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Ph.D. Computer Science

Imperial College London

Safe and Trusted Artificial Intelligence

M.Eng.

Imperial College London

Aeronautical Engineering

Machine Learning Research Engineer

Safe Intelligence Ltd.

Leading the robustness verification and learning initiatives for decision tree ensembles and tabular data

• Developed a novel gradient boosting algorithm with improved robustness.
• Created robustness evaluation methods for regression models using MILP solver (Gurobi).
• Defined robustness specifications for tabular financial data, ensuring reliability and compliance for automated loan approval models.

Product Engineer – NLP

Pangaea Data Ltd.

Led a cross-functional NLP and Product team to develop an ML pipeline for automated diagnosis of rare diseases.

• Applied state-of-the-art NLP methods (transformer-based models, semantic similarity, topic modelling) and deployed ML solutions at scale on Microsoft Azure to identify at-risk patients for a clinical trial with a major pharmaceutical partner.
• Collaborated closely with clinicians and stakeholders to iteratively refine research objectives, validate clinical relevance, and ensure robust empirical evaluation of model performance.
• Authored technical proposals, research grant submissions, and internal white papers that integrated recent research findings, bridging the gap between cutting-edge academic literature and industry applications.
• Represented the company at the Accenture FinTech Innovation Lab 2022, demonstrating domain-transferable ML techniques and securing a proof-of-concept in financial risk assessment.

ICAV Project

• Directed a 25-member team to design an autonomous UAV for the iMechE UAS Challenge, earning commendations for Best Design and Safety & Airworthiness.
• Led mechanical design and integrated UAV electronics.

OpenVaccine Challenge

• Developed a deep sequence-to-sequence model to predict COVID-19 mRNA vaccine degradation.