جست‌وجوی استاد و پژوهشگر

• Medical Imaging Informatics
• Artificial Intelligence in Medicine
• Machine Learning
• +۵ مورد دیگر

Dr. Yunyan Zhang is a Research Associate Professor at the University of Calgary's Cumming School of Medicine with dual appointments in the Department of Radiology and Department of Clinical Neurosciences. She holds additional affiliations as Full Member of the Hotchkiss Brain Institute and Child Health & Wellness Researcher at Alberta Children's Hospital Research Institute. Dr. Zhang obtained her MD in radiology in China and PhD in Imaging Informatics from the University of Calgary, completing postdoctoral training through research and clinical fellowships in neurology and neuroradiology at the Universities of British Columbia and Calgary. Her research program develops novel AI methodologies for advancing healthcare, with specialization in: Machine/deep learning for medical image pattern recognition Natural language processing for clinical data analysis Neuroimaging biomarker discovery for multiple sclerosis Histopathological validation of computational models Tissue integrity and repair assessment techniques The core objective is creating personalized diagnostic and treatment evaluation frameworks through computational innovation. Dr. Zhang has secured significant research funding including: Discovery Grant (NSERC) Project Grant (CIHR) Discovery Research Grant (MS Society of Canada) AICE Concepts Program (Alberta Innovates) She actively mentors students in AI/imaging research and contributes to institutional brain health and child wellness initiatives.

Felipe Gohring de Magalhaesمشاهده پروفایل

استادیار

• Integrated circuits
• Microelectronics
• Computer systems organization
• +۷ مورد دیگر

Felipe Gohring de Magalhaes is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been working since July 2018 and assumed his current position in October 2024. He holds a dual doctorate from PUC-RS (Brazil) and Polytechnique Montréal (2016), with degrees in both computer science and computer engineering. His research spans embedded system architectures, real-time systems, avionics, and cybersecurity for emerging technologies. He is affiliated with the Microelectronics and Microsystems Research Group and the Multidisciplinary Institute for Cybersecurity and Cyber Resilience, reflecting his focus on integrated circuits, microelectronics, and system security. Professor Gohring de Magalhaes has published over 40 articles in international journals and conferences, with recent work focusing on photonic integrated circuits security, optical neural networks, and post-quantum cryptography for avionic systems. His publication trend shows consistent output with increasing focus on security aspects of emerging computing technologies. He has supervised at least one PhD student to completion and teaches courses including Introduction to Programming and Operating System Kernel. His research interests align with NSERC topics in integrated circuits, microelectronics, computer systems organization, and VLSI systems.

Matthew Joseph Kusnerمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +۵ مورد دیگر

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

Michel C. Desmaraisمشاهده پروفایل

استاد

• Artificial Intelligence
• Cognitive Modeling
• Learning Environments
• +۵ مورد دیگر

Michel C. Desmarais is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been faculty since 2002. With a PhD in Psychology from Université de Montréal, his research bridges artificial intelligence, educational technology, and human-computer interaction. He holds affiliations with IVADO and LAMA-WeST research groups, and has held visiting positions at Sorbonne University, Eindhoven Technical University, and other European institutions. His research focuses on three interconnected pillars: 1) Cognitive modeling and educational data mining , developing algorithms for student knowledge assessment and adaptive learning systems; 2) AI-driven educational tools , including automated grading systems and peer instruction platforms; and 3) Recommendation systems and user modeling , particularly for personalized learning interfaces. His work consistently applies machine learning to solve practical challenges in technology-enhanced education. Analysis of his 150+ publications reveals strong trends in educational NLP (sentence similarity for short-answer grading), generative AI (LLM-generated code validation), and Bayesian modeling (Q-matrix refinement). Recent work increasingly focuses on transformer architectures and real-world educational datasets. He maintains an active supervision record, having graduated 35+ graduate students. Current PhD candidates work on NLP for educational applications (Bakhtiari, Kamdem) and AI for engineering (Wang). His teaching covers user interface design, recommender systems, and intelligent interfaces. Professional service includes editorial leadership (JEDM journal), conference co-chairing (UMAP 2017, EDM founding), and grant review panels for NSERC, MITACS, and EU programs. Industry experience includes prior roles as R&D Director at MVM Inc. and researcher at Montreal Computer Research Center.

