CV

University of Cambridge

2020 – 2023

BA Hons Computer Science: Grade 2.1/71%

Dissertation: “Investigating the effect of UDP and QUIC as transport protocols for the MQTT Protocol”

• Developed a multi-threaded Publish-Subscribe communication protocol and benchmarked transport layer latency and throughput, directly analysing the trade-offs between reliable (QUIC) and unreliable (UDP) data transmission in network-constrained environments.
• Dissertation received the highest grade in the year (91%) and a CST Department Prize.

Galatea Associates

September 2023 – September 2025 — Software Consultant

• Engineered high-throughput distributed systems, utilizing reactive programming patterns to handle high-volume concurrent requests with minimal latency.
• Modernised deployment of database changes on a Cockroach database by implementing automated Liquibase deployments in Jenkins with Kubernetes authentication, saving a minimum of 30 minutes of human intervention time during every release.
• Diagnosed and resolved resource contention bottlenecks in persistence layers by implementing advanced connection pooling, reducing database connection failures by 95% and improving system throughput.
• Implemented an API rate limiting solution using Hazelcast distributed caching, which was adopted amongst the wider team due to its low latency.
• Improved system observability by implementing comprehensive Datadog monitoring in Kubernetes environments, enabling real-time performance tracking and faster incident response.
• Collaborated across three development teams to refactor legacy codebases to enforce interface segregation and decouple core logic, resulting in improved code maintainability and reduced technical debt.

AUK Trading

July 2022 – September 2022 — Junior Engineer

• Collaborated closely with the company CEO and CTO to improve the existing data analysis platform and create new ones.
• Leveraged data analysis tools and methods to provide a robust system to generate reliable and descriptive statistics regarding trading performance.
• Automated daily trading performance reporting for shareholder distribution, reducing manual data compilation time by 80%.

University of Cambridge

July 2021 – September 2021 — Machine Learning Research Intern

• Researched adversarial attacks and security flaws in Brain-Computer Interfaces (BCI), utilizing Pandas and NumPy to gather and clean training data from diverse sources.
• Used Python and the machine-learning library PyTorch to train and test various machine-learning models.
• Implemented and evaluated various techniques for generating adversarial examples for machine-learning models.
• Submitted a paper as an author, “Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces”, to MobiSys 2022.