Research

I am the University Assistant Professor of Astrostatistics at the University of Cambridge. I hold this interdisciplinary faculty position jointly at the Institute of Astronomy and at the Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics. My astronomy office is located in the Kavli Institute for Cosmology. I am also a Turing Fellow at the The Alan Turing Institute. 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:

• Supernova cosmology
• Astrostatistics, astronomical machine learning, astroinformatics
• Time-domain and transient astronomy
• Bayesian modeling and inference
• Statistical computation

My research group is a partner in the Young Supernova Experiment time-domain survey using the Pan-STARRS telescopes.
We are also members of: 

• LSST:UK (UK participation in the Legacy Survey of Space and Time) 
• Time-Domain Extragalactic Survey (TiDES) with 4MOST
• International CHASC Astro-Statistics Collaboration

Selected papers

[A more comprehensive list via the Astrophysics Data System]

Jones, D.O., Mandel, K.S., Kirshner, R.P., Thorp, S., Challis, P., Avelino A. et al. Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State. 2022, submitted to ApJ. [ads][arXiv]

Roberts, C., Shorttle, O., Mandel, K., Jones, M., Ijzermans, R., Hirst, B. & Jonathan, P. 2021. Enhanced monitoring of atmospheric methane from space with hierarchical Bayesian inference. accepted for publication in Environmental Research Letters. [ads][arXiv]

Muthukrishna, D., Mandel, K., Lochner, M., Webb, S. & Narayan, G. 2021. Real-time detection of anomalies in large-scale transient surveys. MNRAS, submitted. [ads]

Thorp, S., Mandel, K., Jones, D.O., Ward, S.M. & Narayan, G. 2021. Testing the Consistency of Dust Laws in SN Ia Host Galaxies: A BayeSN Examination of Foundation DR1. MNRAS, 508, 4310. [ads]

Mandel, K., Thorp, S., Narayan, G., Friedman, A. & Avelino, A. 2021. A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical to Near-Infrared. MNRAS, accepted & published. [ads]

Avelino, A., Friedman, A., Mandel, K., Jones. D.O., Challis, P. & Kirshner, R.P. 2019. Type Ia Supernovae are Excellent Standard Candles in the Near-Infrared. ApJ, 887, 106. [ads]

Muthukrishna, D., Narayan, G., Mandel, K., Biswas, R. & Hlozek, R. 2019. RAPID: Early Classification of Explosive Transients using Deep Learning. PASP, 131, 118002. [ads][arXiv]

Patel, E., Besla, G., Mandel, K. & Sohn, S.T. 2018. Estimating the Mass of the Milky Way Using the Ensemble of Classical Satellite Galaxies. ApJ, 857, 78. [ads]

Mandel, K. Scolnic, D., Shariff, H., Foley, R., Kirshner, R.P. 2017.  The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model. ApJ, 842, 93. [ads][arXiv]

Czekala, I., Mandel, K., Andrews, S., Dittman, J., Ghosh, S., Montet, B., Newton, E. 2017. Disentangling Time Series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis. ApJ, 840, 49. [ads]

Patel, E., Besla, G. & Mandel, K. 2017.  Orbits of Massive Satellite Galaxies II: Bayesian Estimates of the Milky Way and Andromeda Masses using high-precision astrometry and cosmological simulations. MNRAS, 468, 3428. [ads][arxiv]

Tak, Hyungsuk, Mandel, K., van Dyk, D., Kashyap, V., Meng, Xiao-Li, Siemiginowska, A. 2017.  Bayesian Estimates of Astronomical Time Delays between Gravitationally Lensed Stochastic Light Curves. The Annals of Applied Statistics, 11, 1309. [ads][arXiv]

Czekala, I., Andrews, S., Mandel, K., Hogg, D., Green, G. 2015.  Constructing a Flexible Likelihood Function for Spectroscopic Inference.  ApJ, 812, 128. [ads]

Mandel, K., Foley, R.J. & Kirshner, R.P. 2014.  Type Ia Supernova Colors and Ejecta Velocities: Hierarchical Bayesian Regression with Non-Gaussian Distributions. ApJ, 797, 75. [ads][arXiv]

Foley, R.J. & Mandel, K. 2013.  Classifying Supernovae using only galaxy data.  ApJ, 778, 167. [ads][arXiv]

Foster, J., Mandel, K., Pineda, J., Covey, K., Arce, H., Goodman, A. 2013.  Evidence for grain growth in molecular clouds: A Bayesian examination of the extinction law in Perseus.  MNRAS, 428, 1606. [ads][arXiv]

Mandel, K., Narayan, G., & Kirshner, R.P. 2011.  Type Ia Supernova Light Curve Inference: Hierarchical Models in the Optical and Near Infrared.  ApJ, 731, 120. [ads][arXiv]

Blondin, S., Mandel, K., & Kirshner, R.P. 2011.  Do spectra improve distance measurements of Type Ia supernovae?  Astronomy & Astrophysics, 526: A81. [ads][arXiv]

Mandel, K., Wood-Vasey, W.M., Friedman, A., & Kirshner, R.P. 2009.  Type Ia Supernova Light Curve Inference: Hierarchical Bayesian Analysis in the Near Infrared.  ApJ, 704: 629-651. [ads]

Mandel, K. and Zaldarriaga, M. 2006.  Weak Gravitational Lensing of High-Redshift 21 cm Power Spectra.  ApJ, 647: 719-736. [ads]

Mandel, K. and Agol, E. 2002.  Analytic Light Curves for Planetary Transit Searches. The Astrophysical Journal, 580: L171-L175. [ads]

Awards and Prizes

2020 - European Research Council Consolidator Grant

2011 - ISBA Savage Award for the Outstanding Doctoral Dissertation in Applied Statistical Methodology

Teaching

Astrostatistics (Part III Maths/Astrophysics), Lent 2018-2021