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

Ahmad Al-Jabi

Undergraduate - McGill University

Website

Cormac Cureton

Master's Research - McGill University

Website

Navid hassan Zadeh

PhD - McGill University

Khanh Ho Ho

PhD - McGill University

Hadi Hojjati

PhD - McGill University

Alexander Koran

PhD - McGill University

Thomas Lai

PhD - McGill University

Brian MacDonald

Master's Research - McGill University

Dario Morle

PhD - McGill University

Abhijeet Praveen

Master's Research - McGill University

Aman Sidhu

Master's Research - McGill University

PhD - McGill University

Sareh Soleimani Gilakjani

Postdoctorate - McGill University

Jack Wei

Master's Research - McGill University

Publications

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

Bahareh Nikpour

2023-02-16

Pattern Recognition (unknown)

Graph Anomaly Detection in Time Series: 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 (relationships within a variable over time) and the inter-variable dependency (relationships between multiple variables) existing 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 TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation for time-series data and and its contributions to facilitating anomaly detection. Then, we review state-of-the-art graph anomaly detection techniques, mostly leveraging deep learning architectures, in the context of time series. For each method, we discuss its strengths, limitations, and the specific applications where it excels. Finally, we address both the technical and application challenges currently facing the field, and suggest potential future directions for advancing research and improving practical outcomes.

2023-01-30

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.

2022-12-31

IEEE Transactions on Knowledge and Data Engineering (published)

arxiv.org

How can intelligent systems revolutionise health care?

2022-12-31

Futurum Careers (published)