Cohort 1 (2019/20 start)

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Alessio Zakaria

I did my undergraduate degree in Computer Science at the University of Bristol where I focused on formal methods and programming language theory. I then pursued internships and work at a local Natural Language Processing startup wherein I became interested in numerical programming and computational statistics. I am currently interested in numerical methods and optimisation, with a side interest in applying formal methods to statistical programming.


Alexander Modell

I completed my BSc in Mathematics at the University of Bristol, specialising in statistics and undertaking research internships at Cisco and Northrop Grumman. My research interests include networks, point processes, anomaly detection, and the intersections of these fields, particularly with applications to cyber security.


Andrea Becsek

I have graduated from the University of Southampton with First-Class Honours in Mathematics. My research experience includes a placement at the British Antarctic Survey, where my work involved analysing spatiotemporal data related to the Sea Ice Concentration around the Antarctic region. I am passionate about applying statistical methodologies, such as spatiotemporal and multi-level modelling, to climate change, healthcare, and social science.


Daniel Williams

I am originally from Birmingham and then the South West, in that order. My undergraduate degree was at the University of Exeter, where I studied a masters in mathematics; mostly focused on statistics, machine learning and applications to the climate and climate change. I am now in the second year of my PhD as part of the first Compass CDT cohort.

My research interests are currently directed towards probability density estimation in interesting ways, such as on a sphere (or more generally, a manifold), in a truncated space (where our observations are artificially truncated), or a mix of both!

Dominic Owens

I’m researching change point analysis for dependent time series, and my aim is to develop new methods for particular problems – this involves deriving theory and writing efficient algorithms. More broadly, my research interests include high-dimensional and non-parametric statistics, and the links these have with time series analysis.

Doug Corbin

In the summer of 2019, I successfully graduated from The University of Bristol with a bachelor’s degree in Mathematics with Statistics. Following this, I decided to pursue my interest in the field of data science, which led to my successful admission to the new University of Bristol Compass Centre for Doctoral Training. Since beginning my journey in academia, I have enjoyed exploring areas like matrix completion, factor analysis and graphical models. I am also interested in reinforcement learning topics like (partially observed) Markov decision processes.

Jake Spiteri

Before joining Compass I graduated from the University of Bristol with a First-Class Honours degree (MSci) in Mathematics and Statistics. Upon graduating I worked as a freelance business writer while studying modern methods in statistics and machine learning. I am interested in high-dimensional statistics, Bayesian statistics, and graph theory. I am particularly interested in the applications of statistical theory to real-world problems, such as the use of graph theory to understand and potentially detect changes in the community structure of brain networks.


Mauro Camara Escudero

My PhD focuses on developing new representation learning methods such as Normalizing Flows, Variational Autoencoders and Generative Adversarial Networks to improve inference in simulator-based models with application in Population Genetics and Biology. I’m very happy to be supervised by Christophe Andrieu and Mark Beaumont.


Michael Whitehouse

Having graduated from Bristol with a BSc in Mathematics in Summer 2019, I joined the Compass Centre for Doctoral training as a PhD student. During my undergraduate studies I focused on Statistical modelling and Probability Theory. My current research interests include time series modelling, agent based models, and Bayesian filtering of latent variable finance models.

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