Student Perspectives: Spectral Clustering for Rapid Identification of Farm Strategies

A post by Dan Milner, PhD student on the Compass programme.

Image 1: Smallholder Farm – Yebelo, southern Ethiopia


This blog describes an approach being developed to deliver rapid classification of farmer strategies. The data comes from a survey conducted with two groups of smallholder farmers (see image 2), one group living in the Taita Hills area of southern Kenya and the other in Yebelo, southern Ethiopia. This work would not have been possible without the support of my supervisors James Hammond, from the International Livestock Research Institute (ILRI) (and developer of the Rural Household Multi Indicator Survey, RHoMIS, used in this research), as well as Andrew Dowsey, Levi Wolf and Kate Robson Brown from the University of Bristol.

Image 2: Measuring a Cows Heart Girth as Part of the Farm Surveys

Aims of the project

The goal of my PhD is to contribute a landscape approach to analysing agricultural systems. On-farm practices are an important part of an agricultural system and are one of the trilogy of components that make-up what Rizzo et al (2022) call ‘agricultural landscape dynamics’ – the other two components being Natural Resources and Landscape Patterns. To understand how a farm interacts with and responds to Natural Resources and Landscape Patterns it seems sensible to try and understand not just each farms inputs and outputs but its overall strategy and component practices. (more…)

Congratulations to Compass student for paper accepted for NeurIPS 2022 Proceedings

Congratulations to Compass PhD student, Anthony Stephenson, who along with his supervisors, Robert Allison and Ed Pyzer-Knapp (IBM Research) has had their paper Provably Reliable Large-Scale Sampling from Gaussian Processes  accepted to be published at NeurIPS 2022.

Anthony mentions:

“Gaussian processes are a highly flexible class of non-parametric Bayesian models used in a variety of applications. In their exact form they provide principled uncertainty representations, at the expense of poor scalability (O(n^3)) with the number of training points. As a result, many approximate methods have been proposed to try and address this. We raise the question of how to assess the performance of such methods. The most obvious approach is to generate data from the exact GP model and then benchmark performance metrics of the approximations against the data generating process. Unfortunately, generating data from an exact GP is also in general an O(n^3) problem. We address this limitation by demonstrating how tunable parameters controlling the fidelity of inexact methods of drawing samples can be chosen to ensure that their samples are, with high probability, indistinguishable from genuine data from the exact GP.”

For more information: [2211.08036] Provably Reliable Large-Scale Sampling from Gaussian Processes (

Applications now open for PhD in Computational Statistics and Data Science

Start your PhD in Data Science now

Compass CDT is now recruiting for its fully funded places to start September 2023.

We are happy to announce that The University of Bristol online application system is open, and we are receiving applications for Compass CDT programme for September 2023 start. Early application is advised.

For 2023/34 entry, applicants must review the projects on offer. The projects are listed in the research section of our website. You will need to provide a Research Statement in your application documents with a ranked list of 3 projects of interest to you: 1 being the project of highest interest.

PhD Project Allocation Process

Application forms will be reviewed based on the 3 ranked projects specified. Successful applicants will be invited to attend an interview with the Compass admissions tutors and the specific project supervisor. If you are made an offer of PhD study it will be published through the online application system. You will then have 2 weeks to consider the offer before deciding whether to accept or decline.

The next review of applications for 2023 funded places will take place after

4 January 2023.


We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and black, Asian and minority ethnic communities, to join us.

Advantages of being a Compass Student

  • Stipend – a generous stipend of £21,668 pa tax free, paid in monthly payments. Plus your own expense budget of £1,000 pa towards travel and research activity.
  • No fees – all tuition fees are covered by the EPSRC and University of Bristol.
  • Bespoke training – first year units are designed specifically for the academic needs of each Compass student, which enables students to develop knowledge and capability to pursue cross-disciplinary PhD research.
  • Supervisors – supervisors from across academic disciplines offer a range of research projects.
  • Cohort – Compass students benefit from dedicated offices and collaboration spaces, enabling strong cohort links and opportunities for shared learning and research.

About Compass CDT

A 4-year bespoke PhD training programme in the statistical and computational techniques of data science, with partners from across the University of Bristol, industry and government agencies.

The cross-disciplinary programme offers exciting collaborations across medicine, computer science, geography, economics, life and earth sciences, as well as with our external partners who range from government organisations such as the Office for National Statistics, NCSC and the AWE, to industrial partners such as LV, Improbable, IBM Research, EDF, and AstraZeneca.

Students are co-located with the Institute for Statistical Science in the School of Mathematics, which occupies the Fry Building.

Hear from our students about their experience with the programme

  • Compass has allowed me to advance my statistical knowledge and apply it to a range of exciting applied projects, as well as develop skills that I’m confident will be highly useful for a future career in data science. – Shannon, Cohort 2

  • With the Compass CDT I feel part of a friendly, interactive environment that is preparing me for whatever I move on to next, whether it be in Academia or Industry. – Sam, Cohort 2

  • An incredible opportunity to learn the ever-expanding field of data science, statistics and machine learning amongst amazing people. – Danny, Cohort 1


Wednesday 4 January 2023, 5pm (London, UK time zone)


Compass student publishes article in Frontiers

Compass student Dan Milner and his academic supervisors have published an article in Frontiers, one of the most cited and largest research publishers in the world. Dan’s work is funded in collaboration with ILRI (International Livestock Research Institute). (more…)

Compass students attending APTS Week in Durham

Between 4th and 8th of April 2022 Compass CDT students are attending APTS Week 2 in Durham.

