Engaged in advanced research on AI-based data analysis methodologies tailored for both ground-based and space-based gravitational wave detection.

Specific projects include:

• Developing AI-driven software and scalable algorithms for signal detection, denoising, parameter estimation, and data processing in ground-based gravitational-wave observations.
• Designing global-fit and machine-learning-assisted inference frameworks for resolving overlapping sources and estimating source and instrument parameters in space-based gravitational-wave data.
• Applying LLMs to algorithm design, code development, automated scientific reasoning, and the optimization of gravitational-wave data-analysis workflows.
• Exploring the use of LLMs as scientific discovery tools in fundamental gravitational physics, including the search for exact black-hole solutions in general relativity and applications to numerical relativity, waveform modeling, and related theoretical investigations.

Focused on the integration of AI and machine learning technologies for noise reduction and signal detection in gravitational wave research.

Key contributions include:

• Utilizing pre-trained models from natural language processing to expedite signal detection and enhance noise suppression in ground-based gravitational wave detection.
• Developing fast parameter estimation algorithms based on normalizing flow models for space-based gravitational wave projects.