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.