## Scientific interests

**Global fits of dark matter phenomenology:**I combine all available observational probes of particle dark matter in an overarching statistical framework, with the goal of leveraging their constraining power and thus determine the underlying theoretical model for dark matter. Data sets include cosmic microwave background data, Large Hadron Collider measurements, direct detection measurements and indirect detection probes (cosmic rays, neutrinos, gamma ray).**Early Universe and inflationary physics:**I compare theoretical models for inflation with high precision cosmic microwave background (CMB) data and determine the “best” model in terms of Bayesian model comparison.**Type Ia supernovae and dark energy:**I use observations of type Ia supernovae to constrain the properties of dark energy and characterize the accelerated expansion of the Universe.

Taken together, the three above questions (about the nature of dark matter, dark energy and the physics that powered inflation in the very early Universe) represent some of the most important open questions in cosmology today.

## ASTROSTATISTICS AND MACHINE LEARNING

My research develops advanced statistical and numerical methods for the analysis and interpretation of complex data from astrophysics, cosmology and particle physics. The goal is to understand the physical origin and characteristics of dark matter, dark energy and the Big Bang. I use a unified statistical approach to combine data obtained with the world's most powerful telescopes (both orbiting and on the ground) and particle detectors (including dark matter detectors, neutrino telescopes and the Large Hadron Collider at CERN). I develop and run codes for statistical inference in many dimensions (several thousands), on complex “big data” sets (hundreds of thousands of points) and/or in small signal-to-noise regimes.

The theoretical models I seek to explore with these data can have tens of thousands of dimensions. Numerical computations often require the use of heavy parallel computing, machine learning, deep learning and highly efficient algorithms for the mapping of the parameter space. Data visualization is thus another important element of my work.

The scientific questions of my work are very much of a fundamental nature, pertaining to the very building blocks of the cosmos. But the methods I developed in pursuing the answers are very widely applicable to ‘real-world’ problems in many fields, that share a similar statistical structure: for example, medical imaging, environmental risk modelling, pharmaceutical testing, security monitoring, reinsurance and detection of anomalies or patterns in the data.

I collaborate with experimental teams all over the world, in order to understand the data collection process in its detail. I also work closely with statisticians at Imperial and I am a founding member of the Imperial Centre for Inference and Cosmology, a member of Imperial's SpaceLab steering group and a collaborator of Imperial's Data Science Institute.

My spin-off company, Data Fusion Consultants, applies the statistical and techniques produced by my research to some of the most challenging data-driven problems in industry and society.

## KEYWORDS

Dark Matter

Cosmic Inflation

Dark Energy

Markov Chain Monte Carlo

Big Data

Bayesian Methods

Machine Learning

Statistical Techniques

Data Visualization

## Collaborators

GAMBIT, Particle dark matter phenomenology, 2015

Associated Scientist, XENON100 Collaboration, Dark matter direct detection experiment, 2014

Short Term Associate, ATLAS Collaboration, Centre Europeen de Recherché Nucleaire, CERN, Particle Physics and Dark Matter Phenomenology, 2014

Associated Scientist, Fermi-LAT Collaboration, Astroparticle physics and dark matter, gamma-ray space observatory, 2012

Theory Working Group Member, Euclid ESA satellite, Cosmology and dark energy space mission, 2010

## Research Student Supervision

Alexander,A, MSc (2009): Early Big Bang Cosmology

Blanchette,K, MSc (2015): Cosmological Parameter Inference in a Bayesian Hierarchical Model with Redshift Dependent Hubble Residuals !

Bloor,S, PhD (2016-): Computational Quantum Field Theory, Dark Matter and Beyond the Standard Model Physics

Bouvier,H, MSc (2017): Supernovae Type Ia Cosmology

Hoof,S, PhD (2015-): Axion-like dark matter and global fits

Kealy,T, MSc (2009): Early Big Bang Cosmology

Kobayashi,S, MSc (2017): Supernovae Type Ia Cosmology

MacKay,J, PhD (2014-): Vacuum stability and Higgs portal models in global fits

March,MC, PhD (2008-12): Bayesian analysis of dark energy models [Best Physics Thesis Prize 2012 and Springer Thesis Prize]

Monge Imedio,A, MSc (2016): Bayesian analysis of supernova type Ia anisotropies

Shariff,H, PhD (2012-): Bayesian hierarchical modelling of supernova type Ia cosmology

Sin,L, MSc (2015): Bayesian analysis of supernova type Ia anisotropies

Siska,I, MSc (2016): Bayesian analysis of supernova type Ia anisotropies

Strege,C, PhD (2010-14): Multimessenger approach to dark matter [Best Physics Thesis Prize 2014]