Portrait of Narges Armanfard

Narges Armanfard

Associate Academic Member
Assistant Professor, McGill University, Department of Electrical and Computer Engineering


Narges Armanfard (PhD, PEng) is the founder of McGill University’s iSMART Lab and its principal investigator. She is a tenure-track assistant professor in the Department of Electrical and Computer Engineering at McGill and an associate academic member at Mila – Quebec Artificial Intelligence Institute.

Armanfard is also affiliated with McGill’s Centre for Intelligent Machines (CIM), the McGill initiative in Computational Medicine (MiCM), and the McGill Institute for Aerospace Engineering (MIAE).

Her research focuses on developing innovative algorithms for various domains, such as time-series data analysis, computer vision, reinforcement learning and representation learning for tasks like data clustering, classification and anomaly detection.

Her contributions to the field of AI have been recognized with numerous awards from a variety of institutions, including the Natural Sciences and Engineering Research Council of Canada, AgeWell, Vanier-Banting and the Fonds de recherche du Québec, as well as McMaster University, McGill University, the University of Toronto, the Canadian Institutes of Health Research and Scale AI.

Current Students

Master's Research - McGill University
Master's Research - McGill University
Master's Research - McGill University
Master's Research - McGill University
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AI healthcare research: Pioneering iSMART Lab
Dr Narges Armanfard, Professor, talks us through the AI healthcare research at McGill University which is spearheading a groundbreaking init… (see more)iative – the iSMART Lab. Access to high-quality healthcare is not just a fundamental human right; it is the bedrock of our societal wellbeing, with the crucial roles played by doctors, nurses, and hospitals. Yet, healthcare systems globally face mounting challenges, particularly from aging populations. Dr Narges Armanfard, affiliated with McGill University and Mila Quebec AI Institute in Montreal, Canada, has spearheaded a groundbreaking initiative – the iSMART Lab. This laboratory represents a revolutionary leap into the future of healthcare, with its pioneering research in AI for health applications garnering significant attention. Renowned for its innovative integration of AI across diverse domains, iSMART Lab stands at the forefront of harnessing Artificial Intelligence to elevate and streamline health services.
Self-supervised anomaly detection in computer vision and beyond: A survey and outlook.
Hadi Hojjati
Thi Kieu Khanh Ho
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Ali Karami
Thi Kieu Khanh Ho
Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties
Sushant Sinha
Denzel Guye
Xiaoping Ma
Kashif Rehman
S. Yue
Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook.
Bahareh Nikpour
Dimitrios Sinodinos
Human activity recognition (HAR) is a popular research field in computer vision that has already been widely studied. However, it is still a… (see more)n active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep reinforcement learning (DRL) has recently been used to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based HAR has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this area, we have constructed a comprehensive survey on activity recognition methods that incorporate DRL. Throughout the article, we classify these methods according to their shared objectives and delve into how they are ingeniously framed within the DRL framework. As we navigate through the survey, we conclude by shedding light on the prominent challenges and lingering questions that await the attention of future researchers, paving the way for further advancements and breakthroughs in this exciting domain.
EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions
Dimitrios Sinodinos
Open-Set Multivariate Time-Series Anomaly Detection
Thomas Lai
Thi Kieu Khanh Ho
Explainable Attention for Few-shot Learning and Beyond
Bahareh Nikpour
Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review
Lily Puterman-Salzman
Jory Katz
Howard Bergman
Roland Grad
Vladimir Khanassov
Genevieve Gore
Isabelle Vedel
Machelle Wilchesky
Negar Ghourchian
Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (see more)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
Multivariate Time-Series Anomaly Detection with Contaminated Data: Application to Physiological Signals
Thi Kieu Khanh Ho
Spatial Hard Attention Modeling via Deep Reinforcement Learning for Skeleton-Based Human Activity Recognition
Bahareh Nikpour
Deep learning-based algorithms have been very successful in skeleton-based human activity recognition. Skeleton data contains 2-D or 3-D coo… (see more)rdinates of human body joints. The main focus of most of the existing skeleton-based activity recognition methods is on designing new deep architectures to learn discriminative features, where all body joints are considered equally important in recognition. However, the importance of joints varies as an activity proceeds within a video and across different activities. In this work, we hypothesize that selecting relevant joints, prior to recognition, can enhance performance of the existing deep learning-based recognition models. We propose a spatial hard attention finding method that aims to remove the uninformative and/or misleading joints at each frame. We formulate the joint selection problem as a Markov decision process and employ deep reinforcement learning to train the proposed spatial-attention-aware agent. No extra labels are needed for the agent’s training. The agent takes a sequence of features extracted from skeleton video as input and outputs a sequence of probabilities for joints. The proposed method can be considered as a general framework that can be integrated with the existing skeleton-based activity recognition methods for performance improvement purposes. We obtain very competitive activity recognition results on three commonly used human activity recognition datasets.
Spatio-temporal hard attention learning for skeleton-based activity recognition
Bahareh Nikpour