Aly Ilyas

Profile

I am a full-time PhD researcher at the University of Leeds within the Faculty of Engineering and Physical Sciences, working at the intersection of seismic signal processing, statistical modelling, and computational fluid dynamics.

My research focuses on developing robust methodologies for rapid seismic event detection, combining frequency-domain analysis using Fast Fourier Transform (FFT) with statistical approaches such as the Bayesian Information Criterion (BIC) for parameter optimisation and bootstrap resampling to ensure reliable performance under uncertain noise conditions.

My experience in disaster response through my work with Indonesia Agency for Meteorology, Climatology, and Geophysics (BMKG), including involvement in post-tsunami efforts following the 2018 Anak Krakatau eruption and tsunami 2018 and subsequent early warning system development in the Sunda Strait, has shaped my commitment to integrating technical expertise with humanitarian impact.

Building on this foundation, my current research applies finite volume methods alongside data-driven and statistical techniques to improve the reliability and physical understanding of volcanic tsunami modelling and early warning systems.

Research interests

My research interests lie in seismic signal processing, statistical and data-driven methods for real-time event detection, and computational modelling of geophysical fluid dynamics. I focus on the development of robust early warning methodologies for volcanic tsunamis, by integrating frequency-domain analysis, statistical inference (including Bayesian model selection and resampling techniques), and numerical methods such as finite volume schemes.

I am also interested in uncertainty quantification and anomaly detection in noisy environmental and geophysical datasets, with the broader aim of improving the reliability and interpretability of early warning systems for disaster risk reduction.

Qualifications

• Master in Physical Science
• Bsc in Meteorological Instrumentation