Junxi Zhangمشاهده پروفایل

استادیار

• Statistics
• Machine learning
• Bayesian nonparametric models
• +۶ مورد دیگر

Junxi Zhang is an Assistant Professor in the Department of Mathematics and Statistics at Concordia University's Faculty of Arts and Science. He joined Concordia University in August 2024 after completing a postdoctoral fellowship at the University of Alberta, where he continues to maintain academic connections. His academic journey includes: PhD in Statistics from the University of Alberta (2023) Master in Mathematics from the University of Kansas (2017) BS in Statistics from Huazhong University of Science and Technology Dr. Zhang's research spans several interconnected areas at the forefront of statistical methodology and machine learning: Bayesian nonparametric models: Conducting asymptotic analysis of random measures, studying consistency properties, and developing dependent and hierarchical models with practical applications Fairness in machine learning: Creating frameworks for fair prediction models, developing metrics for measuring fairness, and applying these approaches to health science domains where algorithmic bias can have serious consequences Reinforcement learning: Addressing fundamental challenges in optimal control within reinforcement learning frameworks Emerging interests: Expanding into conformal inference, causal inference, and time series analysis using machine learning tools His publication record demonstrates a productive trajectory with research appearing in top venues including NeurIPS and Bayesian Analysis. His work bridges theoretical statistical foundations with practical applications, particularly in healthcare and social domains where fairness considerations are increasingly critical. Dr. Zhang has received recognition through publications in prestigious venues and is actively contributing to the academic community through conference presentations and collaborative research. His work on fairness in AI addresses one of the most pressing challenges in contemporary machine learning. As an educator, Dr. Zhang teaches courses including Data Science Lab, Time Series and Forecasting, and specialized topics in statistics with focuses on Bayesian modeling and reinforcement learning. He is currently seeking graduate students for the Mathematics and Statistics MA, MSc, and PhD programs at Concordia University. His research group provides students with opportunities to engage in meaningful research that addresses both theoretical challenges in statistics and practical applications in machine learning, with particular emphasis on developing fair and trustworthy AI systems.

Samira Abbasgholizadeh-Rahimiمشاهده پروفایل

استادیار

• Artificial Intelligence in Primary Healthcare
• Digital Health Technologies
• Medical Machine Learning
• +۵ مورد دیگر

Dr. Samira Abbasgholizadeh-Rahimi serves as Assistant Professor at McGill University in the Faculty of Medicine and Director of Artificial Intelligence in Family Medicine (AIFM) . She holds the Canada Research Chair in Advanced Digital Primary Health Care and maintains affiliations with Mila – Quebec AI Institute and Lady Davis Institute for Medical Research . B.Eng., Ph.D. from McGill University President-Elect, Canadian Operational Research Society Her interdisciplinary research bridges Artificial Intelligence and Primary Healthcare , with specific focus on: AI-enabled decision support tools Explainable machine learning for clinical contexts Medical knowledge graphs and NLP applications Addressing algorithmic bias in vulnerable populations Ageism mitigation in health AI systems Digital support for neurocognitive disorders Recent publications demonstrate her expertise in: AI ethics and EDI integration (4 papers since 2024) Geriatric depression prediction models (2025 JMIR study) Shared decision-making frameworks (2022-2023 series) Conscious AI debates in clinical settings (2024 commentary) Scientific recognition includes: 2022 NAPCRG New Investigator Award Cross-institutional collaborations across Canada, Europe, and Asia Research funding sources: Fonds de recherche du Québec – Santé Natural Sciences and Engineering Research Council Brocher Foundation (Switzerland) CIHR SPOR grants Industry partnership with Roche Canada Lab activities including: Developing HOPE (Home-Based Older Adults' Depression Prediction) model Creating incorpoRATE SDM assessment framework Building evidence-based decision support tools for dementia caregivers Training next-gen AI healthcare researchers through Mila collaborations

• Patient-Reported Outcome Measures
• Implementation Science
• Psychometrics
• +۴ مورد دیگر

Dr. Erin McCabe serves as an Assistant Professor in the Department of Physical Therapy within the Faculty of Rehabilitation Medicine at the University of Alberta. Her academic appointment focuses on advancing patient-centered measurement and implementation science in rehabilitation contexts. Her educational background includes: PhD in Rehabilitation Science (2021), University of Alberta Masters of Science in Physical Therapy (2008), University of Alberta Bachelor of Science in Biological Sciences with Physical Sciences Minor (2005), University of Alberta Additional specialized training encompasses the CIHR Health System Impact Fellowship (2022), OCAP® certification (2024), and UCL Value-Based Health Care (2022). Dr. McCabe's research centers on patient-reported outcome measures (PROMs) implementation in child and adolescent mental health services, with particular emphasis on psychometric validation and equitable access to rehabilitation. Her work bridges clinical practice and health systems research, using implementation science frameworks to overcome adoption barriers in complex healthcare environments. Current projects examine organizational change requirements for PROMs integration and the impact of measurement systems on care coordination. Her publication record demonstrates consistent contributions to rehabilitation science, with recent work focusing on pediatric PROMs implementation, therapeutic relationship measurement, and health systems improvement. Key themes include learning health system development, stakeholder engagement in implementation, and equity considerations in outcome measurement. Scientific recognition includes: 2022 CIHR Health System Impact Fellowship Dr. McCabe actively contributes to academic training through courses including PTHER 526 (Professional Issues III) and REHAB 601 (Research Design in Rehabilitation Science). Her teaching integrates clinical expertise with research methodology, emphasizing evidence-based practice and professional communication skills development for future physical therapists.