From a foundation in computer science to ongoing study in AI, statistics, and reasoning systems - Manchester, Bath, MIT and Oxford.
Degrees
Ph.D. in Computer Science & A.I.
2025 - present
The University of Manchester · UK
My research interests revolve around reinforcement learning and generative model reasoning. I am exploring ways to improve policy optimisation with policy gradients and expectation maximisation. I want to push the boundaries of world models and how they can support reasoning models (not limited to or even focused on LLM's, but also control, robotics, physics, gaming).
Dissertation on Dynamic Time-Warping for clustering market data to enhance investment strategies by identifying asynchronous market patterns.
B.Sc. (Hons) in Computer Science with Industrial Experience
2013 - 2017
The University of Manchester · UK
Dissertation on non-intrusive native JVM agents for capturing the internal state of live production applications during critical failures.
1-year, 2-team placement at Morgan Stanley, London, UK.
Continued education
MicroMasters in Statistics & Data Science
2025 - present
Massachusetts Institute of Technology · US · Remote
Deep-diving into statistics (Larry Wasserman) and probability (Bertsekas, Tsitsiklis) and their use in machine learning and data science - building understanding from the fundamental theory up.
Probability - The Science of Uncertainty and Data - MITx 6.431x
Fundamentals of Statistics - MITx 18.6501x
Data Analysis: Statistical Modelling and Computation in Applications - MITx 6.419x
Machine Learning with Python: from Linear Models to Deep Learning - MITx 6.86x
Capstone Exam in Statistics and Data Science - MITx DS.CFx
Artificial Intelligence Programme
2025
University of Oxford · Saïd Business School · UK
Took off the theoretical engineer hat and put on the real-world problem-solving hat, where ethics, policy and business goals take centre stage. Connected over six weeks with individuals from across the world, learning many new perspectives on artificial intelligence and its deployment at scale.
Artificial intelligence history and ecosystem
AI and machine learning: understanding the black box
Understanding deep learning and neural networks
Beyond prediction: making the most of generative AI
AI, ethics and society
Psychology: An Introduction
2025
University of Oxford · Dept. for Continuing Education · UK
