A post by Dan Milner, PhD student on the Compass programme.
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.
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…)
September saw the first annual Compass Conference, hosted in the newly refurbished Fry Building, home to the School of Mathematics. The conference was a fantastic opportunity for PhD students across Compass to showcase their research, meet with industrial partners and to celebrate their achievements. The event also welcomed the new cohort of PhD students, as well as prospective PhD students taking part in the Access to Data Science programme. (more…)
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.
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.
In May, Compass PhD student, Mauro Camara Escudero, set up PAI-Link: a nation-wide AI postgraduate seminar series.
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.
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.
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.
A post by Conor Crilly, PhD student on the Compass programme.
This project investigates uncertainty quantification methods for expensive computer experiments.It is supervised by Oliver Johnson of the University of Bristol, and is partially funded by AWE.
Physical systems and experiments are commonly represented, albeit approximately, using mathematical models implemented via computer code.This code, referred to as a simulator, often cannot be expressed in closed form, and is treated as a ‘black-box’.Such simulators arise in a range of application domains, for example engineering, climate science and medicine.Ultimately, we are interested in using simulators to aid some decision making process.However, for decisions made using the simulator to be credible, it is necessary to understand and quantify different sources of uncertainty induced by using the simulator. Running the simulator for a range of input combinations is what we call a computer experiment .As the simulators of interest are expensive, the available data is usually scarce.Emulation is the process of using a statistical model (an emulator) to approximate our computer code and provide an estimate of the associated uncertainty.
Intuitively, an emulator must possess two fundamental properties
It must be cheap, relative to the code
It must provide an estimate of the uncertainty in its output
A common choice of emulator is the Gaussian process emulator, which is discussed extensively in  and described in the next section.
Types of Uncertainty
There are many types of uncertainty associated with the use of simulators including input, model and observational uncertainty.One type of uncertainty induced by using anexpensivesimulator is code uncertainty, described by Kennedy and O’Hagan in their seminal paper on calibration .To paraphrase Kennedy and O’Hagan:In principle the simulator encodes a relationship between a set of inputs and a set of outputs, which we could evaluate for any given combination of inputs.However, in practice, it is not feasible to run the simulator for every combination, so acknowledging the uncertainty in the code output is required.(more…)