Professor Kaisey Mandel

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

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

PD Aleo, AW Engel, G Narayan, CR Angus, K Malanchev, K Auchettl, VF Baldassare, A Berres, TJL de Boer, BM Boyd, KC Chambers, KW Davis, N Esquivel, D Farias, RJ Foley, A Gagliano, C Gall, H Gao, S Gomez, M Grayling, DO Jones, C-C Lin, EA Magnier, KS Mandel, T Matheson et al.

– The Astrophysical Journal

(2024)

974,

172

(doi: 10.3847/1538-4357/ad6869)

Scalable hierarchical BayeSN inference: investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift

M Grayling, S Thorp, KS Mandel, S Dhawan, ASM Uzsoy, BM Boyd, EE Hayes, SM Ward

– Monthly Notices of the Royal Astronomical Society

(2024)

531,

953

(doi: 10.1093/mnras/stae1202)

gausSN: Bayesian time-delay estimation for strongly lensed supernovae

EE Hayes, S Thorp, KS Mandel, N Arendse, M Grayling, S Dhawan

– Monthly Notices of the Royal Astronomical Society

(2024)

530,

3942

(doi: 10.1093/mnras/stae1086)

Using rest-frame optical and NIR data from the RAISIN survey to explore the redshift evolution of dust laws in SN Ia host galaxies

S Thorp, KS Mandel, DO Jones, RP Kirshner, PM Challis

– Monthly Notices of the Royal Astronomical Society

(2024)

530,

4016

(doi: 10.1093/mnras/stae1111)

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

K Karchev, M Grayling, BM Boyd, R Trotta, KS Mandel, C Weniger

– Monthly Notices of the Royal Astronomical Society

(2024)

530,

3881

(doi: 10.1093/mnras/stae995)

Hawai'i Supernova Flows: A Peculiar Velocity Survey Using Over a Thousand Supernovae in the Near-Infrared

A Do, BJ Shappee, JL Tonry, RB Tully, TD Jaeger, D Rubin, C Ashall, CR Burns, DD Desai, JT Hinkle, WB Hoogendam, ME Huber, DO Jones, KS Mandel, AV Payne, ER Peterson, D Scolnic, MA Tucker

(2024)

(link to publication)

Flight of the Bumblebee: the Early Excess Flux of Type Ia Supernova 2023bee Revealed by TESS, Swift, and Young Supernova Experiment Observations

Q Wang, A Rest, G Dimitriadis, R Ridden-Harper, MR Siebert, M Magee, CR Angus, K Auchettl, KW Davis, RJ Foley, OD Fox, S Gomez, JE Jencson, DO Jones, CD Kilpatrick, JDR Pierel, AL Piro, A Polin, CA Politsch, C Rojas-Bravo, M Shahbandeh, VA Villar, Y Zenati, C Ashall, KC Chambers et al.

– Astrophysical Journal

(2024)

962,

(doi: 10.3847/1538-4357/ad0edb)

Keck Infrared Transient Survey. I. Survey Description and Data Release 1

S Tinyanont, RJ Foley, K Taggart, KW Davis, N LeBaron, JE Andrews, MJ Bustamante-Rosell, Y Camacho-Neves, R Chornock, DA Coulter, L Galbany, SW Jha, CD Kilpatrick, LA Kwok, C Larison, JR Pierel, MR Siebert, G Aldering, K Auchettl, JS Bloom, S Dhawan, AV Filippenko, KD French, A Gagliano, M Grayling et al.

– Publications of the Astronomical Society of the Pacific

(2024)

136,

014201

(doi: 10.1088/1538-3873/ad1b39)

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data

E Angeloudi, J Audenaert, M Bowles, BM Boyd, D Chemaly, B Cherinka, I Ciuca, M Cranmer, A Doh, M Grayling, EE Hayes, T Hehir, S Ho, M Huertas-Company, KG Iyer, M Jablonska, F Lanusse, HW Leung, K Mandel, JR Martinez-Galarza, P Melchior, L Meyer, LH Parker, H Qu, J Shen et al.

– ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (NEURIPS 2024)

(2024)

37,

(link to publication)

Avoiding methane emission rate underestimates when using the divergence method

C Roberts, R IJzermans, D Randell, M Jones, P Jonathan, K Mandel, B Hirst, O Shorttle

– Environmental Research Letters

(2023)

18,

114033

(doi: 10.1088/1748-9326/ad0252)

Faculty of Mathematics

Publications

Research Groups

Email

Room

Telephone

Personal homepage

Forthcoming Seminars

News, Announcements and Events

Social media

Study at Cambridge

About the University

Research at Cambridge