Academy for PhD Training in Statistics (APTS) organises, through a collaboration between major UK statistics research groups, four residential weeks of training each year for first-year PhD students in statistics and applied probability nationally. Compass students attend all four APTS courses hosted by prestigious UK Universities.

For their APTS Week in Durham Compass students will be attending the following modules:

  • Applied Stochastic Processes (Nicholas Georgiou and Matt Roberts): This module will introduce students to two important notions in stochastic processes — reversibility and martingales — identifying the basic ideas, outlining the main results and giving a flavour of some of the important ways in which these notions are used in statistics.
  • Statistical Modelling (Helen Ogden): The aim of this module is to introduce important aspects of statistical modelling, including model selection, various extensions to generalised linear models, and non-linear models.


DataScience@work seminars 2022 announced

We are delighted to announce the confirmed DataScience@work seminars for 2022. Huge thanks to our invited speakers who will be joining us in person and online over the coming months!

The Compass DataScience@work seminar invites speakers from industry, government and third-sector to provide our PhD students with their perspective on the realities of being a data scientist in industry: from the methods and techniques they use to build applications, to working as part of a wider organisation, and how to build a career in their sector.

Find out more on our DataScience@work seminar here.

Compass Guest Lecture: Dr Kamélia Daudel, Postdoctoral researcher Department of Statistics, University of Oxford

Compass news round-up 2021

As we start 2022, we look back at our Compass achievements over 2021…

Invited speakers and seminars

Over the course of the year we invited seminar speakers Ingmar Schuster on kernel methods, Nicolas Chopin offered a two-part lecture on sequential Monte Carlo samplers, Ioannis Kosmidis on reducing bias in estimation and a special two-part lecture from Barnett Award winning Jonty Rougier on Wilcoxon’s Two Sample Test.

Compass student launches PAI-Link

In May, Compass PhD student, Mauro Camara Escudero, set up PAI-Link: a nation-wide AI postgraduate seminar series.

Last year also saw the launch of our DataScience@work seminar series, at which we had 5 external organisations speak (Adarga, CheckRisk, Shell, IBM Research and Improbable) and the British Geological Survey opened this academic year’s seminar series with a talk from alumna Dr Kathryn Leeming.

Training and internships

We ran training sessions on themes such as interdisciplinary research, responsible innovation and a Hackathon run with Compass partners LV= General Insurance, which is recounted by Doug Corbin in his blog post. Compass held its first Science Focus Lab on multi-omics data and cancer treatment with colleagues from Bristol Integrative Epidemiology unit.

Five Compass students were recruited to internships with organisations such as Microsoft Research, Adarga, CheckRisk, Afiniti and Shell.


The Student Perspectives blog series started up last year with Three Days in the Life of a Silicon Valley Start-up. This student-authored series explored topics such as air pollution in Bristol,  the different

Michael Whitehouse in Sky News article

approaches of frequentists and Bayesians, and how to generalise kernel methods to probability distributions.

Michael Whitehouse contributed to a Sky News report on the potential impact of the pandemic on the Tokyo Olympics by modelling the rise of COVID-19 cases in Japan.

Access to Data Science

Compass ran its first Access to Data Science event – an immersive experience for prospective PhD students which aimed to increase diversity amongst data science researchers by encouraging participants such as women and members of the LGBTQ+ and BAME communities to join us.

Research and studentships

Our second cohort of students selected their mini-projects (a precursor to their PhD research) and our third cohort of students joined the Compass programme in September 2021.

Compass students Sept21
Compass Cohort 3 students

Annie Gray presented her paper ‘Matrix factorisation and the interpretation of geodesic distance’ at NeurIPS 2021. Conor Newton gave a talk at a workshop in conjunction with ACM Sigmetrics 2021 and he and Dom Owens won the poster session of the Fry Statistics Conference.  Jack Simons paper ‘Variational Likelihood-Free Gradient Descent’ was accepted at AABI 2022. Alex Modell’s paper ‘A Graph Embedding Approach to User Behavior Anomaly Detection’ was accepted to IEEE Big Data Conference 2021. Danny Williams and supervisor Song Liu were awarded an EPSRC Impact Acceleration Account for their project in collaboration with Adarga.

We also created links with new industrial partners – AstraZeneca, ILRI and EDF – who are each sponsoring Compass PhD projects for the following students: Harry Tata, Dan Milner, and Ben Griffiths and Euan Enticott.


New Opportunity: Funded PhD Project on Developing methods for model selection in causal health analyses

How can statistical modelling tell us what causes disease? Electronic Health Records (EHR) have transformed medical research, with diverse examples including examining risks of emergency admissions on weekends vs weekdays, and health and psychological outcomes after COVID-19. The strengths of analyses based on EHR data include the very large number of individuals available for analysis and the extremely detailed data available for each individual.


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