Bayesian spectral estimation
LogPSplinePSD
Python · JAX · NumPyro · ArviZ
Estimate noise spectra and correlations in time-series data, with posterior uncertainty and diagnostic plots.
About me
I’m a postdoctoral researcher at the University of Auckland, working with
Prof. Renate Meyer. My research focuses on Bayesian inference for gravitational-wave
data, including parameter estimation and noise modelling for LISA. I develop
research software in Python and JAX.
Education
PhD Astrophysics
Monash University
BA Computer Science & Physics
The College of Wooster
Interests
Research software & noisy time series
Gravitational-wave astrophysics
Bayesian inference
Creative coding & game jams
Research software
Tools for Bayesian spectral estimation and time-frequency analysis.
Differentiable time-frequency analysis
wdm-transform
Python · NumPy · JAX
NumPy and JAX implementations of the Wilson–Daubechies–Meyer transform.
Recent Publications
2025–2026
(2026).
Variational Bayesian Inference for the Spectral Structure of LISA Noise.
arXiv e-prints.
(2026).
Bayesian P-spline recovery of stochastic gravitational-wave backgrounds in LISA.
arXiv e-prints.
(2026).
Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors.
arXiv e-prints.
(2026).
Multivariate Bayesian P-spline estimation of spectral density matrices, with application to LISA TDI noise.
arXiv e-prints.
(2026).
An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis.
arXiv e-prints.
(2026).
First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals.
Physical Review D.
(2026).
Enhancing evidence estimation through informed probability density approximation.
Physical Review D.
(2026).
Bayesian power spectral density estimation for LISA noise based on penalized splines with a parametric boost.
Physical Review D.
(2025).
Analysis of GWTC-3 with fully precessing numerical relativity surrogate models.
Physical Review D.
(2025).
Variational inference for correlated gravitational wave detector network noise.
Physical Review D.
Code Activity
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Contact
Email: avi.vajpeyi@auckland.ac.nz
Phone: +64 22 543 1418
Office: Building 303, Room 305, University of Auckland, New Zealand
Hours: Weekdays 08:30-16:30 NZT
GitHub · LIGO GitLab · Itch.io