Find Academic Supervisors

Mark MeagherView profile

Assistant Professor

• Architectural Design
• Building Information Modeling
• Responsive Architecture
• +4 more

Mark Meagher is Assistant Professor in the Environmental Design Program within the Faculty of Architecture at University of Manitoba. With degrees from Harvard Graduate School of Design and EPFL, his research integrates building data, open-source tools, and software development in architectural design. Previously at University of Sheffield, he co-led the MA in Architectural Design program and conducted research at EPFL's Media and Design Lab. Research Focus: Investigates Building Information Modeling (BIM) as both technical framework and participatory tool for community engagement. Explores responsive architecture, digital design pedagogy, and climate-responsive technologies. Current projects examine how spatial data systems can democratize design processes while addressing environmental challenges. Teaching: Co-developed the Environmental Technology course addressing energy systems and climate change. Emphasizes open-source tools and evidence-based design methodologies that bridge technical analysis with creative practice. Publications: Recent works explore computational design processes, cultural heritage documentation, responsive material systems, and participatory platforms. His scholarship demonstrates consistent focus on the intersection of digital technologies, environmental sustainability, and social equity in the built environment.

Cheng LiView profile

Professor

• Wireless Communications
• Underwater Acoustic Networks
• Wireless Sensor Networks
• +6 more

Cheng Li is a Full Professor and Department Head of Electrical and Computer Engineering at Memorial University of Newfoundland. He holds adjunct professorships at Harbin Institute of Technology and Xi'an University of Post and Telecommunications. His expertise spans wireless communications, underwater networks, and sensor systems. He has held academic roles since 1999, including leadership in Nokia Telecommunications and Baosteel. Education: B.Eng. (Hons.) and M.Eng. from Harbin Institute of Technology, followed by a PhD at Memorial University. His research focuses on energy-efficient wireless networks, underwater localization, and cooperative communication protocols. Research highlights include innovations in underwater acoustic sensor networks, vehicular ad-hoc networks, and integrated satellite-terrestrial systems. He has received 8 notable awards, including the Best Paper Award at IEEE ICC’2010 and the David Dunsiger Award. His work bridges academic and industrial applications, with contributions to standards like NECEC and IEEE conferences. He advises on network security, IoT, and multi-agent systems, with over 30 peer-reviewed publications since 2001.

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.

Junfeng WenView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Junfeng Wen is an Assistant Professor at the School of Computer Science, Carleton University. He holds a Ph.D. in Statistical Machine Learning from the University of Alberta, where he worked with Prof. Dale Schuurmans and Prof. Russ Greiner. His research focuses on foundational machine learning algorithms, including reinforcement learning, transfer learning, federated learning, and domain adaptation. He teaches courses such as Reinforcement Learning and Machine Learning at Carleton University. Education: Ph.D. in Statistical Machine Learning (2013–2020), University of Alberta M.Sc. in Computing Science (2011–2013), University of Alberta Bachelor of Engineering (with Honors, 2007–2011), College of Computer Science and Technology, CKC College, Zhejiang University Research Interests: Dr. Wen’s work emphasizes advancing theoretical understanding and practical applications of machine learning. Key areas include federated learning frameworks for privacy preservation, reinforcement learning for decision-making systems, and multi-source domain adaptation techniques. His research bridges algorithmic innovation with real-world challenges in distributed systems and biological modeling. Publications: His recent articles explore federated learning optimization, decentralized collaboration strategies, and reinforcement learning applications in behavioral studies. Notable contributions include domain aggregation networks for cross-domain adaptation and proxy model-sharing techniques in decentralized learning. The work spans theoretical advancements and practical implementations, often addressing scalability and privacy concerns in distributed environments. Professional Contributions: Conference reviewer for NeurIPS, ICML, ICLR, AAAI, IJCAI, and ACML Journal reviewer for JMLR, Machine Learning Journal (Editorial Board member), and TPAMI Labs and Projects: He contributes to open-source projects like Domain Aggregation Networks (DARN) for multi-source domain adaptation and Frank-Wolfe for Covariate Shift . These repositories reflect his commitment to reproducible research and community engagement.

