CV

Supervised by Prof. Alessandra Russo and Dr. Dalal Alrajeh. Research focus on scalable formal verification using machine learning.

Specialised in Financial Mathematics. Thesis title: “On the Theory of Denoising Score Matching within Score-Based Diffusion Models”

Delivered undergraduate computing course lectures and developed coursework focused on SMT solving using Z3Py. Responsible for exam design, student communication, and ensuring high-quality educational outcomes.

6 month internship developing scalable automatic labeling methods with large language models (LLMs) and vision-language models (VLMs) for improving applications like search and content moderation. Generated large, high-quality image-text dataset for evaluating search retrieval performance. Learned how to perform LLM inference on AWS Bedrock and locally. Applied my work to tackle real-world problems; for improving the search experience across Epic’s asset sharing platforms.

Supervised undergraduate thesis project on “Neurosymbolic learning of explainable commonsense knowledge using LLMs.” Provided technical guidance and ensured timely delivery of high-quality research outcomes combining symbolic reasoning with modern AI approaches.

For Computational Techniques & Symbolic Reasoning. Developed and deployed an automated grading system for computational coursework. Resolved technical implementation issues and successfully launched the platform, improving assessment efficiency and student feedback processes.

Created statistical models for predicting laundry powder properties and developed specialised image analysis tools for MicroCT powder imaging using ImageJ. Applied data science techniques to solve manufacturing and quality control challenges.

Research group led by Professor Alessandra Russo.