Alvin J. K. CHUA, NUS Presidential Young Professorship

Alvin J. K. CHUA

PhD, University of Cambridge, UK (2017)

Assistant Professor, NUS Presidential Young Professorship

Email: alvincjk@nus.edu.sg
Office: S12-03-07
Tel: Tel: +65 6516 1818

The setting for most of my research is the field of gravitational-wave (GW) astronomy, which has exploded in scientific relevance and public attention since the first direct detection of GWs by the LIGO detectors in 2015. As a theorist in this field, I apply modern computational and statistical techniques to the two-sided problem of source modelling (describing astrophysical GW sources using general relativity) and data analysis (extracting and measuring GW signals in detector data). My recent focus is on binaries with extreme mass ratios, which will be important sources for the near-future space-based detector LISA. I am also broadly interested in machine learning for science, as well as more fundamental topics in applied and computational statistics.

S. Kejriwal, L. Speri and A. J. K. Chua, “Impact of correlations on the modelling and inference of beyond vacuum-GR effects in extreme-mass-ratio inspirals”, Phys. Rev. D 110, 084060 (2024).
A. J. K. Chua and C. J. Cutler, “Non-local parameter degeneracy in the intrinsic space of gravitational-wave signals from extreme-mass-ratio inspirals”, Phys. Rev. D 106, 124046 (2022).
A. J. K. Chua, M. L. Katz, N. Warburton and S. A. Hughes, “Rapid generation of fully relativistic extreme-mass-ratio-inspiral waveform templates for LISA data analysis”, Phys. Rev. Lett. 126, 051102 (2021).
A. J. K. Chua and M. Vallisneri, “Learning Bayesian posteriors with neural networks for gravitational-wave inference”, Phys. Rev. Lett. 124, 041102 (2020).
A. J. K. Chua, “Sampling from manifold-restricted distributions using tangent bundle projections”, Stat. Comput. 30, 587 (2020).