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PhD in Statistics and Machine Learning

• Under the supervision of Dean Bodenham, Ed Cohen, and Niall Adams within the Department of Mathematics.
• Statistical and machine learning methods for changepoint, distribution shift, and anomaly detection in high-dimensional sequential data, with broader interests in online learning, time series, self-supervised learning, and distribution-free methods.

MSc in Statistics

• Obtained with Distinction, 82%.
• Computational Statistics, Statistical Inference, Probability, Linear Models, Machine Learning, Deep Learning, Stochastic Processes, Bayesian Statistics, Stochastic Calculus.

MSc in General Engineering ("Ingénieur Généraliste")

• GPA 3.8/4.0.
• Linear Algebra, Applied & Numerical Analysis, Optimisation, Probability, Statistics, Data Structures & Algorithms, Machine Learning, Quantum Physics, Chemistry, Fluid Dynamics.

Applied Science PhD Intern

• Under the supervision of Jorge Tavares (Principal Researcher) within the MSAI research group.
• Researched generative retrieval to unify indexing and retrieval in one model, designing and training decoder-only large language models through multi-stage training and knowledge distillation.

Data Scientist

• Developed statistical and machine learning models for large-scale advertising, combining representation learning, language models, geolocation embeddings, and A/B experimentation to improve targeting and behavioural modelling.

Machine Learning Intern

• Developed statistical and machine learning models for customer segmentation and conversion probability estimation, with applications to personalised content delivery.

• Awarded the selective PhD Roth Scholarship by the Department of Mathematics, Imperial College London.

• Awarded first prize at the Finance & Innovation for Good competition (Google Cloud, HSBC, Sia Partners).

• Awarded second prize for best first-year research project at École Centrale de Lyon (Prix Francis Leboeuf).