Research output per year
Research output per year
Personal profile
Biography
Nestor Cabello is a Research Fellow in the Department of Data Science and Artificial Intelligence at Monash University. His research focuses on machine learning, with particular expertise in time series analysis and classification.
He has developed novel algorithms for time series classification that improve accuracy, scalability, and interpretability. His work has been published in leading venues, including the IEEE International Conference on Data Mining and the journal Data Mining and Knowledge Discovery.
His broader research interests include data mining, interpretable machine learning, and the development of advanced methods for analysing complex temporal data.
Research area keywords
- Time series analysis
- Time series classification
- Time Series Forecasting
- Pattern recognition and data mining
- Anomaly Detection
- Feature selection
- representation learning
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Fast and accurate time series classification through supervised interval search
Cabello, N., Naghizade, E., Qi, J. & Kulik, L., 2020, Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020. Plant, C., Wang, H., Cuzzocrea, A., Zaniolo, C. & Wu, X. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 948-953 6 p. 9338332. (Proceedings - IEEE International Conference on Data Mining, ICDM; vol. 2020-November).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
74 Link opens in a new tab Citations (Scopus) -
Fast, accurate and explainable time series classification through randomization
Cabello, N., Naghizade, E., Qi, J. & Kulik, L., Mar 2024, In: Data Mining and Knowledge Discovery. 38, 2, p. 748-811 64 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access26 Link opens in a new tab Citations (Scopus) -
PULSAR: Advancing Interval-Based Time Series Classification to State-of-the-Art Performance
Cabello, N. & Kulik, L., 2025, Proceedings - 25th IEEE International Conference on Data Mining, ICDM 2025. Ding, W., Vreeken, J., Lu, C.-T., Gunopulos, D. & Wu, X. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 1085-1094 10 p. (Proceedings - IEEE International Conference on Data Mining, ICDM).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Ordinal Embedding for Collaborative Filtering: A Unified Regularization for Enhanced Generalization and Interpretability
Yang, J., Luo, L., Cabello, N. & Kulik, L., 2025, Proceedings of the 34th ACM International Conference on Information and Knowledge Management. Yang, C., Basu Roy, S., Li, J. & Kamps, J. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 3762-3772 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile -
Adaptive Rich-kernelized Contrastive Learning for Capacity Enhancement in Collaborative Filtering
Yang, J., Luo, L., Cabello, N. & Kulik, L., 2026, Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval. Bast, H., Najork, M. & Zhang, M. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 2185-2195 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile