin ZhangView profile

Professor

• Fault Diagnosis and Tolerant Control
• Avionics and Flight Control
• UAV Systems and Autonomous Vehicles
• +5 more

Youmin Zhang is a Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University, affiliated with the Concordia Institute of Aerospace Design & Innovation. His research focuses on advanced control strategies for safety-critical systems, including fault diagnosis, fault-tolerant control, and autonomous vehicle systems. His work spans aerospace, industrial, and mechatronic domains, with applications in UAV navigation, spacecraft formation flying, and real-time environmental monitoring. Research interests include: Fault-tolerant control for safety-critical systems Avionics and flight control systems UAV trajectory planning and swarm coordination Modeling and simulation of physical systems Signal processing for diagnostics Recent publications emphasize resilient control architectures, cybersecurity in autonomous systems, and AI-driven solutions for fault detection in industrial processes. His work bridges theoretical control frameworks with practical applications in aerospace, energy, and environmental systems. He holds an academic position at Concordia’s Sir George Williams and Loyola campuses, and his research contributes to advancing autonomous systems and smart manufacturing technologies.

Nizar BouguilaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +7 more

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +5 more

Eugene Belilovsky is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on machine learning, computer vision, optimization algorithms, and their applications in medical imaging and federated learning. He explores topics such as continual learning, model merging, and bias mitigation in deep learning systems. His work bridges theoretical advancements with practical applications, including healthcare diagnostics and efficient training strategies for large models. Belilovsky's recent publications emphasize innovative methods like MuLoCo for inner optimizer design, FairDropout for enhancing minority group generalization, and techniques to scale multi-task learning with sparse masks. His studies on federated learning explore incentive mechanisms for decentralized systems and robust pre-training of large language models. His contributions to 3D reconstruction and motion prediction highlight interdisciplinary applications in computer graphics and generative models. His research trends reflect a strong emphasis on improving model efficiency, fairness, and adaptability across diverse domains. While no scientific awards are explicitly listed, his prolific publication record underscores his impactful contributions to AI and machine learning.

Reza MirsalariView profile

Part-Time Lecturer

• Software Engineering
• Cloud Computing
• Distributed Systems
• +5 more

Dr. Reza Mirsalari is a Part-Time Lecturer in the Department of Computer Science and Software Engineering at Concordia University. He teaches courses covering Full-Stack Web Development (Java, .NET, PHP), Mobile App Development (iOS/Android), Game Development with Unity, and Cloud Software Development using Azure/AWS. His research focuses on advancements in software engineering, including AI's impact on development processes, software quality metrics, large-scale architectural design, team dynamics in agile environments, and API integration strategies. Teaching emphasizes practical application of technologies like Spring Boot, ASP.NET Core, Laravel, Unity, and Linux system administration. Research activities include exploring cloud computing patterns, microservices, and methodologies like Waterfall vs. Agile. He is also involved in software measurement techniques and big data analytics for extracting actionable insights. Dr. Mirsalari does not currently supervise theses. No specific grants or labs are listed in the profile.

Yogendra P. ChaubeyView profile

Professor

• Sampling
• Distribution Theory
• Survival Analysis
• +3 more

Dr. Yogendra P. Chaubey is a Professor in the Department of Mathematics and Statistics at Concordia University, Montréal, Canada. Holding a Ph.D. from the University of Rochester (1977), his research focuses on statistical methodology with emphasis on sampling theory, distribution modeling, and survival analysis. Education: Ph.D., University of Rochester (1977) His work spans nonparametric density estimation for circular and non-negative data, wavelet-based noise reduction in biological imaging, entropy estimation, left-censored spatial modeling (e.g., arsenic contamination), and inverse Gaussian distributions. Recent collaborations include applications in protein classification and public health. Dr. Chaubey has published extensively on statistical theory and methodology, including corrections and extensions to classical estimators. His teaching includes advanced courses in sample survey theory and applications.

