Narges Armanfard

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

Bahar Bahareh Nikpour

PhD - McGill University

Cormac Cureton

Master's Research - McGill University

Khanh Ho Ho

PhD - McGill University

Hadi Hojjati

PhD - McGill University

Ali Karami

Master's Research - McGill University

Thomas Lai

PhD - McGill University

Abhijeet Praveen

Master's Research - McGill University

Mohammadreza Sadeghi

PhD - McGill University

Dimitrios Sinodinos

PhD - McGill University

Zihan Wang

Master's Research - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Website

Jack Wei

Master's Research - McGill University

Publications

Multivariate Time-Series Anomaly Detection with Contaminated Data: Application to Physiological Signals

Thi Kieu Khanh Ho

2023-08-24

ArXiv (preprint)

arxiv.org

Deep Multirepresentation Learning for Data Clustering.

Mohammadreza Sadeghi

Deep clustering incorporates embedding into clustering in order to find a lower-dimensional space suitable for clustering tasks. Conventiona… (see more)l deep clustering methods aim to obtain a single global embedding subspace (aka latent space) for all the data clusters. In contrast, in this article, we propose a deep multirepresentation learning (DML) framework for data clustering whereby each difficult-to-cluster data group is associated with its own distinct optimized latent space and all the easy-to-cluster data groups are associated with a general common latent space. Autoencoders (AEs) are employed for generating cluster-specific and general latent spaces. To specialize each AE in its associated data cluster(s), we propose a novel and effective loss function which consists of weighted reconstruction and clustering losses of the data points, where higher weights are assigned to the samples more probable to belong to the corresponding cluster(s). Experimental results on benchmark datasets demonstrate that the proposed DML framework and loss function outperform state-of-the-art clustering approaches. In addition, the results show that the DML method significantly outperforms the SOTA on imbalanced datasets as a result of assigning an individual latent space to the difficult clusters.

2023-07-07

IEEE Transactions on Neural Networks and Learning Systems (published)

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 agentâ€™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.

2023-07-01

IEEE Transactions on Systems, Man, and Cybernetics: Systems (published)

Spatio-temporal hard attention learning for skeleton-based activity recognition

Bahareh Nikpour

2023-02-01

Pattern Recognition (published)

Graph-based Time-Series Anomaly Detection: A Survey

Thi Kieu Khanh Ho

Ali Karami

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time … (see more)series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency and the inter-variable dependency, where a variable can be defined as an observation in time series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of Graph-based TSAD (G-TSAD). First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.

2023-01-31

ArXiv (preprint)

arxiv.org

DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection

Hadi Hojjati

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.

2023-01-01

IEEE Transactions on Knowledge and Data Engineering (published)

arxiv.org

How can intelligent systems revolutionise healthcare?

2023-01-01

Futurum Careers (published)