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Simon VielView profile

Associate Professor

• Dark Matter Detection
• Particle Physics
• Silicon Photomultipliers
• +2 more

Simon Viel is Associate Professor of Experimental Physics at Carleton University, specializing in astroparticle physics. His research develops detection methodologies for dark matter searches using liquid argon and xenon technologies. Primary investigations focus on DEAP-3600 data analysis and next-generation detector optimization. Recent work improves event reconstruction algorithms and characterizes silicon photomultiplier performance in cryogenic environments. Current projects include ARGO and nEXO collaborations for large-scale dark matter detection. Publications demonstrate innovations in background discrimination techniques and precision isotope measurements, with growing emphasis on machine learning applications. Supervises graduate students and postdoctoral researchers in detector physics and data science applications. Leads DEAP-3600 analysis coordination and contributes to SNOLAB experimental operations.

Debasmita MukherjeeView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Human-Computer Interaction
• +3 more

Dr. Debasmita Mukherjee is an Assistant Professor at the University of New Brunswick, affiliated with the Department of Electrical and Computer Engineering and the Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship. She holds a PhD in Mechanical Engineering from the University of British Columbia (2023). Her research focuses on creating safety-centric and natural communication frameworks for human-robot collaboration within Industry 5.0 contexts. Key areas include personalizing deep learning models through affective computing, probabilistic methods, and explainable AI to enhance system safety. With eight years of experience in R&D, she has worked in both academia and industry, specializing in deploying models in industrial environments. Her work has been patented, published in prestigious IEEE conferences and journals, and covered by media. Notable research themes include domain adaptation, multimodal learning, and reinforcement learning applications in human-robot interaction. Her articles emphasize adapting communication models across environments and leveraging user feedback for personalization. Dr. Mukherjee’s contributions bridge theory and practice, addressing challenges in industrial automation through innovative AI strategies. While no specific awards are listed, her research impact is evident through patents and media recognition. Her advising and grants sections remain unreported in the provided texts. She is part of interdisciplinary teams at the Smith Centre, focusing on technology management and entrepreneurship.

Yvan LabicheView profile

Professor

• Software Engineering
• Software Testing
• UML Modeling
• +7 more

Yvan Labiche is a Professor at Carleton University's Department of Systems and Computer Engineering (SCE), part of the Faculty of Engineering and Design. He holds a Ph.D., P.Eng., and CTFL certifications. His research focuses on software verification/validation, UML-based model-driven engineering, and software quality assurance. Key areas include object-oriented systems, high-dependability systems, and applying AI/evolutionary methods to software testing. He leads research on UML consistency rules, state-based testing strategies, and automated model transformations. Recent work explores metamorphic relations in testing and cost reduction for 5G systems. He has organized workshops like WUCOR (UML Consistency Rules) and contributed to tools like aToucan for UML model derivation. His expertise spans empirical studies in traceability, test effectiveness, and integration testing methodologies.

Sreeraman RajanView profile

Professor

• Sensors and Sensor Systems
• Compressive Sensing
• Signal Processing
• +8 more

Prof. Sreeraman Rajan is a Professor and Tier II Canada Research Chair in Sensor Systems at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. He holds a Ph.D. from the University of New Brunswick and has prior experience at Defence Research and Development Canada (DRDC), Biopeak Corporation, and JDS Uniphase. His research focuses on sensors, compressive sensing, signal processing, and machine learning with applications in biomedical, defense, and security domains. Education : Bachelor’s in Electronics and Communications Engineering (Bharathiyar University, 1987) M.Sc. Electrical Engineering (Tulane University) Ph.D. Electrical and Computer Engineering (University of New Brunswick) Research Interests : Sensors and sensor systems for biomedical and defense applications Compressive sensing and signal processing techniques Machine learning and pattern classification Doppler radar-based life sign monitoring and human activity recognition Biomedical signal processing for noninvasive diagnostics Awards : Tier II Canada Research Chair (2015) IEEE MGA Achievement Award (2012) Queen Elizabeth II Diamond Jubilee Medal (2012) Outstanding Contribution Award (Defence Research and Development Canada, 2013) Grants : Tier II Canada Research Chair: $500,000 (2015-2019) Correctional Services Canada: $98,850 (2014-2015) Royal Canadian Air Force: $50,000 (2013-2015) Advising & Collaborations : Supervised over 20 graduate and undergraduate students Co-founder of multiple patents in radar and biomedical systems Active in IEEE leadership roles, including chair of IEEE Ottawa Section Labs & Teams : Conducts research in Carleton’s Systems and Computer Engineering labs, focusing on sensor systems, radar technology, and biomedical signal processing. Collaborates with DRDC, industry partners, and international researchers on defense and healthcare projects.