Sanjeena DangView profile

Associate Professor

• Computational Statistics
• Biostatistics
• Bioinformatics
• +4 more

Sanjeena Dang is an Associate Professor and Canada Research Chair in Data Science and Analytics at Carleton University's School of Mathematics and Statistics. She holds a PhD in Statistics from the University of Guelph (2012). Her research focuses on clustering and classification of high-dimensional data, particularly in bioinformatics, with applications to RNA-seq, microbiome, and microarray datasets. She has led projects supported by NSERC Discovery Grants and the Canada Research Chair Program. Education: PhD in Statistics, University of Guelph (2012) Her research interests span computational statistics, biostatistics, machine learning, and mixture models. She develops novel statistical methodologies to analyze complex biological data, emphasizing high-dimensional data analysis and interpretable models. Recent work includes cluster-weighted factor analyzers for socioeconomic crime rate analysis and Gaussian mixture models for changepoint detection. Her publications highlight advancements in microbiome data clustering, RNA-seq analysis, and polygenic adaptation studies. She has also explored applications in public health, such as obesity prevention interventions. Awards: Canada Research Chair in Data Science and Analytics Grants: NSERC Discovery Grant, CRC Program Funding Her research group collaborates on interdisciplinary projects, applying data science to biological and health domains. She contributes to statistical education through courses and mentorship in data analytics.

Tarun SheelView profile

Assistant Professor

• Computational Fluid Dynamics
• Scientific Computing
• Numerical Analysis
• +3 more

Dr. Tarun Sheel is a Teaching Assistant Professor in the Department of Mathematics & Statistics at Memorial University of Newfoundland. With a PhD in Mechanical Engineering from Keio University, his research focuses on computational fluid dynamics, vortex methods, and high-performance computing techniques. His expertise includes developing accelerated vortex methods using Fast Multipole Method (FMM) and special-purpose computers like MDGRAPE systems. Current research investigates scour reduction at bridge piers using RANS turbulence modeling and bluff body flow control. Additional work spans parallel mesh generation, fluid-structure interaction, and multiphase flow simulation. Dr. Sheel's publications demonstrate consistent focus on computational acceleration techniques, with applications ranging from turbulent flow simulation to geotechnical problems like submarine landslides. His work integrates numerical analysis with hardware-specific optimizations for scientific computing. Teaching responsibilities include calculus, linear algebra, differential equations, and numerical methods across undergraduate and graduate levels at multiple institutions internationally.

• Quantum Mechanics
• Nuclear Physics
• Computational Physics
• +3 more

Wytse Van Dijk is an Adjunct Professor in the Department of Physics & Astronomy at McMaster University. His research focuses on quantum mechanics, computational physics, and theoretical studies of nuclear and atomic systems. He has published extensively on numerical solutions of the Schrödinger equation, quantum tunneling, and quantum information processing. Research Interests : Quantum systems simulation, numerical methods in quantum mechanics, quantum computing algorithms, nuclear decay processes, and many-body systems like Fermi and Bose gases. His work bridges theoretical physics with computational techniques, addressing challenges in wavepacket dynamics and time-dependent quantum phenomena. Articles Trends : Recent work emphasizes numerical methods for quantum systems, including efficient algorithms for the time-dependent Schrödinger equation and quantum search algorithms. He has also explored quantum backflow, tunneling effects, and unitary Fermi gas properties. Co-Authors : Collaborations with Yukihisa Nogami and Melvin Preston highlight his network in theoretical and computational physics. His research spans over 50 years with 94+ publications (1967–2024).

Ramiro LiscanoView profile

Professor

• Pervasive and Mobile Computing
• Distributed Computing
• Sensor Networks
• +4 more

Dr. Ramiro Liscano is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University's Faculty of Engineering and Applied Science. He holds a PhD in Systems Design Engineering from the University of Waterloo (1998), an MScEng in Mechanical Engineering from the University of Rhode Island (1984), and a BScEng in Mechanical Engineering from the University of New Brunswick (1982). His research focuses on Pervasive and Mobile Computing (including service discovery and security), Distributed Computing (peer-to-peer, web services, grid services), and Sensor Networks (wireless/Internet interoperability). He teaches courses such as Network Design, Pervasive and Mobile Computing, and Software Design Fundamentals. His work includes significant contributions to patent-pending innovations in presence-aware telephony systems, context-aware call handling, and service discovery protocols. Dr. Liscano is a Professional Engineer (PEng) and a Senior Member of IEEE (SMIEEE). Education: PhD (Systems Design Engineering), University of Waterloo, 1998 MScEng (Mechanical Engineering), University of Rhode Island, 1984 BScEng (Mechanical Engineering), University of New Brunswick, 1982 Research Themes: His work bridges theoretical and applied aspects of distributed systems, emphasizing practical implementations in mobile computing environments. Recent efforts include developing interoperable sensor networks and secure service discovery mechanisms. Patents: Over 10 patent filings in areas such as context-aware telephony systems, presence services, and distributed application messaging. Notable filings include interactive conflict resolution for personalized services and availability predictors using call processing data.

