3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD)

Overview

The 3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD) aims to gather researchers and practitioners in AI to explore the latest advancements and novel approaches in anomaly detection using AI techniques, with a focus on spatial and temporal dimensions. As AI-assisted systems become more embedded in critical applications such as healthcare and industry, the demand for robust anomaly detection methods has intensified. Anomalies can significantly hinder system reliability, making robust detection methods critical for various real-world applications. In the age of foundation models and large models, the 3rd ASTAD workshop would incorporate discussion on the latest trends in anomaly detection, which includes foundation models in anomaly detection, zero-shot and few-shot anomaly detection, real-time anomaly detection in industrial automation and healthcare systems, and explainable AI methods that enhance transparency and reliability.

Call for Paper

We invite researchers and practitioners to submit their original research contributions to the 3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD), held as part of NeurIPS 2025. This workshop aims to explore the latest advancements and novel approaches in anomaly detection using AI techniques within the domain of computer vision. Topics of Interest (but not limited to):

  • Novel AI architectures for anomaly detection in images and videos
  • Large-scale anomaly datasets and benchmarking methodologies
  • Self-supervised, unsupervised, few-shot, and zero-shot anomaly detection techniques
  • Continual learning for anomaly detection
  • Foundation models and Large Models for anomaly detection
  • Interpretability and explainability in anomaly detection
  • Real-world anomaly detection applications in healthcare, industry, automotive sector, etc.
  • Cross-modal and tabular anomaly detection
  • Real-time anomaly detection

Submission Site: Coming Soon

Important Dates

The important dates are as follows:

  • Paper submission deadline: TBD
  • Author notification: TBD
  • Camera-ready deadline: TBD

Speakers

To be announced

Organizers

Narges Armanfard (Chair)

Associate Professor

Narges Armanfard is an Associate Professor of Electrical and Computer Engineering at McGill University and Mila - Quebec AI Institute. She is also affiliated with McGill Centre for Intelligent Machines (CIM), McGill initiative in Computational Medicine (MiCM) and McGill Institute for Aerospace Engineering (MIAE). Dr. Armanfard is the founder and principal investigator of the iSMART Lab with the mission to pioneer advanced algorithms in artificial intelligence, with expertise in computer vision, time series analysis, tabular data, large language models, and visual language models.

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Sareh Soleimani

AI Researcher (Postdoctoral)

Sareh received the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Ottawa, Ontario, Canada, both with the prestigious University of Ottawa admission scholarships. She subsequently joined Queen’s University as a postdoctoral fellow focusing on privacy-preservation audio classification for resource-constrained devices. Her research at the iSMART lab focuses on computer vision towards industrial automation.

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Thi Kieu Khanh Ho

AI Researcher (PhD)

Khanh received her Master’s degree at Gwangju Institute of Science and Technology (GIST), South Korea. She was a researcher at Seoul National University Hospital (SNUH), South Korea, before moving to Montreal for her PhD. She has received the prestigious McGill Engineering Doctoral Award (MEDA), the GREAT Award, and the highly prestigious Vanier Scholarship. Her research focuses on time-series anomaly detection using generative models and graphs.

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Hadi Hojjati

AI Researcher (PhD)

Hadi received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology, Iran. He started as an MSc student at the iSMART Lab. and fast-tracked to PhD. He has received the Graduate Excellence Fellowship Award (GEF), McGill Engineering Doctoral Award (MEDA), GREAT Award, and AGE-WELL Award. He is currently doing research on multi-modal data analysis and anomaly detection employing lightweight multimodal large language models.

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Dimitrios Sinodinos

AI Researcher (PhD)

Dimitrios completed his Bachelor’s degree in Electrical Engineering at McGill University and was awarded the British Association Medal for graduating with the highest CGPA in the department. Shortly after starting his Master’s, Dimitrios fast-tracked into a PhD, where he received the MEDA Award and was selected into the prestigious Vadasz Scholars program. His research involves multi-task learning learning employing lightweight multimodal large language models.

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Thomas Lai

AI Researcher (PhD)

Thomas received his Bachelor of Engineering (B.Eng.) from McGill University. He joined the iSMART Lab in the summer of 2023 and subsequently pursued an MSc, focusing on open-set anomaly detection. A year after starting his Master’s at the iSMART Lab, Thomas fast-tracked into a PhD. He is a recipient of the MEUSMA and FRQ awards. His research is on open-set anomaly detection leveraging LLM models.

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Alexander Koran

AI Researcher (PhD)

Alex earned a Bachelor’s degree in Computer Engineering with minors in Mathematics and Artificial Intelligence in Fall 2023. He joined the iSMART Lab in the summer of 2023 and subsequently pursued an MSc, focusing on time series anomaly detection. In January 2025, Alex began his PhD at the iSMART Lab. He is a recipient of the GEF Award and the prestigious Vadasz Engineering Fellowship. His research focuses on leveraging vision language models (VLMs) for video anomaly detection.

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Navid Hassan Zadeh

AI Researcher (PhD)

Navid received his Bachelor’s degree in Joint Honours in Mathematics and Computer Science from McGill University. He pursued an MSc at the Computer Science Department of the University of Montreal. He is currently an RA at the iSMART Lab. He will begin his Ph.D. studies at the iSMART Lab in September 2025. His research interests include theories of Machine Learning and its applications.

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