• Econometrics
• Statistics
• Machine Learning
• +3 more

Luke Russell is a Contract Instructor at Carleton University's Faculty of Engineering and Design. He holds a Ph.D. from Carleton University. His research focuses on econometric theory, statistical methods for causal inference, and machine learning applications in policy analysis. Key areas include counterfactual analysis, partial identification in econometric models, and robust optimization techniques. His publications span topics like dynamic panel models, specification tests for moment inequalities, and Wasserstein-robust counterfactuals. He contributes to methodological advancements in handling endogeneity, nonseparable models, and optimal policy learning. No awards or grants are explicitly mentioned in the provided information. Russell’s work bridges econometrics and machine learning, addressing challenges in treatment effect estimation and policy evaluation. While specific lab affiliations are unlisted, his role within the Faculty of Engineering suggests involvement in interdisciplinary research initiatives.

Maggie BrownView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Healthcare Analytics
• +3 more

Maggie Brown is an Assistant Professor in the Department of Mathematics and Statistics at the University of New Brunswick. She holds a PhD and specializes in biostatistics and epidemiology, with a focus on maternal and child health outcomes. Her research employs statistical methodologies to analyze healthcare utilization patterns and clinical risks in populations, particularly in obstetrics, pediatrics, and chronic disease studies. Education: PhD in Mathematics and Statistics (institution unspecified). Research Interests: Biostatistics, Maternal and Child Health, Healthcare Analytics, Epidemiology, Clinical Research, and Public Health. Her work emphasizes retrospective cohort studies and predictive modeling in healthcare settings. Her recent publications explore topics such as cesarean delivery effects on offspring health, intergenerational health impacts of maternal conditions, and complications in pediatric populations. These studies highlight her expertise in statistical analysis of clinical and population health data. Awards: None explicitly listed in the provided text. Grants and advising roles: No specific information available. Labs/Teams: No lab affiliations or teams mentioned in the current data.

Scott PaveyView profile

Associate Professor

• Aquatic Molecular Ecology
• Ecological Genomics
• Conservation Biology
• +3 more

Scott Pavey is an Associate Professor and Canada Research Chair (CRC) in Biological Sciences at the University of New Brunswick Saint John. His work focuses on Aquatic Molecular Ecology and Ecological Genomics, leveraging genomic tools to address conservation, fisheries management, and evolutionary questions. He leads the CRI Genomics lab, equipped with advanced technologies for DNA sequencing and bioinformatics. Education: BSc in Wildlife Biology (Colorado State University), MSc in Biological Sciences (University of Alaska Anchorage), PhD in Biological Sciences (Simon Fraser University), and a Postdoctoral Fellowship at Université Laval. Research Interests: Environmental DNA (eDNA) applications, population genetics of fish species, local adaptation physiology, and ecological roles of genetic elements. Key projects include eDNA-based species monitoring, genetic stock identification of Striped Bass, and genomic responses to environmental stress. Scientific Contributions: Over 50 publications in peer-reviewed journals, with a focus on salmonids, cod, and other aquatic species. Awards include the Canada Research Chair position. Advising: Supervises a dynamic team of graduate students and postdocs, including current PhD candidates (e.g., Shawn Kroetsch, Jagger Watters-Gray) and MSc students (e.g., Jake Reicker, Laken Devost). Alumni include notable researchers like Faith Penny (PhD in striped bass physiology) and Melissa Morrison (eDNA in salmon abundance). Labs and Collaborations: CRI Genomics lab at the Canadian Rivers Institute, collaborating with Fisheries and Oceans Canada (DFO) and the Mactaquac Aquatic Ecosystem Study (MAES) on applied projects.

