It’s now been six months since I switched from maths to healthcare (Wow, time goes fast!) and I’m starting to understand the bigger picture of funding bodies, legislative restrictions, and government-affiliated organisations in the area.
Two of these bodies are the MRC (Medical Research Council) and NHS Digital (owners of most secondary care datasets), so I was keen to attend their “roadshow” event in York. The event outlined some of the major upcoming changes to the healthcare domain over the next few years, mainly arising from the EUs data protection law, GDPR. I’ve summarised some of the key points below whilst the slides from the event can be found here.
I managed to stock up on Betty’s tea whilst in York
Continue reading “Summary of the MRC / NHS Digital Roadshow, York, Jan 2018”
As some of you might know, I recently made a (fairly large) career change. I’m now a Research Fellow in Health Data Analytics at The University of Leeds, UK. Since I’m coming from a maths/HPC background and have almost no prior knowledge of healthcare I’ve had a bit of catching up to do! Overall I’m aiming to apply machine learning and large-scale data analysis to large datasets arising from health and socio-economic domains, where my skills in linear algebra and HPC will undoubtedly become useful.
In this post I aim to summarise what I’ve learned in my first few weeks on the job. This is partly to archive things for myself but should also be interesting reading for anyone who wants to get involved with health data. I’ll touch upon
- the legal issues arising from using private data,
- interesting talks and papers that I’ve found, and,
- some recurring opinions on the future challenges in the field.
Continue reading “A Crash Course in Health Analytics”
Over the last year, there has been significant interest in solving many small linear algebra problems simultaneously. Library vendors such as MKL and NVIDIA, along with researchers at instutions including Manchester, Tennessee, and Sandia National Labs have all been attempting to perform these calculations as efficiently as possible.
Over the weekend prior to the SIAM CSE17 meeting, many of those researchers (including myself) held a workshop to discuss strategies for batched BLAS (basic linear algebra subprogram) computations. Furthermore, lots of discussion was aimed at standardising the function APIs and the memory layout that users will interact with. The slides, and a number of research papers on the topic, are available at this page.
At the SIAM CSE17 meeting, our team at Manchester organised a minisymposium to discuss the highlights of our weekend with a wider audience. A brief summary of the four talks, along with a copy of their slides, is given below.
Continue reading “Batched BLAS Operations at SIAM CSE17”
Last week Nick Higham, Edvin Deadman, and I ran a minisymposium on matrix functions at the SIAM Applied Linear Algebra 2015 conference (link). This post gives a brief summary of each talk, links to published work, and (once they appear) links to the slides with synchronised audio.
Edit: Links to the talks are now available.
Attendance at the sessions was very good, with some high-quality questions coming from the audience.
The symposium had two sessions.
- Marcel Schweitzer – Error Estimation in Krylov Subspace Methods for Matrix Functions
- Michele Benzi – Functions of Matrices with Kronecker Sum Structure
- Bruno Iannazzo – First-Order Riemannian Optimization Techniques for the Karcher Mean
- Sivan Toledo – A High Performance Algorithm for the Matrix Sign Function
- Peter Kandolf – The Leja Method: Backward Error Analysis and Implementation
- Massimiliano Fasi – An Algorithm for the Lambert W Function on Matrices
- Antii Koskela – An Exponential Integrator for Polynomially Perturbed Linear ODEs
- Edvin Deadman – Estimating the condition number of f(A)b
Peter Kandolf describing the famour “hump” in the matrix exponential.
Continue reading “SIAM ALA 15 – Minisymposium on Matrix Functions”
Recently I’ve been working with some of the statistics staff at the University of Manchester on sports analytics. Specifically we’ve been looking for useful models in football data. People from this background normally use R to analyze data and fit models.
Normally I would use Python for this kind of task but, since there was already a considerable amount of code in R, it made sense for me to do some work in R. The people at Continuum Analytics (who make the brilliant Anaconda Python distribution) recently announced support for R using their package manager conda. However, it wasn’t easy to find instructions to get a fully working environment, so here is what I did.
Continue reading “How to setup R using conda”