Hey, I’m Matthew.
I help practitioners and researchers make fast, data-informed decisions in the face of uncertainty.
Specialisations
I combine artificial intelligence, Bayesian inference, and advanced computing to deliver robust, high-performance solutions.
Data-Driven Decision Systems
Build intelligent, scalable pipelines that transform raw data into actionable insights.
Capturing Uncertainty
Quantify and communicate confidence using Bayesian models and probabilistic inference.
Fast Decisions
Leverage race-tuned algorithms on GPUs, TPUs, and distributed clusters for near real-time results.
About
I am a researcher in the Signal Processing Group at the University of Liverpool, passionate about enabling practitioners and researchers to make fast, data-informed decisions in the face of uncertainty. My research interests span artificial intelligence, Bayesian inference, and high-performance and distributed computing, with a focus on combining these fields to create powerful, efficient decision-making systems.
I completed my undergraduate degree in Business Economics and Financial Management at the University of Hull in 2018. I then earned my MSc in Big Data and High-Performance Computing from the University of Liverpool in 2019. In 2025, I achieved my PhD in Electrical Engineering and Electronics at the University of Liverpool, where my thesis focused on developing advanced numerical Bayesian inference techniques that capitalised on high-performance and distributed computing environments.
In my spare time, I enjoy playing instruments including the clarinet, saxophone, and guitar. One of my favourite pieces to play is Si Tu Vois Ma Mère by Sidney Bechet; I love it for the emotive, almost voice-like quality of the clarinet that speaks without words. When I'm not playing, I enjoy taking photographs and writing poetry. You can see some of my creative work on Instagram. I am also an avid gamer, regularly playing Vermintide and Counter-Strike.
Recent Publications
Opportunistic computing for real-world problems
Matthew Carter · CDT in Distributed Algorithms · Case Study · April 2024
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject
Lee Devlin, Matthew Carter, Peter Green, Paul Horridge, Simon Maskell · IEEE Signal Processing Letters · Journal Paper · April 2024
Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models
Conor Rosato, Robert Moore, Matthew Carter, John Heap, John Harris, Jose Stropoli, Simon Maskell · Information MDPI · Conference Paper · January 2023
Recent Writing
Modelling Right-Censored Data in Stan with CmdStanPy
A hands-on introduction to Bayesian inference using Stan to model patient wait times in a clinic. By fitting a Gamma distribution with right-censoring, we capture uncertainty in both model parameters and unseen outcomes, showcasing how Bayesian methods naturally handle incomplete data where traditional tools fall short.
From Priors to Predictions: An Intuitive Introduction to Bayesian Inference
An intuitive introduction to Bayesian inference, from Bayes' theorem and likelihoods to hands-on modelling with Gamma-distributed wait times. We show how Bayesian methods estimate full distributions over parameters, quantify uncertainty, and enable probabilistic predictions, wrapping up with a practical example using Stan.