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Dennis K. PetersView profile

Professor

• Software Verification
• High-Performance Computing
• Parallel Processing
• +2 more

Dennis K. Peters is a Professor in Electrical and Computer Engineering at Memorial University, currently serving as Interim Associate Vice-president (Academic). His research spans software verification, high-performance computing, and marine simulation. With a PhD from McMaster University, he focuses on real-time systems and parallel processing applications in marine environments. Recent work includes unsupervised clustering for geological data and ship-iceberg discrimination using convolutional neural networks. His publications demonstrate consistent innovation in applying parallel computing to maritime challenges. Awards include the ECEDHA CHECE Leadership Award and IEEE J. J. Archambault Merit Award. As an active professional volunteer, he has chaired PEGNL and IEEE Newfoundland and Labrador Section. He teaches courses in software design and concurrent programming.

• Remote Sensing
• Machine Learning
• Satellite Image Processing
• +4 more

Masoud Mahdianpari is a Cross-appointed Professor between Memorial University's Department of Electrical and Computer Engineering and C-CORE, where he serves as Remote Sensing Technical Lead. He holds a PhD in Electrical Engineering from Memorial University, complemented by graduate degrees from the University of Tehran. His research specializes in advanced remote sensing techniques, including PolSAR data processing, multi-sensor fusion, and deep learning applications for environmental monitoring. Primary research domains encompass wetland ecosystem mapping, agricultural surveillance, and Arctic landscape analysis using satellite data and geo big data platforms. Mahdianpari's publications demonstrate a consistent focus on machine learning applications in earth observation, with recent work emphasizing wetland biomass quantification, methane emission tracking, and wildfire prediction using convolutional neural networks and transformer architectures. T. David Collett Best Industry Paper Award (2020) ESRI Map Award for 'Big Data for Big Country' (2020) Microsoft AI for Earth Grant (2018-2019) Outstanding Reviewer Award for Remote Sensing of Environment (2018) RDC Ocean Industries Student Research Award (2016-2019) PhD Comprehensive Examination Pass with Distinction (2017) He coordinates research initiatives at C-CORE's remote sensing division and mentors graduate students in the GeoBigData Laboratory. Current projects include developing novel CNN-Transformer hybrid models for wetland classification and satellite-based methane monitoring systems.

Sheela RamannaView profile

Professor

• Artificial Intelligence (AI)
• Machine Learning
• Multimodal Information Processing
• +5 more

Dr. Sheela Ramanna is a Professor and Chair of the ACS Graduate Program in the Department of Applied Computer Science at the University of Winnipeg, and an Adjunct Professor at the University of Manitoba. She holds a Ph.D. in Computer Science from Kansas State University, and completed her earlier education at Osmania University, India. Her research focuses on AI, machine learning, and soft computing with applications in natural language processing, multimodal information processing, and topological data analysis. She has been recognized with numerous awards, including multiple UW Merit Awards and Senior Member status in the International Rough Set Society. Dr. Ramanna leads projects funded by NSERC and MITACS, including work on precipitation forecasting, microplastics analysis, and cannabinoid medicine studies. She supervises over 20 graduate students and has authored/co-authored over 150 publications in top journals/conferences. Key roles include editorial positions for EAAI and KES journals, and program chairs for major conferences like IJCRS and RSCTC. Education: B.S. Electrical Engineering (Osmania University), M.S. Computer Science (Osmania University), Ph.D. Computer Science (Kansas State University) Research Interests: Machine Learning, Natural Language Processing, Multimodal Deep Learning, Computational Topology, Rough Set Theory, Social Network Analysis Grants & Projects: NSERC Alliance/Engage Grants, MITACS Accelerate Projects, WeatherLogics collaborations on precipitation forecasting and road condition mapping

Kristen SchellView profile

Associate Professor

• Wind power modeling and forecasting
• Power systems planning
• Renewable energy policy design
• +3 more

Professor Kristen Schell is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Carleton University, joining in July 2020. She holds dual PhDs in Engineering from Carnegie Mellon University and the University of Porto, alongside degrees from Johns Hopkins and Carnegie Mellon. Her research focuses on renewable energy integration, including wind power modeling, electricity market dynamics, and policy design for sustainable energy systems. B.S. Chemical Engineering - Carnegie Mellon University M.S.E. Environmental Engineering - Johns Hopkins University Dual Ph.D. Engineering - Carnegie Mellon University & University of Porto Her work combines machine learning and optimization to address energy transition challenges, leveraging empirical datasets from utilities and climate models. She previously held roles at Rensselaer Polytechnic Institute (RPI) and Polytechnique Montréal, contributing to a US DOE ARPA-E grant. Currently leads the APEX lab, offering a PhD position in Physics-informed Deep Learning for Wind Power. No individual scientific awards listed, though part of team-awarded DOE funding. Active in federal-to-community energy policy advising.

