About Me

I explore the Universe through Bayesian inference of massive and multimessenger datasets enabled by machine learning emulation and GPU-acceleration. The coming era of astronomy will be hallmarked by Petabyte-scale datasets from large surveys, observational data spanning the electromagnetic and gravitational-wave spectrums, and unprecedented machine learning and computational capabilities. My work aims to synthesize information across this complex, multimodal, and multimessenger landscape by leveraging cutting-edge technology and principled statistical frameworks.

Recent News

  • LISA Preparatory Science Award

    A. W. Criswell (Science PI), S. R. Taylor (Institutional PI) secure NASA grant to spearhead development of Galactic and stellar population astrophysics with LISA.

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  • Archival Multiband Gravitational-wave Signals

    Criswell et al. (2026) establish a new low-frequency multiband gravitational-wave signal class which could use pulsars across the Milky Way to peer back in time and observe a massive black hole binary system thousands of years before merger. This work has been accepted to Nature Astronomy.

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  • Advances in Population Inference for LISA

    Criswell et al. (2026) develop the formalism for gravitational-wave population inference of compact Galactic binaries in the LISA global fit.

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