I’m Timi! I work on building and developing AI and machine mearning applciations, often from the ground up. This has involved thinking of ways to build better RAG frameworks, building machine learning models for large, complex financial data, or orchestrating AI agents for traversing structured datasets. Academically, I also explored the intersection between AI safety and neural networks, and I wrote a thesis on the adversarial robustness of Spiking Neural Networks.
Research and Papers
Adversarial Robustness of Rate-Encoded Spiking Neural Networks
As part of my Master's thesis, I wrote a technical report studying the resistance to adversarial attacks of rate-encoded Spiking Neural Networks (SNNs) across various white-box and black-box attacks. This study explores the robustness of SNNs in different adversarial scenarios and proposes methods to enhance their security and reliability.
Modelling Heart Conditions and Train Delays Using Machine Learning Methods
Alastair Harrison, Timi Folaranmi, Ying Zhan, Weiyun Wu
The first study looks at the question of: To what extent can heart conditions be predicted from ECG readings? The second study looks at a machine learning approach to predicting train delays, and conducting all analysis in R.
An Exploration into Support Vector Machines (SVMs) with comparisons to other Classification Methods
Jake Dorman, Timi Folaranmi, Anas Almhmadi, Rishabh Agarwal
A technical report on the background, performance and evaluation of Support Vector Machines in solving general classification problems (in Python), compared with other classification methods.