91Ö±²¥

Subject

Astrophysics and Cosmology

Degree(s)

Ph.D. (King’s College London), B.A., M.Math (University of Cambridge)

Research interests

My main research interests lie in the application of machine learning and statistical techniques to the analysis of gravitational wave detector data. In particular, I am interested in the challenges posed by the space-based LISA experiment when trying to find stochastic signals from the early Universe.

Teaching Interests

Supervisions: Part IA Mathematics for Natural Sciences

Publications

Leveraging Time-Dependent Instrumental Noise for LISA SGWB Analysis ()

Dwarf galaxies imply dark matter is heavier than 2.2×10−21 eV ()

Simulation-based inference for stochastic gravitational wave background data analysis ()

Scalable inference with autoregressive neural ratio estimation ()

What to do when things get crowded? Scalable joint analysis of overlapping gravitational wave signals ()

Sequential simulation-based inference for gravitational wave signals ()

Albatross: a scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way ()

No room to hide: implications of cosmic-ray upscattering for GeV-scale dark matter ()

Cosmic neutrino background detection in large-neutrino-mass cosmologies ()

What can CMB observations tell us about the neutrino distribution function? ()

Constraints on global symmetry breaking in quantum gravity from cosmic birefringence measurements ()

New constraints on the mass of fermionic dark matter from dwarf spheroidal galaxies ()

The axion quality problem: global symmetry breaking and wormholes ()

Improved BBN Constraints on the Variation of the Gravitational Constant ()

Refined Bounds on MeV-scale Thermal Dark Sectors from BBN and the CMB ()

Detecting Light Dark Matter via Inelastic Cosmic Ray Collisions ()

Linking Scalar Dark Matter and Neutrino Masses with IceCube 170922A ()

Other interests

Triathlon, cycling, cricket, jazz guitar