Research

Gravitational waves can carry information not only about a binary, but also about how it moves through its surroundings. My research examines waveform changes caused by recoil, orbital motion, and gas-induced dynamical friction. These studies explore how such effects can reveal kick velocities, globular cluster properties, supermassive black hole masses and spins, and gas densities in active galactic nucleus disks.

Reliable gravitational-wave astronomy depends on waveform models that balance accuracy with computational cost. My work examines Newtonian and post-Newtonian approximations and explores fast generation of binary neutron star waveforms with tidal and precession effects. Related studies use deep learning to identify multiband signals lensed by dark matter halos, linking waveform modeling with signal recognition.

General relativity predicts two tensor polarizations and specific gravitational-wave signatures of binary evolution. My research explores how additional polarizations and parametrized waveform deviations could be constrained by observations. These studies examine detector networks, multiband observations, and time-delay interferometry, while developing analytical waveform derivatives for efficient and stable Fisher-matrix forecasts.

Many weak sources overlap in gravitational-wave data, while correlated detector noise can complicate the search for a cosmic background. My work studies Galactic binary foreground subtraction, parameter estimation for resolvable binaries, and machine-learning identification of cosmic-string backgrounds. It also examines how correlated solar-wind plasma noise can bias network measurements, helping assess the reliability of future space-based background searches.