* denotes equal contribution
- Galib, A. H., Tan, P. N. & Luo, L. (2024, Dec.). FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation. In Proceedings of the Thirty-eighth Annual Conference on Neural Information Processing Systems, NeurIPS 2024.
- Deng, Y., Galib, A. H., Tan, P. N., & Luo, L. (2024, Aug.). Unraveling Block Maxima Forecasting Models with Counterfactual Explanation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 (pp. 562-573). PDF
- Cullen*, L., Smith*, A. W., Galib*, A. H., Varshney*, D., Brown, E., Chi, P., … & Svoboda, F. (2024, Jan.). A Global Analysis of Pre-Earthquake Ionospheric Anomalies. arXiv. PDF
- Galib, A. H., Tan, P. N. & Luo, L. (2023, Dec.). SimEXT: Self-supervised Representation Learning for Extreme Values in Time Series. In Proceedings of the 23rd IEEE International Conference on Data Mining, ICDM 2023 (pp. 1031-1036), IEEE. PDF
- Galib, A. H., McDonald, A., Tan, P. N. & Luo, L. (2023, Aug.). Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage using Self-Supervised Learning. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023 (pp. 3723-3731). PDF
- Cullen*, L., Galib*, A. H., Smith*, A. W., Varshney*, D., Brown, E., Chi, P., … & Svoboda, F. (2022, Dec.). Can We Forecast And Detect Earthquakes From Heterogeneous Multivariate Time Series Data? In I Can’t Believe It’s Not Better Workshop: Understanding Deep Learning Through Empirical Falsification. (ICBINB@ NeurIPS 2022). PDF
- Cullen*, L., Galib*, A. H., Smith*, A. W., Varshney*, D., Brown, E., Chi, P. J., … & Svoboda, F. (2022, Dec.). Open-Source Data Pipelines and Statistical Tool for Studying Pre-Seismic and Post-Seismic Disturbances in the Ionosphere and Geomagnetic Field. In AGU Fall Meeting Abstracts (Vol. 2022, pp. IN25A-07).
- Cullen*, L., Galib*, A. H., Smith*, A. W., Varshney*, D., Brown, E., Chi, P. J., … & Svoboda, F. (2022, Dec.). Comprehensive Statistical Analysis of Ionospheric and Geomagnetic Signatures Before and After Earthquakes. In AGU Fall Meeting Abstracts (Vol. 2022, pp. NH13A-04).
- Varshney*, D., Cullen*, L., Galib*, A. H., Smith*, A. W., Brown, E., Chi, P. J., … & Svoboda, F. (2022, Dec.). Multimodal Machine Learning for Earthquake Identification and Forecasting. In AGU Fall Meeting Abstracts (Vol. 2022, pp. INV44A-05).
- Wilson, T., McDonald, A., Galib, A. H., Luo, L., & Tan, P. N. (2022, Aug.). Beyond Point Prediction: Capturing Zero-Inflated \& Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models. In Proceedings of the 28th ACM SIGKDD 2022 Conference on Knowledge Discovery and Data Mining, KDD 2022 (pp. 2020-2028). PDF
- Galib, A. H., McDonald, A., Wilson, T., Luo, L., & Tan, P. N. (2022, Jul.). DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022 (pp. 2980-2986). PDF
- Galib, A. H., & Bashyal, B. (2022, May.). On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense. arXiv. PDF
- Wilson, T., Tan P., Luo, L., & Galib, A. H. (2021, Dec.). Deep Learning With Extreme Value Theory for Modeling Precipitation Events. In AGU Fall Meeting Abstracts (Vol. 2021, pp. A15Q-07).
- Galib, A. H., & Hossain, B. M. (2020, Jul.). Significant API Calls in Android Malware Detection (Using Feature Selection Techniques and Correlation Based Feature Elimination). In Proceedings of the 32nd International Conference on Software Engineering Knowledge Engineering (SEKE 2020) (pp. 566-571). PDF
- Galib, A. H., & Hossain, B. M. (2019, Dec.). A Systematic Review on Hybrid Analysis using Machine Learning for Android Malware Detection. In 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET 2019). PDF
- Galib, A. H., & Hossain, B. M. (2020, Jul.). A Review on Hybrid Analysis using Machine Learning for Android Malware Detection. In Dhaka University Journal of Applied Science and Engineering (DUJASE), Volume 5, Issue 1\&2, pp. 49-55. PDF
- Yasir, R. M., Asad, M., Galib, A. H., Ganguly, K. K., & Siddik, M. S. (2019, May). GodExpo: an automated god structure detection tool for Golang. In Proceedings of the 3rd International Workshop on Refactoring (IWOR 2019) (pp. 47-50). IEEE. PDF
- Galib, A. H., Nahar, N., & Hossain, B. M. (2020). The Influences of Pre-birth Factors in Early Assessment of Child Mortality using Machine Learning Techniques. arXiv. PDF
