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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post 1
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portfolio
Panelo.ai – AI-Driven Panel-Drawing Management Platform Permalink
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End-to-end web platform that streamlines precast-engineering workflows.
publications
Investigating Active Positive-Unlabeled Learning with Deep Networks
Published in AI 2021 – 34ᵗʰ Australasian Joint Conference on Artificial Intelligence, 2022
We propose an active learning framework that combines positive–unlabeled (PU) learning with deep networks. By actively querying the most informative negatives, our method reduces annotation cost while maintaining competitive performance on noisy real-world data.
Recommended citation: Kun Han, Weitong Chen, & Miao Xu. (2022). “Investigating Active Positive-Unlabeled Learning with Deep Networks.” *Proceedings of the 34ᵗʰ Australasian Joint Conference on Artificial Intelligence (AI 2021)*, pp. 607–618. https://doi.org/10.1007/978-3-030-97546-3_49
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Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach
Published in Australasian Database Conference (ADC 2025), 2025
We introduce a principled risk-estimation framework for mining irregular time-series data with noisy labels. By modelling per-instance noise and calibrating risk bounds, our method delivers robust classification while providing uncertainty estimates that aid downstream decision-making.
Recommended citation: Kun Han, Abigail M.Y. Koay, Ryan K.L. Ko, Weitong Chen & Miao Xu (2025). “Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach.” *Proceedings of the Australasian Database Conference (ADC 2025)*, pp. 293–307. Springer, Singapore.
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HITS: Hierarchical Interpretable Time Series Classification via Multiple Instance Learning
Published in IJCNN 2025 – International Joint Conference on Neural Networks, 2025
HITS introduces a two-level MIL framework that combines variable-level and temporal-patch attention, achieving state-of-the-art accuracy on irregular multivariate time-series benchmarks while delivering clear, human-readable explanations.
Recommended citation: Kun Han, Abigail M.Y. Koay, Ryan K.L. Ko, Weitong Chen & Miao Xu (2025). “HITS: Hierarchical Interpretable Time Series Classification via Multiple Instance Learning.” *Proceedings of the International Joint Conference on Neural Networks (IJCNN 2025)*. Rome, Italy.
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Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
Published in Frontiers of Computer Science, Vol. 19 (Issue 8), 2025
We propose an instance-attention graph neural network (IA-GNN) that learns robust representations for irregular and multivariate time-series streams. By modelling cross-timestamp relations as a dynamic graph and applying fine-grained instance attention, the method achieves state-of-the-art accuracy while remaining interpretable.
Recommended citation: **Kun Han**, Abigail M.Y. Koay, Ryan K.L. Ko, Weitong Chen & Miao Xu (2025). “Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data.” *Frontiers of Computer Science*, 19(8):198340. https://doi.org/10.1007/s11704-024-40449-z
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.