Past projects and testimonials

During this placement, my research focused on investigating how developers identify, understand, discuss, and fix security vulnerabilities in software systems - and how we can utilise modern AI to analyse these elements of vulnerability remediation.

During my summer research internship, I investigated how Large Language Models (LLMs) can be used to automate formal verification of C programs using the bounded model checker, CBMC.

During my CDT summer placement at the ANGLE Lab, I researched Zero-Knowledge Machine Learning (ZKML): a cutting-edge field at the intersection of theoretical cryptography and applied artificial intelligence.

During my internship, I explored the use of beta-VAEs (Variational AutoEncoders) as a tool for disentangling the latent representations of images. I knew I wanted to work in the latent space and that VAEs were a good tool for this task, but I didn’t know exactly how to apply it.

The project aimed to build a compiler pipeline in MLIR that automatically quantizes floating-point neural network models, eg. FP32 to INT8/INT4, and includes a testing framework to benchmark accuracy vs. performance tradeoffs.

In this project I explored whether separate retinal image datasets can collaboratively contribute to training LLM without pooling the raw patient images and data.