Isik BicerView profile

Associate Professor

• Business Analytics
• Demand Fulfilment
• Operational Performance
• +2 more

Isik Bicer is an Associate Professor of Operations Management and Information Systems at the Schulich School of Business, York University. His research focuses on analyzing how operational factors impact financial parameters and designing strategies for customer fulfillment using methods from corporate finance, quantitative finance, and optimization theory. His primary research areas include business analytics, demand fulfillment optimization, operational performance analysis, and supply chain management under uncertainty. He has developed decision tools for supply chain valuation and lead time optimization. Bicer's publications focus on supply chain finance, demand volatility modeling, inventory optimization, and disruption risk mitigation. His work integrates operational strategies with financial outcomes to enhance supply chain resilience and efficiency. He maintains research collaborations with institutions including Rotterdam School of Management and the Swiss Federal Institute of Technology (EPFL).

Brian EamesView profile

Professor

• Skeletal development
• Molecular genetics
• Synchrotron imaging
• +6 more

Dr. Brian Eames is a Professor in the Department of Anatomy, Physiology and Pharmacology at the University of Saskatchewan's College of Medicine. His research focuses on skeletal development and evolution, molecular genetics, synchrotron imaging, and 3D bioprinted tissue engineering. He leads an interdisciplinary lab of evolutionary and developmental biologists and tissue engineers. University of Saskatchewan, College of Medicine, Department of Anatomy, Physiology and Pharmacology Research Interests: Skeletal cell differentiation mechanisms External signals directing bone/cartilage cell fate Pathway activation in skeletal development Application to osteoarthritis therapies Evolutionary variation in skeletal genetics Comparative transcriptomics across chordates Publication Trends (2016-2025): His work spans developmental biology, evolutionary genetics, biomedical engineering, and imaging technology. Key themes include proteoglycan signaling, 3D bioprinting for cartilage, comparative skeletal evolution in cartilaginous fishes, and gene network analysis using bioinformatics tools. Scientific Awards: CIHR New Investigator Salary Award Faculty of 1000 'Must Read' recognition Cover image in Tissue Engineering Part C Methods ACS Editors’ Choice feature Laboratory Overview: The Eames Lab integrates molecular genetics, advanced imaging, and tissue engineering to explore skeletal development and evolution. They employ zebrafish and pig models, synchrotron radiation imaging, and computational approaches to analyze gene co-expression networks.

Emanuele SacchiView profile

Associate Professor

• Transportation Safety
• Statistical Modeling
• Highway Design
• +2 more

Dr. Emanuele Sacchi is an Associate Professor in Civil, Geological and Environmental Engineering at the University of Saskatchewan's College of Engineering. His research focuses on transportation safety analytics, highway design optimization, and statistical methods for traffic analysis. Key research areas include surrogate safety measures evaluation, geometric design principles, speed management strategies, and advanced statistical modeling of collision data. Recent work employs machine learning for safety perception analysis and develops predictive tools for infrastructure safety management. He holds Ph.D., M.Sc., and B.Sc. degrees in Environment and Territory/Transportation Engineering from Politecnico di Torino, Italy.

Steven X. WangView profile

Professor

• AI/Machine Learning
• Healthcare Applications
• Biomedical Signal Processing
• +3 more

Dr. Steven X. Wang is a full professor in the Department of Statistics at York University and currently serves as the Director of the undergraduate Data Science program. He holds a U.S. patent for Machine Learning and has been recognized with prestigious awards including the Queen Elizabeth II Platinum Jubilee Award (2023) and the Markham-Unionville Achievement Award and Medal (2025) for his contributions to AI in healthcare. Education: Postdoc in Data Science, Pacific Institute of Mathematical Sciences (2002) Ph.D. in Statistics, University of British Columbia (2001) M.Sc. in Applied Statistics, University of California, Riverside B.Sc. in Computational Mathematics, Beijing University of Technology Research Interests: Dr. Wang focuses on applying AI/machine learning to healthcare, particularly in pain analysis using EEG signals and financial time series modeling. His work bridges statistical theory with practical applications in biomedical engineering and medical data analysis. Publications: His recent articles emphasize advancements in neural networks for EEG analysis, financial portfolio strategies, and biomedical signal processing. His work consistently integrates interdisciplinary methods to solve real-world health and financial challenges. Awards: Queen Elizabeth II Platinum Jubilee Award (2023) Markham-Unionville Achievement Award and Medal (2025) U.S. Patent for Machine Learning Innovations Experience: With 30 years of consulting experience and 25 years in scientific computing, Dr. Wang leads research projects addressing critical challenges in healthcare and data-driven decision-making.