John FriedlanView profile

Associate Professor

• Accounting
• Financial Reporting
• Auditing
• +3 more

Dr. John Friedlan is an Associate Professor of Accounting at Ontario Tech University’s Faculty of Business and Information Technology, where he also serves as Program Director, Commerce. With over 20 years of experience, he has taught at York University’s Schulich School of Business and holds a PhD from the University of Washington. His research focuses on critical analysis of financial reporting, managerial accounting, and the integration of business analytics into decision-making. Dr. Friedlan has been recognized for his teaching excellence, including the Educator of the Year Award (1992) and the Seymour Schulich Award for Teaching Excellence (2000). His educational background includes a Bachelor of Science from McGill University, an MBA from York University, and a PhD from the University of Washington. Prior to academia, he worked at Nabisco Brands and served on the Board of Examiners at the Canadian Institute of Chartered Accountants, qualifying as a Chartered Accountant in 1980. Dr. Friedlan’s research explores topics such as supply chain resilience, risk-averse decision-making, and disruption management. His work bridges theoretical frameworks with practical applications, leveraging computational methods in operations research and analytics. Recent articles address challenges in electric vehicle infrastructure planning, supply chain recovery strategies, and environmental disclosure practices. He actively contributes to practitioner journals like CGA Magazine and CA Magazine , emphasizing real-world relevance. His scientific contributions include over 15 peer-reviewed articles, with a focus on optimizing complex systems under uncertainty. Beyond research, Dr. Friedlan advocates for experiential learning, integrating industry partnerships to prepare students for data-driven business environments.

Randy FortierView profile

Academic

• Software Security
• Web/Mobile Development
• Computer Graphics
• +3 more

Randy Fortier is an Associate Teaching Professor and Associate Dean of Science (Undergraduate) at Ontario Tech University's Faculty of Science. He specializes in Computer Science education with a focus on software security, web/mobile development, graphics, and game development. His academic career includes teaching roles at the University of Windsor (1997-2003) and Thompson Rivers University (2003-2013). Education: BSc (Honours Computer Science, Software Development) from University of Windsor (1997), MSc (Computer Science) from University of Windsor (2003). Research interests span software security, mobile/web development, computer graphics, and game development. His earlier work focused on natural language processing and distributed virtual environments. His publications explore semantic web technologies, mobile speech interfaces, and scalable distributed systems architectures. Teaching portfolio includes over 15 courses ranging from introductory programming to advanced topics like interactive media and simulation modeling. He currently holds administrative leadership in undergraduate science education.

• Distributed Systems
• Peer-to-Peer Networks
• Mobile Device Applications
• +3 more

Dr. Ying Zhu is an Associate Professor in the Department of Information Technology at Ontario Tech University, cross-appointed with the Faculty of Engineering and Applied Science. She holds a PhD in Computer Engineering from the University of Toronto, with earlier degrees in Computer Science from both the University of Toronto and Dalhousie University. Education: PhD in Computer Engineering (University of Toronto), MSc in Computer Science (University of Toronto), BSc in Computer Science and Mathematics (Dalhousie University) Her research focuses on distributed systems, peer-to-peer networks, and mobile device applications. She develops algorithms and protocols for optimizing network performance and enhancing user experience in heterogeneous environments. Her work bridges computer engineering principles with practical applications in mobile and wireless systems. Recent publications emphasize optimization in supply chain resilience, electric vehicle infrastructure planning, and risk-averse decision-making under uncertainty. These studies highlight her interdisciplinary approach to solving complex technical and operational challenges. Dr. Zhu collaborates with industry partners to translate theoretical insights into actionable solutions for real-world systems.