I am the Professor of Astrostatistics and Data Science 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. As of 2024, I am Past Chair of the Astrostatistics Interest Group of the American Statistical Association and a Turing Fellow Alumnus of The Alan Turing Institute. My research interests lie at the intersections of astrophysics, cosmology, statistics, and machine learning.
Research Interests: Astrostatistics and astroinformatics, Applications in time-domain astronomy and cosmology, Bayesian modeling and inference, Statistical computation
Publications
An Early-time Optical and Ultraviolet Excess in the Type-Ic SN 2020oi
– The Astrophysical Journal
(2022)
924,
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,
4310
(doi: 10.1093/mnras/stab2849)
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals
(2021)
(doi: 10.48550/arxiv.1809.11145)
Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
(2020)
(doi: 10.48550/arxiv.2012.12392)
First cosmology results using supernovae ia from the dark energy survey: Survey overview, performance, and supernova spectroscopy
– 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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