Chris JoslinView profile

Professor

• Computer Graphics
• Medical Imaging
• Virtual Reality
• +5 more

Chris Joslin is a Professor at the School of Computer Science, Carleton University. His office is located in 4302 Canal Building, and he can be reached at Chris.Joslin@carleton.ca. He specializes in interdisciplinary research areas including computer graphics, medical imaging, virtual reality, computer vision, and human-computer interaction. His work bridges theoretical advancements with practical applications in animation, 3D modeling, and medical visualization. Research interests include developing novel techniques for 3D editing, medical image processing, and immersive virtual environments. Notable contributions include advancements in 3D Gaussian splatting, AI-driven MRI analysis, and robust sensor fusion for autonomous systems. His publications span from foundational studies on motion retargeting to applied work in procedural audio generation for soft-body simulations. Recent trends in his articles emphasize integration of deep learning with traditional computer vision tasks, optimization of medical imaging workflows, and enhancing accessibility in virtual reality systems. Despite prolific output, no scientific awards are explicitly mentioned in the provided texts. Advising and grant details remain unspecified, though his involvement in collaborative projects like VPARK and ISIS suggests engagement with interdisciplinary teams. His work is anchored at Carleton’s Herzberg Laboratories, a hub for advanced computational research.

Omair ShafiqView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +4 more

Omair Shafiq is an Associate Professor at the School of Computer Science, Carleton University. His research focuses on advanced topics in natural language processing (NLP), machine learning, and their applications in knowledge graphs, blockchain, and network security. He leads projects that integrate large language models (LLMs) with structured data to enhance information retrieval and decision-making systems. His work spans areas like explainable AI, adversarial robustness, and real-time traffic prediction. Shafiq is affiliated with Herzberg Laboratories and contributes to interdisciplinary research, including smart contracts, encrypted traffic classification, and IoT security. Education details are not explicitly listed in the provided text. His research interests emphasize practical solutions for challenges in AI ethics, data privacy (e.g., GDPR compliance), and scalable analytics for big data applications. He has developed frameworks like ECSGen/iZen for NLP tasks and CARD-B for encrypted traffic classification. His recent publications (2022–2025) highlight innovations in ensemble learning, adversarial attacks defense, and blockchain-based solutions for secure transactions. Shafiq’s work often bridges theory and practice, addressing real-world problems such as cybersecurity in vehicular networks, stock market prediction using deep learning, and user behavior analysis on social platforms. His contributions include tools like RevDet for event detection in news feeds and HybLoc for indoor localization. Though no specific awards are mentioned, his prolific publication record reflects sustained academic impact.

• Intelligent Human Machine Interaction
• Ubiquitous Computing in Healthcare
• Human Behavior Recognition
• +3 more

Dr. Xianta Jiang is an Associate Professor in the Department of Computer Science at Memorial University of Newfoundland, Canada. His research focuses on intelligent human-machine interaction, ubiquitous healthcare computing, and bio-signal processing. He leads the UCML (Ubiquitous Computing and Machine Learning) Lab, advancing technologies for human activity sensing, prosthetic control, and surgical training systems. Research Interests: Intelligent Human-Machine Interfaces Medical Applications of Wearable Sensors Eye Tracking & Pupil Dynamics Biomedical Signal Processing Robotics & Grasp Recognition Deep Learning for Healthcare Key Contributions: Developed novel bio-signal processing methods for prosthetic control Advanced eye-tracking techniques for surgical expertise assessment Pioneered multimodal sensor fusion approaches in robotics Labs/Teams: Director of the UCML Lab, collaborating with industry partners in medical robotics and wearable tech. Active in surgical training simulation and rehabilitation engineering projects.

Terrence TriccoView profile

Assistant Professor

• Synthetic Data Generation
• Smoothed Particle Hydrodynamics (SPH)
• High-Performance Computing
• +6 more

Terrence Tricco is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland, cross-appointed with the Department of Physics and Physical Oceanography. He chairs the Master's of Data Science program and leads the Simulation, Modeling and Synthetic Data Lab. His research focuses on synthetic data generation using deep learning, astrophysical smoothed particle hydrodynamics (SPH), and high-performance computing. Tricco is a development lead for the Phantom SPH code and creator of the Sarracen Python analysis package. He is Principal Investigator of the Centre for Analytics, Informatics and Research (CAIR), managing a high-performance computing cluster. Collaborations include Verafin (financial synthetic data) and VBHC-NL (healthcare analytics). Key research areas include multi-physics SPH algorithms, parallel computing optimization, and numerical analysis of SPH convergence. Tricco's work bridges computational science with applications in astrophysics, healthcare, and finance. Notable software contributions include Phantom (astrophysical SPH simulations), Sarracen (SPH visualization), and Framalytics (FRAM modeling integration). His lab emphasizes synthetic data generation for sensitive domains like healthcare and finance. Recent work explores generative AI for data synthesis and improving SPH method scalability on modern architectures.