Zachary PattersonView profile

Professor

• Transportation Engineering
• Data Science
• Machine Learning
• +5 more

Zachary Patterson is a Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on emerging technologies in transport-related data collection, computational geospatial data processing, and the application of statistical and artificial intelligence techniques to transportation and environmental challenges. He specializes in discrete choice experiments, greenhouse gas emission prediction, and smart mobility solutions. Dr. Patterson's work integrates machine learning, graph neural networks, and geospatial analysis to address complex issues such as public transit optimization, passenger flow prediction, and environmental impact assessment. He has developed frameworks for privacy-preserving data collection and innovative solutions for on-demand transit systems. His interdisciplinary approach bridges computer science, transportation engineering, and environmental science. Notable contributions include the TMS-GNN model for traffic-aware passenger flow prediction, HydroVision for hydrometric forecasting, and frameworks for electric vehicle adoption analysis. His research often emphasizes interpretability of black-box machine learning models and the ethical use of data in urban systems. Dr. Patterson has authored over 50 publications, with a focus on transportation data analytics, sustainability, and smart cities. His work has been applied in real-world contexts such as Montreal’s transportation infrastructure and environmental policy design.

Adam KrzyzakView profile

Professor

• Computer Vision
• Image Processing
• Neural Networks
• +5 more

Adam Krzyzak is a Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on computer vision, image processing, neural networks, and pattern recognition. He has contributed extensively to the development of algorithms for tasks such as palmprint classification, face recognition, hyperspectral imagery analysis, and noise reduction in images. His work bridges theoretical foundations of machine learning with practical applications in biometric systems, medical imaging, and autonomous navigation systems. Recent studies emphasize the convergence properties of deep neural networks and the robustness of pattern recognition techniques under noisy conditions. His research spans over three decades, with a strong emphasis on combining statistical methods with deep learning architectures. Key advancements include novel approaches to feature extraction, filter-based classification, and the application of wavelet transforms in 3D data denoising. Krzyzak's contributions to conferences like the IAPR TC3 Workshop highlight his role in advancing neural network methodologies for real-world pattern recognition challenges.

Angela KrossView profile

Assistant Professor

• Remote Sensing
• Geographic Information Systems
• Ecosystem structure, function and services
• +4 more

Angela Kross is an Assistant Professor in the Department of Geography, Planning and Environment at Concordia University. She holds a PhD in Physical Geography/Remote Sensing from McGill University and an M.Sc. in Geographic Information Science/Remote Sensing from Wageningen University, Netherlands. Prior to joining Concordia in 2015, she was a postdoctoral researcher at Agriculture and Agri-Food Canada. Her research focuses on using geospatial technologies (GIS, remote sensing) combined with ground measurements and models to study ecosystem processes, vegetation dynamics, and land use change in response to anthropogenic and natural factors such as agriculture, mining, and climate change. Her work spans diverse environments, including peatlands, tropical forests, and urban areas. Key themes include habitat suitability modeling, biodiversity assessment, climate change impacts, and sustainable land management. She has contributed to studies in regions like the Congo Basin, Suriname, and the Adirondack-to-Laurentians wildlife corridor. Her methodologies often integrate machine learning and satellite data to address complex environmental challenges. Dr. Kross has published extensively on topics ranging from crop yield prediction using neural networks to assessing mining impacts in the Amazon. She maintains an active research profile with a focus on transdisciplinary solutions for environmental sustainability.

Frédéric GodinView profile

Associate Professor

• Quantitative Finance
• Risk Management
• Actuarial Science
• +3 more

Frédéric Godin is an Associate Professor in the Department of Mathematics and Statistics at Concordia University, where he supervises graduate students in Mathematics and Statistics (MA, MSc, PhD) and actively accepts research inquiries. He earned his Ph.D. from HEC Montréal in 2014. Research Focus His work integrates advanced computational methods with financial and actuarial theory, spanning: Quantitative Finance : Option pricing, volatility modeling, and hedging strategies using machine learning Risk Management : Catastrophic risk assessment, portfolio optimization, and derivatives pricing Actuarial Science : Life insurance contract valuation and embedded guarantees Energy Markets : Electricity price forecasting and commodity derivatives Recent publications (2023-2025) demonstrate a strong emphasis on reinforcement learning applications in finance, particularly deep hedging frameworks and volatility surface modeling. Additional themes include climate-aware portfolio design and statistical methods for insurance risk. Academic Contributions As a thesis supervisor, he guides graduate research in mathematical finance and statistical modeling. No awards or grants were documented in available sources.