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

09:00 – 09:15: Opening
09:15 – 09:30: Workshop Overview and Plan
09:30 – 10:30: Keynote: Dr. Ye Zhu
10:30 – 11:00: Coffee Break
11:00 – 12:00: Paper Session (Oral)
12:00 – 13:00: Lunch Break
13:00 – 14:00: Keynote: Prof. Pang Guansong
14:00 – 15:00: Keynote: Dr. Jie Ren
15:00 – 15:30: Poster Session
15:30 – 16:00: Coffee Break
16:00 – 16:30: Poster Session
16:30 – 17:00: Poster Session and Closing

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
Workshop Date: January 26, 2026 (9AM - 5PM Singapore Time)

Speakers

Dr. Ye Zhu

School of Information Technology, Deakin University

Dr Ye Zhu is a Senior Lecturer of computer science with the School of Information Technology, Deakin University, Geelong, VIC, Australia. He is an IEEE senior member and also a visiting faculty in Peking University and Nanjing University. He received a PhD degree in Artificial Intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017. His research focuses on the fields of data mining and machine learning, particular topics including clustering analysis, anomaly detection, similarity learning, and their applications for pattern recognition and information retrieval. Dr Zhu has published over 90 papers in top-tier conferences and journals, including SIGKDD, VLDB, ICML, IJCAI, AAAI, AIJ, VLDBJ, ISJ, TKDE, PRJ, JAIR, and MLJ.

Prof. Pang Guansong

School of Computing and Information Systems, Singapore Management University

Dr. Guansong Pang is a tenure-track Assistant Professor of Computer Science and Lee Kong Chian Fellow at the School of Computing and Information Systems, Singapore Management University (SMU), where he leads the Machine Learning & Applications (MaLA) Lab. He is also a faculty member of Centre on Security, Mobile Applications and Cryptography. He was a Research Fellow with the Australian Institute for Machine Learning (AIML), University of Adelaide, Australia. Before joining AIML, he received his Ph.D. at University of Technology Sydney (UTS), Australia. His research interests include machine learning, data mining, and computer vision, with a research theme focused on recognizing and generalizing to abnormal, unknown, or unseen data for creating trustworthy AI systems. Read more Read less

Dr. Jie Ren

Google DeepMind

Jie Ren is a Staff Research Scientist at Google DeepMind. She holds a PhD in Computational Biology and Bioinformatics and an MSc in Statistics, from the University of Southern California. Jie’s research centers on developing trustworthy AI solutions that can be safely deployed in real-world scenarios, aiming to advance scientific discoveries and enhance human well-being. Her work spans three key areas: (1) uncertainty estimation and robustness in large foundation models, (2) out-of-distribution detection and robustness in deep learning, and (3) the development of reliable machine learning for real-world application, with a special focus on biological and medical research.

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.