Research
My research focuses on physics-first machine-learning approaches to gravitational-wave data analysis, with applications to both ground-based detectors (LIGO) and future space-based missions (LISA). I develop template-free and unsupervised methods designed for robustness, interpretability, and discovery.
Me at the Hanford LIGO interferometer facility during detector characterization work.
Publications
See the full list here: /publications/
Latest Projects
Manifold Learning for Source Separation in Confusion-Limited Gravitational-Wave Data.
The Laser Interferometer Space Antenna (LISA) will operate in a fundamentally different data-analysis regime than ground-based detectors such as LIGO. Rather than rare signals buried in instrumental noise, LISA observations are expected to be dominated by a dense superposition of unresolved Galactic binaries, forming a confusion-limited background. A central challenge is identifying resolvable sources that deviate meaningfully from this background.
In this project, I investigate whether manifold-learning techniques can aid source separation in confusion-dominated gravitational-wave data. I develop a convolutional autoencoder trained exclusively on synthetic confusion-background data and augment the standard reconstruction-error anomaly score with a geometric term derived from the local structure of the learned latent-space manifold. This combined score exploits not only reconstruction fidelity but also deviations from the geometric structure characteristic of the background.
Tests on synthetic LISA datasets with injected massive black hole binaries, extreme mass ratio inspirals, and individual Galactic binaries demonstrate that incorporating latent-space geometry substantially improves source discrimination compared to reconstruction error alone. These results suggest that the learned latent manifold captures physically meaningful structure in the confusion background and that manifold-aware anomaly detection may provide a practical, template-free tool for LISA data analysis.
Manuscript submitted to Classical and Quantum Gravity.
Gravitational Wave Hunting with CWT-LSTM Autoencoder in LIGO Data.
Gravitational-wave searches traditionally rely on matched filtering against large banks of theoretical waveforms, which can be computationally expensive and inherently biased toward known signal morphologies. In this project, I develop a template-free, unsupervised detection framework that combines physically motivated time–frequency representations with sequence-based machine learning.
The method applies a continuous wavelet transform (CWT) to LIGO strain data to produce time–frequency representations aligned with the chirp-like evolution of compact binary coalescences. An LSTM-based autoencoder is then trained exclusively on detector noise, allowing gravitational-wave signals to be identified as anomalies without the use of waveform templates or labeled training data.
During development, I identified a previously underappreciated issue: when training across multiple LIGO observing runs, the autoencoder’s latent structure clustered by observing run rather than by astrophysical signal properties. This behavior revealed systematic batch effects associated with evolving detector calibration and preprocessing, rather than true physical differences. Adopting a per-run training strategy—consistent with established LIGO analysis practice—eliminated these effects and substantially improved detection performance.
Applied to O4 LIGO data, the resulting pipeline achieves high-efficiency, high-purity detection using a purely unsupervised approach, demonstrating that physics-informed anomaly detection can perform competitively with supervised methods while remaining sensitive to signals with unexpected or poorly modeled morphologies. This work highlights both the promise of template-free methods for discovery-oriented searches and the importance of careful treatment of non-stationarity in multi-epoch gravitational-wave datasets.
Accepted for publication in Classical and Quantum Gravity.