Papers
Neural physical engines for inferring the halo mass distribution function
Tom Charnock, Guilhem Lavaux, Benjamin D. Wandelt, Supranta Sarma Boruah, Jens Jasche, Michael J. Hudson
arxiv:1909.06379
The Quijote simulations
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana Maria Delgado, Doogesh Kodi Ramanah, Tom Charnock, et al.
arxiv:1909.05273
Painting halos from 3D dark matter fields using Wasserstein mapping networks
Doogesh Kodi Ramanah, Tom Charnock* and Guilhem Lavaux: *Project supervisor
Physical Review D 100, 0435151 (2019)
arxiv:1903.10524
Fast likelihood-free cosmology with neural density estimators and active learning
Justin Alsing, Tom Charnock, Stephen Feeney and Benjamin Wandelt
Monthly Notices of the Royal Astronomical Society, 488 (2019)
arxiv:1903.00007
Towards online triggering for the radio detection of air showers using deep neural networks
Florian Führer, Tom Charnock*, Anne Zilles and Matias Tueros: *Supervisory role
Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2018) (C18-06-12)
arxiv:1809.01934
Automatic physical inference with information maximising neural networks
Tom Charnock, Guilhem Lavaux and Benjamin D. Wandelt
Physical Review D 97, 083004 (2018)
arxiv:1802.03537
Planck versus large scale structure: methods to quantify discordance
Tom Charnock, Richard A. Battye and Adam Moss
Physical Review D 95, 123535 (2017)
arxiv:1703.05959
Deep recurrent neural networks for supernovae classification
Tom Charnock and Adam Moss
The Astrophysical Journal Letters, 837 L28 (2017)
arxiv:1606.07442
CMB constraints on cosmic strings and superstrings
Tom Charnock, Anastasios Avgoustidis, Edmund J. Copeland and Adam Moss
Physical Review D 93, 123503 (2016) - Editor's Suggestion
arxiv:1603.0.1275
Tension between the power spectrum of density perturbations measured on large and small scales
Richard A. Battye, Tom Charnock and Adam Moss
Physical Review D 91, 103508 (2015)
arxiv:1409.2769
Talks and Posters
Invited Lectures
Novel uses of machine learning: Predicting probabilities and trusting outputs
March 2019 École doctorale - Observatoire de Paris
Beginner's guide to neural networks: Tips and tricks
July 2017 Institut d'Astrophysique de Paris
Beginner's guide to neural networks
June 2017
Invited tutorials
Bayesian deep learning for cosmology and gravitational waves
March 2020 Bayesian deep learning for cosmology and gravitation waves, APC, Paris
Invited seminars
Machine learning for cosmology and astronomy
February 2020 Cosmology and astroparticle physics group, University of Geneva, Switzerland
Machine learning for cosmology and astronomy
February 2020 Physique théorique : gravitation et cosmologie, Institut d'Astrophysique de Paris, Paris
Neural networks: How they work and their future in science
January 2020 Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
Parameter inference using neural networks
December 2019 Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
Bayesian methods for performing inference using neural networks
November 2019 Département d'Informatique, Ecole normale supérieure, Paris
Parameter inference using neural networks
November 2019 Scuola Normale Superiore, Pisa, Italy
Neural physical engines for inferring the halo mass distribution function
October 2019 Laboratoire Astroparticle & Cosmologie, Université Paris Diderot, Paris
Machine learning and its application in astronomy and cosmology
November 2016 Queen Mary University of London
Invited Conference talks
Parameter inference using neural networks
January 2020 Machine learning meets astrophysics, Saclay, France
Modern machine learning methods for trustworthy science
December 2019 Machine Learning Tools for Research in Astronomy, Ringberg Castle, Germany
Lossless data compression for cosmological surveys (and everything else!)
June 2019 AICosmo2019, Ascona
Information maximising neural networks for dimensionality reduction
March 2019 LFI TaskForce, Flatiron Institute, New York
Machine learning based (likelihood-free) statistical inference
October 2018 IHP statistics, Paris
Statistics and novel uses of machine learning
March 2018 IAP Postdoc day, Paris
Generating galaxies with DCGAN
January 2018 DEEPVIS, Paris
Information maximising non-linear data compression
November 2017 BLSS, Munich
Recurrent neural networks and the classification of supernovae
May 2017 ICAP, Paris
Machine learning and its application in supernovae classification
November 2016 Nottingham
Monte Carlo parameter estimation
January 2016 HPC conference, Nottingham
Conference talks
Automatic physical inference with information maximising neural networks
May 2018 CosmoStat21, Valencia
Automatic physical inference with information maximising neural networks
April 2018 StatLSS, Oxford
The state of the tension between the CMB and LSS
November 2015 UKCosmo, Edinburgh
September 2015 COSMO15, Warsaw
Tension between the power spectrum of density perturbation measured on large and small scales
December 2014 YTF2014, Durham
September 2014 BUSSTEPP2014, Southampton
Posters
Cosmic string constraints
April 2016 BritGrav2016
February 2016 Tessella competition
Tension between the power spectrum of density perturbations measured on large and small scales
April 2015 BritGrav2015
February 2015 Tessella competition
Workshops
September 2016 ICIC Data Analysis
May 2016 Astronomical Data Analysis (ADA8)
September 2014 BUSSTEPP2014
Positions of Responsibility
Institut d'Astrophysique de Paris
At the Institut d'Astrophysique de Paris I am an active member of the academic community. In particular, I have become the dedicated consultant at the IAP for advice on machine learning, deep learning, Bayesian techniques, likelihood free inference methods and other statistical tools. I have co-supervised several successful Masters students and led (and supervised) many project groups including those for PhD students, post-doctoral researchers and consortium-level collaborations.

Supervisory roles
2017-present GRAND collaboration advisor, Neural triggers for radio showers
2018-present GRAND collaboration advisor, Bayesian pruning of deep networks for reduced computational architectures
2019-present Euclid collaboration advisor, Statistical understanding of CCD defects and automatic recalibration
2018-2019 PhD project supervisor, Painting halos using novel physically motivated networks
2019 Masters thesis supervisor, Using conditional generative networks to understand CCD detector defects
2019 Masters thesis advisor, Statistical understanding of families in classification networks

Expertise grant review
ANR

Refereed journals
The Astrophysical Journal
Journal of Astronomy and Computing
Journal of Cosmology and Astroparticle Physics
Monthly Notices of the Royal Astronomical Society
Computers in Biology and Medicine

Grants awarded
2018-present AstroPePs xk€ Member
2019 HENON xk€ Member

Journal clubs
I run the machine learning journal club (neural networking) and have organised several machine learning workshops. For information on the machine learning workshop click here.
2017-present Machine learning journal club (neural networking)
2018-present Machine learning workshop organiser
University of Nottingham
I really enjoyed getting involved with both the academic and social workings of the Nottingham cosmology and particle theory group during my PhD. As well as redesigning the particle theory website, creating the particle theory group advertisement slide and organising social events within the group (including the world famous Charneddy Mince Pies and Christmas Jumpers Party) I have taken on the following roles:
2015-2016 BritGrav16 organising committee
2015-2016 Journal club organiser
2014-2015 Student journal club organiser
2013-2017 Website manager
Outreach
I have also particpated in particle physics outreach aimed at attracting new students to study at the University of Nottingham. Some events that I have attended are:
June 2016 Open day particle physics talk
October 2015 Particle physics masterclass
June 2015 Open day particle physics talk
October 2014 Particle physics masterclass
September 2014 Open day particle physics talk
June 2014 Open day particle physics talk