Alison MalcolmView profile

Professor

• Seismic Imaging
• Full-Waveform Inversion
• Uncertainty Quantification
• +3 more

Alison Malcolm is a Professor in the Earth Sciences Department at Memorial University of Newfoundland, specializing in seismic imaging and geophysical methods. Her research focuses on uncertainty quantification, nonlinear imaging techniques, and full-waveform inversion (FWI). She leads a vibrant research group advising PhD, Master’s, and undergraduate students in topics such as subsurface imaging, rock physics, and CO2 sequestration monitoring. She holds a Ph.D. from Colorado School of Mines and a B.Sc. from the University of British Columbia. Affiliations: Memorial University of Newfoundland (MUN) Education: Ph.D. in Geophysics (2005, Colorado School of Mines), B.Sc. in Geophysics (University of British Columbia) Her research interests span uncertainty quantification in FWI, nonlinear elastic wave interactions, and subsurface monitoring. Current projects include experimental studies on crack effects in nonlinear elasticity (collaboration with Xuan Feng and James TenCate) and Bayesian inversion methods for CO2 reservoirs. She teaches advanced courses like Wave Equation Modeling, Processing and Inversion Algorithms and Seismic and Potential Fields Methods . Her lab conducts experiments on rock samples to understand nonlinear wave coupling, with applications in reservoir characterization and fault mechanics. Collaborations include MIT, ETH Zurich, and industry partners like ExxonMobil and Chevron. Alumni from her group hold positions in academia and industry worldwide. Key research themes include: Quantifying subsurface uncertainties using probabilistic methods Developing FWI algorithms for targeted imaging Exploring nonlinear phenomena for subsurface property estimation Her work integrates computational methods with laboratory experiments and field data, addressing challenges in energy exploration and environmental monitoring.

Howard M. SchwartzView profile

Professor

• Adaptive and Learning Systems
• Multi-Robot Learning and Adaptation
• Cooperative Control of Autonomous Robots
• +4 more

Howard M. Schwartz is a Professor at the Department of Systems and Computer Engineering, Carleton University, within the Faculty of Engineering and Design. He holds a Ph.D. from MIT and specializes in adaptive control systems, multi-robot learning, and reinforcement learning applications in autonomous systems. His work integrates machine learning with robotics, focusing on cooperative control, swarm intelligence, and real-time systems. Education: Ph.D. in Engineering, Massachusetts Institute of Technology (MIT) Research Interests: Adaptive and Learning Systems Multi-Robot Learning and Cooperation Reinforcement Learning for Autonomous Vehicles Control of Robot Manipulators Cooperative Multi-Agent Systems Advising & Research: Supervised over 40 graduate students, with notable projects in drone navigation, swarm robotics, and game-theoretic control. Collaborates with Ericsson on 5G-enabled autonomous systems in the Ericsson Carleton Partnership Program. Labs & Facilities: Ericsson 5G Autonomous Control Laboratory for multi-vehicle research. Swarm robotics experimentation facilities focusing on decentralized control and adaptive algorithms.

Chao ShenView profile

Assistant Professor

• Robotics Control
• Autonomous Underwater Vehicles
• Model Predictive Control (MPC)
• +5 more

Dr. Chao Shen is an Assistant Professor at the Department of Systems and Computer Engineering, Carleton University, within the Faculty of Engineering and Design. His research focuses on control theory, machine learning, and optimization applied to robotics systems, autonomous underwater vehicles (AUVs), and intelligent control of mechatronic systems. He leads the Robotics Control and Optimization Laboratory and has supervised multiple capstone projects, including a winning team in the 2022 departmental competition. Dr. Shen holds a PhD from the University of Victoria. He has been appointed as a Technical Editor for IEEE/ASME Transactions on Mechatronics (2023) and Associate Editor for IEEE Canadian Journal of Electrical and Computer Engineering (2022). He also served as Technical Program Chair for the 2022 IEEE Electrical Power and Energy Conference. His research interests include perception and navigation for robotics, cooperative control of multi-agent systems, and distributed model predictive control (MPC) for large-scale systems. Notable contributions include a recently published Springer book on 'Advanced Model Predictive Control for Autonomous Marine Vehicles' (2023) and award-winning work funded by NSERC Discovery Grants. Dr. Shen actively mentors graduate and undergraduate students in topics like robust MPC for visual servoing, UAV coordination, and energy-optimal control strategies. His lab offers funded PhD and MASc positions focusing on control theory applications in robotics and autonomous systems.