Zinovi RabinovichView profile

Assistant Professor

• Artificial Intelligence
• Multi-Agent Systems
• Reinforcement Learning
• +6 more

Zinovi Rabinovich is an Assistant Professor in the School of Computer Science at Carleton University, Ottawa. He previously held positions at Nanyang Technological University (Singapore), Mobileye Vision Technologies, Bar-Ilan University (Postdoctoral Researcher), and the University of Southampton (Research Fellow). He earned his Ph.D. in Computer Science from the Hebrew University of Jerusalem in 2008. Education: Ph.D. in Computer Science, Hebrew University of Jerusalem, 2008 Research Interests: Focuses on manipulating decision behavior and perceptual control in AI systems, particularly in adversarial scenarios. Current work addresses Behavior Cultivation (Backdoor attacks/Environment Poisoning) against Reinforcement Learning, leveraging deep learning, game theory, and evolutionary methods. Key themes include scalability, stealth, and applicability of attacks across learning frameworks. Projects include Choice Manipulation and Security Games and Mixed Initiative DCOPs under grants from the Ministry of Education (Singapore). Awards: None explicitly listed. Advising & Grants: Advises PhD students: Hang Xu, Rundong Wang, Ridhima Bector Recipient of AcRF Tier-1 grant for Mixed Initiative DCOPs research Labs/Teams: Leads The Lab of Z , focusing on ethically grounded AI and its societal implications.

Michel BarbeauView profile

Professor

• Wireless Communications and Networking
• Underwater Communications
• Quantum Communications
• +4 more

Michel Barbeau is a Professor and Director at the School of Computer Science, Carleton University. He holds a Ph.D. in Computer Science from Université de Montréal (1991) and has held academic roles at Université de Sherbrooke (1991–1999) and a visiting position at the University of Aizu, Japan. His research focuses on non-classical wireless networks, including underwater acoustic communication systems, quantum networks, and resilience assessment of cyber-physical systems. He leads projects involving acoustic signal processing, underwater node communication, and challenges like multipath propagation in aquatic environments. His team tested prototypes in Ottawa’s Rideau Canal. His work spans underwater communication protocols, quantum encryption, and AI-driven network optimization. He is active on YouTube and Twitter, and his ORCID profile highlights interdisciplinary contributions. His research emphasizes practical applications in environmental monitoring, coastal navigation, and secure quantum communication frameworks. Education: Ph.D. Computer Science, Université de Montréal (1991) M.Sc. Computer Science, Université de Montréal (1987) B.Sc. Computer Science, Université de Sherbrooke (1985) Research Interests: Underwater Acoustic Networks Quantum Communications and Cryptography Cyber-Physical Systems Resilience Ad Hoc and Mobile Networks Machine Learning for Network Optimization His current projects address challenges such as signal interference in underwater environments and developing quantum-safe cryptographic algorithms. Collaborations involve interdisciplinary teams of students and researchers. Advancement and Grants: Lead projects on underwater surveillance systems Explores quantum encryption protocols for post-quantum security Investigates AI-driven solutions for UAV and MIMO networks Labs and Teams: His research group focuses on experimental prototyping, including underwater acoustic networks and quantum communication frameworks. Collaborations span academia and industry for real-world applications.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Abbas Akkasi is a Research Fellow at the School of Computer Science, Carleton University. His research focuses on advancing artificial intelligence, machine learning, natural language processing (NLP), and accessibility technologies. He has contributed to projects like TactileNet, which uses AI to create tactile graphics for visually impaired individuals, and has explored biomedical informatics applications in clinical text analysis and symptom recognition. His work bridges multiple disciplines, including multimodal learning, semantic analysis, and generative AI. Notable contributions include developing methods for job description parsing, reference-free summarization evaluation, and improving chemical named entity recognition through undersampling techniques. He has also engaged with interdisciplinary efforts in telecommunications (e.g., LTE-D2D for connected cars) and grid computing optimization. Key research themes include leveraging large language models (LLMs) for biomedical NLP tasks, improving accessibility through assistive technologies, and enhancing machine learning algorithms for imbalanced datasets. His publications reflect a commitment to both theoretical advancements and practical applications of AI across healthcare, education, and industry. Abbas has collaborated with institutions such as the TakeLab (as indicated in his SemEval-2018 participation) and has authored over 20 peer-reviewed articles since 2015. His work emphasizes innovation in NLP, computer vision, and interdisciplinary problem-solving.

