Portrait of Narges Armanfard

Narges Armanfard

Associate Academic Member
narges.armanfard@mila.quebec
Associate Professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
Active Learning
AI in Health
Anomaly Detection
Applied AI
Computer Vision
Deep Learning
Dimensionality Reduction Methods
Generative Models
Graph Neural Networks
Medical Machine Learning
Multimodal Learning
Online Learning
Reinforcement Learning
Representation Learning
Website
Google Scholar

Biography

Narges Armanfard (PhD, PEng) is the Founder and Principal Investigator of McGill University’s iSMART Lab. She's 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

Publications

AI healthcare research: Pioneering iSMART Lab
Narges Armanfard
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.
2024-04-08
Open Access Government (published)
doi.org
Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties
Sushant Sinha
Denzel Guye
Xiaoping Ma
Kashif Rehman
Stephen Yue
Narges Armanfard
2024-02-29
Machine Learning with Applications (published)
doi.org
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
Hadi Hojjati
Narges Armanfard
Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent adv… (see more)ancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation. We propose an anomaly score which is a combination of autoencoder's reconstruction error and the distance from the center of the enclosing hypersphere in the latent representation. Minimizing this anomaly score aids us in learning the underlying distribution of the normal class during training. Including the reconstruction error in the anomaly score ensures that DASVDD does not suffer from the common hypersphere collapse issue since the DASVDD model does not converge to the trivial solution of mapping all inputs to a constant point in the latent representation. Experimental evaluations on several benchmark datasets show that the proposed method outperforms the commonly used state-of-the-art anomaly detection algorithms while maintaining robust performance across different anomaly classes.
2023-11-09
IEEE Transactions on Knowledge and Data Engineering (unknown)
doi.org
arxiv.org
Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning
Bahareh Nikpour
Narges Armanfard
Attention mechanisms have demonstrated significant potential in enhancing learning models by identifying key portions of input data, particu… (see more)larly in scenarios with limited training samples. Inspired by human perception, we propose that focusing on essential data segments, rather than the entire dataset, can improve the accuracy and reliability of the learning models. However, identifying these critical data segments, or"hard attention finding,"is challenging, especially in few-shot learning, due to the scarcity of training data and the complexity of model parameters. To address this, we introduce LaHA, a novel framework that leverages language-guided deep reinforcement learning to identify and utilize informative data regions, thereby improving both interpretability and performance. Extensive experiments on benchmark datasets validate the effectiveness of LaHA.
2023-10-10
ArXiv (preprint)
doi.org
arxiv.org
Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction
Hadi Hojjati
Mohammadreza Sadeghi
Narges Armanfard
2023-09-16
Lecture Notes in Computer Science (published)
doi.org
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
Narges Armanfard
Negar Ghourchian
S. A. Rahimi
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.
2023-09-12
Dementia and Geriatric Cognitive Disorders Extra (published)
doi.org
Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis
Thi Kieu Khanh Ho
Narges Armanfard
Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detec… (see more)tion of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.
2023-06-25
AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Deep Clustering with Self-supervision using Pairwise Data Similarities
Mohammadreza Sadeghi
Narges Armanfard
Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. Most of the existing me… (see more)thods try to group similar data points through simultaneously minimizing clustering and reconstruction losses, employing an autoencoder (AE). However, they all ignore the relevant useful information available within pairwise data relationships. In this paper we propose a novel deep clustering framework with self-supervision using pairwise data similarities (DCSS). The proposed method consists of two successive phases. First, we propose a novel AE-based approach that aims to aggregate similar data points near a common group center in the latent space of an AE. The AE's latent space is obtained by minimizing weighted reconstruction and centering losses of data points, where weights are defined based on similarity of data points and group centers. In the second phase, we map the AE's latent space, using a fully connected network MNet, onto a K-dimensional space used to derive the final data cluster assignments, where K is the number of clusters. MNet is trained to strengthen (weaken) similarity of similar (dissimilar) samples. Experimental results on multiple benchmark datasets demonstrate the effectiveness of DCSS for data clustering and as a general framework for boosting up state-of-the-art clustering methods.
2023-06-19
INDIGO (preprint)
doi.org
Spatial Hard Attention Modeling via Deep Reinforcement Learning for Skeleton-Based Human Activity Recognition
Bahareh Nikpour
Narges Armanfard
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.
2023-03-16
IEEE Transactions on Systems, Man, and Cybernetics: Systems (unknown)
doi.org
Spatio-temporal hard attention learning for skeleton-based activity recognition
Bahareh Nikpour
Narges Armanfard
2023-02-16
Pattern Recognition (unknown)
doi.org
How can intelligent systems revolutionise health care?
Narges Armanfard
2022-12-31
Futurum Careers (published)
doi.org
Deep Reinforcement Learning in Human Activity Recognition: A Survey
Bahareh Nikpour
Dimitrios Sinodinos
Narges Armanfard
<p>  Human activity recognition is a popular research field in computer vision that has already been widely studied. However, it is … (see more)still an 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 employed to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based human activity recognition has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this field, we have constructed a comprehensive survey on activity recognition methods that incorporate deep reinforcement learning. Towards the end of this survey, we summarize key challenges and open problems in this area that can be addressed by researchers in the future.  </p>
2022-08-28
Figshare (preprint)
doi.org