Ph.D. in Computer Science and A.I.

The University of Manchester, UK, 2025 - present

Artificial Intelligence, Computer Science, LLM Reasoning.

B.Sc. (Hons) in Computer Science ^{with Industrial Experience}

The University of Manchester, UK, 2013 - 2017

• Dissertation on non-intrusive native JVM agents for capturing the internal state of live production applications during critical failures.
• 1-year 2-teams placement at Morgan Stanley, London, UK.

M.Sc. in Business Analytics

University of Bath, UK, 2020 - 2023

Dissertation on Dynamic Time-Warping for clustering market data to enhance investment strategies by identifying asynchronous market patterns.

Micromasters in Statistics and Data Science

Massachusetts Institute of Technology, US-Remote, 2025 - PRESENT

As nothing beats a structured and committed approach to learning, deep-diving into statistics (Larry Wasserman) and probabilities (Bertsekas, Tsitsiklis), and their use in machine learning and data science, from the fundamental theory, building and sedimenting understanding from the bottom 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

University of Oxford, Saïd Business School, UK, 2025

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, at different levels of seniority and at diverse points in their lives, which taught me so many new perspectives on the problem of 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

University of Oxford, Department for Continuing Education, UK, 2025

Agentically-Orchestrated Foundational Models for Cloud Architecture Development

US20250343728 - Application ID: 18651871

1. User response information is obtained comprising information indicative of cloud architecture requirements for a cloud architecture to fulfil.
2. Based on the user response information, a plurality of agentic orchestration models are used to generate a respective plurality of role outputs, each of the plurality of agentic orchestration models comprising a machine-learned language model prompted to fulfil a corresponding cloud architecting role of a plurality of cloud architecting roles, wherein one of the plurality of role outputs is indicative of a plurality of proposed generic component placeholders for components necessary to meet the cloud architecture requirements.
3. Based on the plurality of role outputs, a proposed architecture output is generated comprising a visual representation of the proposed generic component placeholders.