
I am the University Assistant Professor of Astrostatistics at the University of Cambridge. I hold this interdisciplinary faculty position jointly at the Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics, and at the Institute of Astronomy. From 2022, I am Chair-Elect of the Astrostatistics Interest Group of the American Statistical Association. My research interests lie at the intersections of astrophysics, cosmology, statistics, and machine learning, and include:
- Astrostatistics, astronomical machine learning, astroinformatics
- Statistical applications in time-domain astronomy and cosmology
- Bayesian modelling and inference
- Statistical computation
Publications
Enhanced monitoring of atmospheric methane from space over the Permian basin with hierarchical Bayesian inference
– Environmental Research Letters
(2022)
17,
064037
(DOI: 10.1088/1748-9326/ac7062)
Enhanced monitoring of atmospheric methane from space over the Permian basin with hierarchical Bayesian inference
– Environmental Research Letters
(2022)
(DOI: 10.1088/1748-9326/ac7062)
An Early-time Optical and Ultraviolet Excess in the Type-Ic SN 2020oi
– Astrophysical Journal
(2022)
924,
ARTN 55
(DOI: 10.3847/1538-4357/ac35ec)
A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical to Near-Infrared
– Monthly Notices of the Royal Astronomical Society
(2021)
510,
3939
(DOI: 10.1093/mnras/stab3496)
Testing the consistency of dust laws in SN Ia host galaxies: A BayeSN Examination of Foundation DR1
– Monthly Notices of the Royal Astronomical Society
(2021)
508,
stab2849-
(DOI: 10.1093/mnras/stab2849)
First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy
– The Astronomical Journal
(2020)
160,
267
(DOI: 10.3847/1538-3881/abc01b)
Type Ia Supernovae are Excellent Standard Candles in the Near-Infrared
– The Astrophysical Journal
(2019)
887,
106
(DOI: 10.3847/1538-4357/ab2a16)
The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals
– The Astronomical Journal
(2019)
158,
171
(DOI: 10.3847/1538-3881/ab3a2f)
RAPID: Early Classification of Explosive Transients Using Deep Learning
– Publications of the Astronomical Society of the Pacific
(2019)
131,
118002
(DOI: 10.1088/1538-3873/ab1609)
Models and simulations for the photometric lsst astronomical time series classification challenge (Plasticc)
– Publications of the Astronomical Society of the Pacific
(2019)
131,
094501
(DOI: 10.1088/1538-3873/ab26f1)
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