Ilgiz Almukhametov is a Research Associate (ML Engineering) at the Centre for Exploration Geophysics, School of Earth and Planetary Sciences, Curtin University (Perth, Australia), where he leads the design and development of machine-learning systems for automated microseismic event detection in Distributed Acoustic Sensing (DAS) data — including the pipeline applied to the CO2CRC Otway Stage 4 dataset in this study. His work centres on end-to-end ML pipeline architecture, retrieval and active-learning methods for extreme class imbalance, geophysical signal processing, and research software engineering (strictly typed, modular, reproducible Python systems for TB-scale data). He holds a Master of Predictive Analytics from Curtin University and a five-year Specialist degree in Applied Mathematics and Computer Science from Ufa State Aviation Technical University. Before joining Curtin, he spent over seven years as a Computational Scientist and Reservoir Simulation Specialist at Rosneft's R&D Centre, developing HPC-based reservoir-simulation, history-matching, and field-development-optimisation software, and was awarded first place at the Rosneft corporate scientific conference. His research interests span applied machine learning, scientific computing, and seismic signal processing.