I recently completed and successfully defended my Ph.D. in Computer Science at the University of Waikato. My research centers on developing advanced machine learning algorithms for streaming data. My broader interests span machine learning, data mining, and artificial intelligence. I am particularly passionate about applying data stream techniques to real-world challenges, including energy pricing and Electromyography (EMG) signal analysis.

CapyMOA

As of May 02, 2024, we launched a novel machine learning library for data streams! See more information about it below. We presented tutorials for CapyMOA in various venues in 2024, including PAKDD (Taipei), IJCAI (Jeju, South Korea), KDD (Barcelona, Spain), ECML (Vilnius, Lithuania), KiwiPycon (Wellington, NZ), PRICAI (Kyoto, Japan), and ICONIP (Auckland, NZ). Material is available on the CapyMOA discord here.

• Website: https://capymoa.org/
• CapyMOA Github: https://github.com/adaptive-machine-learning/CapyMOA
• Introductory Paper: https://arxiv.org/abs/2502.07432

Publications

Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning

Y Sun, N Lim, G W Cassales, H M Gomes, B Pfahringer, A Bifet, A Dwivedi. Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2025.

We investigate real-time domain shift detection in EMG data streams using Kernel PCA and drift detectors, revealing current limitations and opportunities for more robust adaptive models.

Dynamic Ensemble Member Selection for Data Stream Classification

Y Sun, B Pfahringer, H M Gomes, A Bifet. ACM 34th Internation Conference on Information and Knowledge Management (CIKM), 2025.

We propose a dynamic ensemble member selection method that adaptively chooses the most relevant classifiers over time to improve accuracy and robustness in data stream classification.

Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis

Y Sun, H M Gomes, B Pfahringer, A Bifet. Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2024. Springer. Longer version at ArXiv.

This study focuses on real-time energy pricing in New Zealand using stream analysis to provide timely and accurate predictions for energy costs.

Adaptive Prediction Interval for Data Stream Regression

Y Sun, H M Gomes, B Pfahringer, A Bifet. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024. Springer.

This paper proposes an adaptive prediction interval model for data stream regression, improving accuracy and reliability over traditional methods by dynamically adjusting prediction intervals.

SOKNL: A novel way of integrating k-nearest neighbours with adaptive random forest regression for data streams

Y Sun, H M Gomes, B Pfahringer. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2022. Springer.

This paper introduces SOKNL, a new approach combining k-nearest neighbors and adaptive random forest regression for enhanced data stream regression performance.