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Dr Will Handley

Dr Will Handley

Research Fellow (Gonville & Caius College)

Inflationary Cosmology

Bayesian Statistics and Machine Learning

Will Handley is accepting applications for PhD students.

Will Handley is available for consultancy.

Office Phone: (01223) 764042


2016 Oct-Present: Junior Research Fellow at Gonville & Caius College

2016 July-Sep: Postdoctoral position at UCL

2012-2016: PhD in Physics (University of Cambridge)

2008-2012: MSci in Experimental and Theoretical Physics (University of Cambridge)


Research Interests

Will Handley is a theoretical cosmologist, studying the very earliest moments of the universe. 

His main interest is in "initial conditions for inflation", examining the effect that high energy physics has on the universe a split second after the Big Bang. His current research is focused on examining the theoretical and observational consequences of primordial curvature, searching for imprints in the cosmic microwave background of the shape of the universe at the beginning of time.

In addition to theoretical investigation, he also examines the observational consequences of these new ideas; testing theories against the latest data from microwave telescopes and large scale structure surveys using the University supercomputers.

He also works on developing novel Bayesian algorithms for high-dimensional parameter estimation and model comparison using nested sampling. Possible applications currently under investigation include protein folding, sparse reconstruction for facial recognition and Bayesian neural network training in addition to cosmological parameter estimation and model comparison. His latest theoretical statistical work revolves around quantifying tensions in measurements of cosmological parameters.

He is an active member of REACH, a team of cosmologists designing and operating a radio telescope to make an unambiguous detection of the global 21cm signal coming from the cosmic dawn.

Research Supervision

Postdoctoral researchers: Kamran Javid (2018-9)

PhD Students (co-supervised): 

Ian Roque, Harry Bevins (2019-present)

Dominic Anstey (2018-present)

Lukas HergtFruzsina AgocsWill Barker (2017-present)

Masters Students: 

Thomas Gessey-Jones, Aleksandr Petrosyan, Ayngaran Thavanesan, Emma Shen (2019-present) 

Deaglan Bartlett, Jamie Bamber, Ian Roque (2018), 

Ward Haddadin, Jessica Rigley, Panagiotis Mavrogiannis (2017), 

Fruzsina Agocs, Robert Knighton, Stephen Pickman, Daniel Manela (2016)

Summer Students: 

Denis Werth, Maxime Jabarian, Liam Lau (2019), 

Elizabeth Guest, Ward Haddadin, Shu-Fan Chen (2018)



  • Supervisions: Part IA Mathematics for Natural Sciences (2012-2017)
  • Supervisions: Part IA Physics for Natural Sciences (Michaelmas 2015 - Easter 2016)
  • Supervisions: Part II General Relativity (2013-2018)
  • Demonstrating: Part II Theoretical Physics 1 (Michaelmas 2012)
  • Demonstrating: Part II Theoretical Physics 2 (Lent 2013)
  • Tripos Examples Classes: Part IA Mathematics for Natural Sciences (Easter 2014 -Present)

Part III Projects


Other Professional Activities

Awards and Prizes

  • December 2013: Best Presentation (Cavendish Graduate Students Conference)
  • 2011-2012: Best Theoretical final year project (University of Cambridge)
  • 2018: Gruber Prize (Awarded to Planck members)
  • 2019: Guiseppe and Vanna Cocconi Prize (Shared between WMAP and Planck)


  • Nested Sampling: an efficient and robust Bayesian inference tool for physics and machine learning, Adelaide Colloquium, Australia (Feb 2020)
  • Nested Sampling: an efficient and robust Bayesian inference tool for astrophysics and cosmology, Oxford, UK (Jan 2020)
  • PolyChord: next generation nested sampling, Mathematical challenges in the electromagnetic environment, DMTP, Cambridge, UK (Jan 2020)
  • Quantised primordial power spectra, Texas 2019, Portsmouth, UK (Dec 2019)
  • Nested Sampling: an efficient and robust Bayesian inference tool for machine learning and data science, CDT talk, Cambridge, UK (Nov 2019)
  • Curvature tension: evidence for a closed universe(?), ICG Portsmouth, UK (Aug 2019)
  • Quantifying cosmological tensions, UCL, UK (Jul 2019)
  • Likelihood-free inference, GAMBIT X, Germany (Jun 2019)
  • Compromise-free Bayesian sparse reconstruction, LFI workshop, Flatiron institute, US (Feb 2019)
  • Inflation, curvature and kinetic dominance, Future uses of Planck data, ESAC, Spain (Dec 2018)
  • BAMBI Resurrection: Blind Accelerated Multimodal Bayesian Inference, DarkMachines, Worldwide (Nov 2018)
  • Nested Sampling: an efficient and robust Bayesian inference tool for cosmology and particle physics, DarkMachines, Worldwide (Nov 2018)
  • Bayesian Statistics, Third Asterics-Obelics workshop, Cambridge, UK (Oct 2018)
  • Planck, inflation and the future of inflationary constraints, Consistency of Cosmological Datasets, Cambridge, UK (May 2018)
  • MaxEnt priors with derived parameters in a specified distribution, Cambridge (May 2018)
  • Nested Sampling: an efficient and robust Bayesian inference tool for astrophysics and cosmology ICIC, London (May 2018)
  • Introduction to statistics, CosmoTools 18, RWTH Aachen, Germany (April 2018)
  • Advances in Nested Sampling and astrophysical application, Cambridge, UK (Jan 2018)
  • PolyChord 2.0: Fast cosmological inference with Nested Sampling, Cosmo 17 Paris (Aug 2017)
  • Modern Bayesian Inference: Theory and Practice, RWTH Aachen (Jun 2017)
  • Parameter estimation and Model comparison, IFT Summer School, Madrid (Mar 2017)
  • PolyChord 2.0: Advances in Nested Sampling with astrophysical applications, CCA, Flatiron institute, New York (Feb 2017)
  • PolyChord 2.0 & the future of nested sampling, University College London, UK. (Sep 2016)
  • PolyChord 2.0 & the future of nested sampling, University of Sussex, UK. (May 2016)
  • PolyChord & the future of nested sampling, Edinburgh, UK. (Mar 2016)
  • PolyChord: next generation nested sampling, Max Planck Institute, Germany. (Dec 2015)
  • PolyChord: next generation nested sampling, University of Sussex, UK. (Feb 2015)
  • Kinetic dominance in the pre-inflationary universe, Cavendish grad. conference (Dec 2013)



Bayesian Statistics ; Machine Learning ; Inflation ; Cosmology ; Numerical methods


  • Relativity and Gravitation

Key Publications

Other Publications