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.

Alan DavoustView profile

Professor

• Distributed Systems
• Multi-Agent Systems
• Social Machines
• +4 more

Alan Davoust is a Professor at the University of Quebec in Outaouais (UQO) since December 2018. He holds a Ph.D. from Carleton University (2015), specializing in Decentralized Social Data Sharing, and has held postdoctoral positions at the University of Edinburgh (2016–2018) and Carleton University (2016). His academic focus spans distributed systems, multi-agent systems, and social machines, with a strong emphasis on ethical interactions and decentralized collaboration. Education: Ph.D., Carleton University, 2015 M.A.Sc., Carleton University, 2009 Engineer Degree (ENSIMAG), Grenoble INP, 2005 Research interests include distributed data management, game theory applications, and the design of social machines. His work explores mechanisms to enhance collaborative systems through decentralized architectures and ethical considerations. Notable projects include the Sociogrammes initiative, which aims to democratize social machine creation, and studies on social contracts to improve non-cooperative game outcomes. Teaching responsibilities include introductory programming courses in Java and courses on distributed artificial intelligence, focusing on multi-agent systems and social choice theory. He actively collaborates with researchers globally and mentors students in these areas.

Paulo GarciaView profile

Assistant Professor

• Embedded Real-Time Systems
• Hardware/Software Co-Design
• FPGA Acceleration
• +3 more

Paulo Garcia is an Assistant Professor in the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada. He holds a Ph.D. in Computer Engineering from the University of Minho, Portugal, with research periods at Asian Institute of Technology and University of Würzburg. His academic roles include serving as a faculty member, thesis chair/examiner, and committee member in multiple university initiatives. Research Focus: Embedded real-time systems, hardware/software co-design, FPGA acceleration, and engineering pedagogy. Specific technical areas include multicore architectures, runtime systems, and hardware accelerators for embedded applications. His work emphasizes synergies between processor architectures, compilers, and FPGA-based solutions. Teaching: Courses include SYSC 3310 (Real-Time Systems), SYSC 4310 (Computer Architecture), and SYSC 5807 (Hardware/Software Co-Design). He supervises senior projects like robotic systems and eHealth wearable devices. Awards & Grants: Includes 2020 awards from General Dynamics Mission Systems and Carleton’s Rapid Response Grant, plus multiple scholarships from FCT Portugal and EU programs. His research has been supported by industry partnerships and defense collaborations. Service: Active in academic service as a thesis evaluator, member of student mental health committees, and representative in university governance bodies. Engaged in curriculum development and embedded systems program revisions.

Betty LiView profile

Adjunct Professor

• Biomaterials
• 3D Bioprinting
• Microphysiological Systems
• +7 more

Dr. Betty Li is an Adjunct Professor in the Department of Systems and Computer Engineering at Carleton University and a researcher at the Human Health Therapeutics Research Center. She holds a Ph.D. in Biomedical Engineering from the University of Western Ontario (2018), with prior degrees from Western Ontario (Master of Engineering Science, 2013) and the University of Toronto (Bachelor of Applied Science in Nanoengineering, 2011). Her research focuses on developing microphysiological systems, such as organ-on-chip models, using stem cells, 3D bioprinting, and biomaterials to advance preclinical drug discovery. Key research areas include biomaterials, biomechanics, and microfluidic design. Her recent publications (2023–2025) emphasize 6G networks, non-terrestrial networks (NTN), and AI-driven solutions for wireless communication challenges. She explores topics like federated learning in NTN, hemispherical antenna arrays for HAPS, and energy-efficient satellite IoT systems. Her work bridges biomedical engineering and telecommunications, addressing challenges in both fields. No scientific awards are explicitly mentioned in the text. She advises no listed students but collaborates on projects involving robotic aerial base stations and spectral efficiency optimization. Her lab work integrates interdisciplinary approaches for in vitro tissue modeling and sustainable network architectures.