Welcome to iSMART Lab

Welcome to the Innovative Solutions in Machine Learning, Artificial Intelligence, and Robotics Technologies (iSMART) Laboratory, founded and led by Prof. Narges Armanfard. Our mission is to pioneer advanced algorithms in artificial intelligence, with expertise in physics-informed modeling, computer vision, time series analysis, tabular data, large language models, and visual language models. Below are our primary areas of focus:

Theoretical Artificial Intelligence: At the iSMART Lab, our research in Theoretical AI is dedicated to exploring foundational principles and pioneering methodologies to advance artificial intelligence technologies. We investigate a broad spectrum of theoretical domains, including attention mechanisms, dimensionality reduction, and time series analysis. Our work also extends to computer vision, where we develop techniques to enhance the interpretation and understanding of visual data. Our research encompasses various learning paradigms such as unsupervised learning, clustering, anomaly detection, reinforcement learning, and inverse machine learning. Additionally, we focus on multi-task and multi-modal learning, as well as self-supervised approaches. Through advancing these theoretical concepts, we strive to improve the robustness, efficiency, and interpretability of AI systems, thereby fostering significant innovations and applications in the field.

Artificial Intelligence in Industry: In the realm of AI in industry, the iSMART Lab leads the integration of artificial intelligence to drive transformative advancements across diverse applications. Our research emphasizes optimizing operations, enhancing efficiency, and ensuring safety and quality within industrial environments. We develop advanced machine learning algorithms and intelligent systems that facilitate automation, predictive maintenance, and process optimization. Whether in autonomous driving technologies, manufacturing processes, or other industrial applications, our work showcases AI’s potential to revolutionize industry operations. By addressing complex challenges and exploring innovative solutions, we assist companies in achieving greater productivity, reducing costs, and fostering innovation across various sectors.

Artificial Intelligence for Healthcare: The iSMART Lab is dedicated to revolutionizing healthcare through cutting-edge AI technologies. Our research aims to enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes. We develop sophisticated AI algorithms that leverage machine learning, data analytics, and predictive modeling to support early disease detection, personalized medicine, and efficient healthcare management. Our collaborations with healthcare institutions ensure that our technological advancements translate into tangible benefits for patients and medical professionals, driving real-world improvements in patient care and medical research.

We extend our gratitude to McGill University, Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de recherche du Québec (FRQ), MITACS, SCALE AI, Canada Foundation for Innovation (CFI), Nissan, Ericsson, Algoma, Preteckt, Trimac, Agewell, Montreal Neurological Institute (MNI), Jewish General Hospital, Canadian Institutes of Health Research (CIHR), National Research Council Canada (NRC), Surftec, and University Health Network UHN for their invaluable support.

Highlights

May 2025

TSAD‑C — a pioneering framework for handling label‑level noise in sensory time‑series anomaly detection—has been accepted for publication at the Uncertainty in Artificial Intelligence conference (UAI 2025), one of the premier venues in AI research. The work introduces a practical method that remains robust even when the training data itself is contaminated with anomalies. Congratulations to the iSMART team!

April 2025

Our in-depth study, Graph-based Time Series Anomaly Detection, has been accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI) — the leading venue in the Machine Learning field. This paper functions almost as a textbook, offering a comprehensive treatment of the topic. Congratulations to Khanh and the entire team.

April 2025

We are being awarded two prestigious and highly competitive NSERC (PGS and CGS) awards in support of our research studies on Time series Data Analysis and Inverse Machine Learning. Congratulations to Thomas, Aman, and the iSMART team!

Mar 2025

We successfully held our half-day workshop at WACV 2025 in Tucson, Arizona, focusing on Anomaly Detection. The session highlighted recent advances in foundational models and self-supervised learning. Special thanks to our outstanding keynote speakers and the organizing team. For more details, see here.

Feb 2025

The book Fundamentals of Linear Algebra for Signal Processing—authored by Prof. James Reilly, an expert with over 35 years of signal processing and Machine Learning experience—has been published by Springer Nature! Happy reading!

Feb 2025

iSMART Wins Prestigious Award for Innovations in AI Healthcare! Congratulations to Our Entire Team!

Jan 2025

Congratulations to Alex on successfully transferring to the PhD program at iSMART! He continues his excellent work on LLM and VLM for Autonomous Driving.

Dec 2024

We are being awarded the prestigious Vadasz Scholar McGill Engineering Doctoral Award in support of our research studies. Congratulations to Alex and the iSMART team!

Dec 2024

Congratulations to Sushant for successfully completing his MSc thesis on Data-driven Mechanical Property Prediction. Excellent work, Sushant!

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