Lizhi LiaoView profile

Assistant Professor

• Software Engineering
• DevOps
• Performance Testing
• +4 more

Lizhi Liao is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN). He received his Ph.D. in Electrical and Computer Engineering from the University of Waterloo under Dr. Weiyi Shang. His research focuses on software performance engineering, DevOps practices, and AI system quality, particularly addressing performance regression detection and optimization in fast-paced development environments. Education: Ph.D., Electrical and Computer Engineering, University of Waterloo (supervisor: Dr. Weiyi Shang). Previous roles include Research Intern at ERA Environmental Management Solutions (2019–2024) and Software Developer Intern at Nakisa (2019). Research interests include software performance engineering, web GUI testing, and AI-driven performance analysis. His work emphasizes bridging architectural models with performance data to improve testing efficiency. Recent awards include the ACM SIGSOFT Distinguished Paper Award (2025) and multiple doctoral awards from the University of Waterloo. Teaching includes AI-related courses at MUN and guest lectures on software architecture and testing at the University of Waterloo. He has served as a program committee member for conferences like ICSE, FSE, and ESEC, and co-chaired workshops such as LTB 2025. He is actively involved in the Standard Performance Evaluation Corporation (SPEC) Research Group, focusing on DevOps performance standards. Liao collaborates with industry partners to apply performance analytics in real-world systems, such as database-centric applications and serverless architectures. His presentations span global venues including ICSE, FSE, and the Consortium for Software Engineering Research.

Jian TangView profile

Professor

• Database Systems
• Distributed Computing
• Data Mining
• +3 more

Dr. Jian Tang is a Professor in the Department of Computer Science at Memorial University of Newfoundland. He holds a M.Sc. from the University of Iowa and a Ph.D. from Penn State University. His primary affiliation is within the Faculty of Science. His research focuses on database systems, distributed computing, data mining, and IoT security. Key projects include fault tolerance in distributed systems, workflow management, intrusion detection in IoT networks, and machine learning applications in healthcare and genomics. He has published extensively on topics like autonomous IoT security systems, feature selection methods for microarray data, and probabilistic resource scheduling in grid environments. Dr. Tang’s work bridges theoretical foundations with practical implementations, particularly in cybersecurity, bioinformatics workflows, and scalable distributed systems. His recent publications emphasize real-time autonomous IoT security solutions and machine learning-driven predictive models for healthcare outcomes.

Marc St-HilaireView profile

Professor

• Telecommunication Network Planning
• Wired and Wireless Communication Networks
• Mobile Computing
• +7 more

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.

Eranga UkwattaView profile

Assistant Professor

• Medical Image Analysis
• Image Segmentation
• Myocardial Tissue Characterization
• +4 more

Eranga Ukwatta is an Assistant Professor at Carleton University's Department of Systems and Computer Engineering within the Faculty of Engineering and Design. He joined Carleton in 2016 after postdoctoral fellowships at Johns Hopkins University and Sunnybrook Research Institute. His research focuses on medical image analysis, with specialties in cardiac imaging, histopathology analysis, and machine learning applications. He leads the Medical Image Analysis Lab and advises multiple PhD, master’s, and undergraduate researchers. Education: PhD in Biomedical Engineering, Western University (2013) MESc in Electrical and Computer Engineering, Western University (2009) BSc in Electronic and Telecommunications Engineering, University of Moratuwa (2005) Research Interests: Medical Image Analysis, Image Segmentation, Myocardial Tissue Characterization, Machine Learning, Digital Histopathology, Computational Cardiac Modeling. Key Contributions: Developed algorithms for 3D myocardial scar segmentation in cardiac MRI. Advanced methods for neonatal cerebral ventricle segmentation in ultrasound. Explored AI-driven solutions for histopathology analysis (e.g., Hirschsprung’s disease). Grants & Awards: NSERC Faculty Research Award (2018/19) OCE VIP + NSERC Engage Grant (2017) Ontario Trillium Scholarship ($160k, 2017) Multiple industry collaborations (e.g., Ericsson Canada). Lab & Team: Supervises a dynamic team of graduate and undergraduate students working on projects like deep learning for medical imaging, placental pathology analysis, and cardiac electrophysiology modeling.

