3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD)
Overview
The 3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD) at AAAI 2026 is a premier platform for researchers and practitioners at the forefront of AI-driven anomaly detection. With the proliferation of spatiotemporal data from sources like surveillance cameras, IoT sensors, and satellite imagery, the need for robust, automated systems to identify novel and unusual patterns has never been more critical. This workshop will delve into cutting-edge AI techniques that move beyond traditional rule-based methods to uncover hidden anomalies, fostering a new generation of intelligent monitoring and discovery. Join us to discuss how we can build more resilient, autonomous, and insightful systems for a safer and more efficient world.
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 AAAI 2026. This workshop aims to explore the latest advancements and novel approaches in anomaly detection (AD) using Artificial Intelligence techniques, with a focus on spatial and temporal dimensions. Topics of Interest :
- Novel deep learning and generative models for spatiotemporal AD
- Self-supervised, unsupervised and few-shot learning for AD with limited data
- Continual learning approaches for evolving anomalous patterns
- Explainable AI for interpreting and justifying anomaly detection decisions
- Foundation models for anomaly detection, including large language models (LLMs) and vision-language models (VLMs)
- Novel datasets, benchmarks, and evaluation metrics
- On-edge anomaly detection in real-time and resource-constrained settings
- Applications in computer vision, robotics, autonomous driving, predictive maintenance, healthcare, finance, and beyond
Submission Site: OpenReview
Schedule
Coming Soon
Important Dates
The important dates are as follows:
- Paper submission deadline: October 22, 2025
- Author notification: November 5, 2025
- Camera-ready deadline: November 10, 2025
Speakers
Coming Soon
Workshop Poster
Organizers
Narges Armanfard (Chair)
Associate Professor - narges.armanfard@mcgill.ca
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.
Thi Kieu Khanh Ho
AI Researcher (PhD) - thi.k.ho@mail.mcgill.ca
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.
Hadi Hojjati
AI Researcher (PhD) - hadi.hojjati@mcgill.ca
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.
