I am a senior research associate in statistics at the department of Mathematics and Statistics at Lancaster University.
I currently work with Dr. Chris Sherlock on non-reversible Markov chain Monte Carlo algorithms.
My research concentrates on developing algorithms for Monte Carlo methods and statistical analysis for network data.
I am interested in computer programming for scientific research in C++, R and julia.
Networks are abundant in day to day life, from social networks to transportation.
The connected nature of networks leads to statistical challenges: the classic assumption of data independence no longer holds.
Classically, networks with only on/off connections have been considered.
My research in this area has focused on a Stochastic Block Model where connections a more general.
I have considered two main problems: determining the number of groups in a network and detecting changes in group membership.
In Bayesian statistics, lots of problems involving computing an integral.
For lots of interesting models, these integrals cannot be solved by hand, but can be approximated by sampling from the model and computing averages.
In complex models, even sampling can be difficult.
Markov chain Monte Carlo algorithms are a class of procedures that allow one to draw samples from any model.
I am interested in developing novel and more efficient MCMC algorithms.
Most Markov chain Monte Carlo algorithms involve some parameters that the user is free to choose.
These are often critical to the performance of the procedure, with poor chooses leading to higher variance estimates.
I am interested in theoretical and methodological ways to automate the choice of parameters for Hamiltonian Monte Carlo algorithms to alleviate this burden of choice.
Practitioners in statistics rely heavily on software for data analysis.
Modern research in statistics, especially methodology, produces a lot of code to demonstrate its worth.
Reproducability of research involving software is a big issue which I have been exploring, involving writing legible, interpretable code; teaching others on good practises in software; and version control.
Ludkin, M., Sherlock, C. (2019). arXiv pre-print
Matthew Ludkin, (2020). Computational Statistics & Data Analysis.
Ludkin, M (2020). arXiv pre-print
Ludkin, M., Eckley, I. and Neal, P. (2018). Statistics and Computing, 28, 1201-1213.
A PDF version of my thesis is available via the Lancaster research portal
This award provided for a two week research trip to Paris, France for continued collaboration with Sté phane Robin (agrosParisTech) and Catherine Matias (UPMC).
This award provided funding to establish a research project titled "Fast Approximate Inference for Changes in Network Structure" in collaboration with Stéphane Robin (agrosParisTech) and Catherine Matias (UPMC).
Contributed talk at An afternoon of Bayesian Computation. Slides available soon
Contributed poster at ISBA world meeting conference. Poster available soon
Invited alumni speaker for the STOR-i annual conference 2018.
Emergent and Self Adaptive Systems workshop, DSI, Lancaster University.
Challenges in Industry and Society Open for Business Event at Isaac Newton Institute, Cambridge.
Analysis of nonstationary multivariate time series Workshop at Lancaster University.
Invited seminar speaker at Laboratoire de Biometrie et Biologie Evolutive at the University Claude Bernard, Lyon, France
Invited seminar speaker at INRA/MIA at AgrosParisTech, Paris, France
Distinction - Lancaster University
1st Class with Honours - University of Birmingham
University of Birmingham
University of Birmingham
University of Birmingham
I feel very privileged to have been granted this scholarship, which helped me greatly in my studies during my time at Birmingham.
If you are interested in my research or just want to get in touch.