Fang X. WuView profile

Professor

• Artificial intelligence
• Machine/deep learning
• Health informatics
• +7 more

Fang X. Wu is a Professor and Graduate Chair in Mechanical Engineering at the Division of Biomedical Engineering, University of Saskatchewan. He holds offices at Room 3B42, Engineering Building, 57 Campus Drive, Saskatoon, SK S7N 5A9. His work focuses on artificial intelligence, medical image analytics, and computational biology with applications in health informatics. Education: BSc/MSc from Dalian University of Technology (DLUT), and PhDs from Northwestern Polytechnical University (NWPU) and the University of Saskatchewan. He is a Professional Engineer (PEng). Research interests include machine learning theory, medical image analysis, drug repositioning, and biomarker discovery. Current projects involve predictive modeling for hepatocellular carcinoma recurrence, complex network control algorithms, and real-time mass spectrometry methodologies. He leads research groups in Bioinformatics, Artificial Intelligence, and Control Systems. Graduate student positions are available in AI and digital health. His lab develops methodologies for bionetwork controllability and big bio-data analysis.

Yvonne CoadyView profile

Professor

• Aspect-oriented software development
• Scalable system infrastructures
• Distributed virtualization
• +7 more

Yvonne Coady is a Professor in the Department of Computer Science at the University of Victoria. She leads the MOD(ularity) Squad, focusing on system infrastructure modularity, mixed reality systems, and geospatial computing. Her research spans scalable distributed systems, immersive technologies, and AI-driven applications. She has co-authored over 170 papers and supervised numerous graduate students. Research interests include citizen science integration, advanced software modularity, and cloud computing architectures. Collaborations include projects with IBM, Ericsson, and Urthecast, addressing Earth observation, urban planning, and virtual reality analytics. Recognized with the 2015 Knowledge Mobilization Award, her work emphasizes real-world impact through industry partnerships. Current projects explore immersive applications via VR/AR, such as the RAVR and SAVR initiatives, and accessibility-enhanced interfaces. She also investigates geospatial computing for environmental monitoring and disaster response. Awards and grants include NSERC Collaborative Research and Mitacs Accelerate funding. Grants: NSERC grants (2023-2024), Mitacs projects (2020-2024) Labs/Teams: MOD(ularity) Squad, RAVR (Research in Augmented and Virtual Reality) Key Collaborations: Ocean Networks Canada, SALA Lab (UBC)

Manar AmayriView profile

Researcher

• Machine Learning
• Data Science
• Explainable AI
• +5 more

Dr. Manar Amayri is a researcher affiliated with the CIISE (College/Institution name not explicitly provided), focusing on machine learning, data science, and their applications in energy management systems. Her work emphasizes smart buildings, smart cities, and human-in-the-loop AI systems. She explores advanced clustering techniques, domain adaptation, and anomaly detection in smart environments. Research Interests: Machine Learning, Explainable AI, Energy Management Systems, Smart Buildings, Data Clustering, Domain Adaptation, and Anomaly Detection. Her studies address challenges in load forecasting, occupancy estimation, and energy disaggregation using deep learning, Bayesian methods, and probabilistic models. Publications highlight innovations in hybrid models (e.g., Transformer-BiLSTM networks), data imputation for electric vehicle charging, and unsupervised learning techniques for smart grid applications. Recent work emphasizes interpretability and robustness in AI systems. No scientific awards or grants were explicitly mentioned in the provided texts. No student advisees or lab affiliations were listed.

Serguei A. MokhovView profile

Adjunct Associate Professor

• Computer Science
• Software Engineering
• Cybersecurity
• +9 more

Serguei A. Mokhov is an Affiliate Associate Professor in the Department of Computer Science and Software Engineering at Concordia University’s Gina Cody School of Engineering. He also holds the position of Affiliate Assistant Professor at the Concordia Institute for Information Systems Engineering. Additionally, he serves as the Manager for Network, Security & HPC within GCS ENCS and AITS under the Gina Cody School. His research focuses on cybersecurity, high-performance computing (HPC), digital forensics, real-time systems, and distributed computing frameworks like OpenISS and GIPSY. He is actively involved in developing tools for forensic analysis (e.g., Forensic Lucid) and real-time interactive systems for performing arts and multimedia applications. His work bridges computer science with creative technologies, emphasizing interdisciplinary collaboration. Notable projects include the OpenISS framework for real-time broadcast services and the GIPSY system for intensional programming. He has also contributed to machine learning applications in recommendation systems and cybersecurity. His teaching and research support roles involve integrating containerization (e.g., Kubernetes) and HPC into academic and industrial contexts. Recent publications highlight advancements in forensic computing, real-time gesture recognition, and knowledge graph-enhanced recommendation systems. His research often addresses challenges in scalability, interoperability, and real-time performance across diverse domains from digital forensics to interactive art installations.