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I am a PhD Student in Applied Mathematics and Machine Learning at École Polytechnique. My research focuses on advancing the field of machine learning through mathematical foundations and innovative applications.

My research interests include:

• Applied Mathematics & Machine Learning - Developing theoretical frameworks and practical applications
• Bayesian & Probabilistic Learning - Quantifying and exploiting uncertainty in non-deterministic environments
• Machine & Deep Learning - Focusing on model-based algorithms and generative models (VAE, GAN, Diffusion, Flow)
• Optimization - Developing and applying optimization algorithms to solve complex problems

Prior to my PhD, I completed my MSc in Computer Science and Mathematics at Imperial College London, where I worked in the Adaptive & Intelligent Robotics Lab under the supervision of Dr Antoine Cully. My master’s thesis work is available in this GitHub repository and the complete thesis can be found here.

Research Focus

My current research at École Polytechnique combines rigorous mathematical foundations with practical machine learning applications. I am particularly interested in:

• Theoretical foundations of machine learning
• Advanced optimization techniques
• Applications in scientific computing and data analysis

Application Areas

My research has potential applications in various domains:

• Scientific Computing & Simulation
• Finance & Risk Analysis
• Control Systems & Robotics
• (Quantum) Physics
• Complex Systems Modeling

After my PhD, I will be looking to join the industry rather than stay in academia, so I am keenly interested in an industrial collaboration during this thesis.
This collaboration could take the form of condensed internship blocks, or distributed days of work each week to meet the company’s needs.

Here are some application sectors that interest me:

• Finance & Risk
• Supply Chain & Operations Management
• Neuroscience
• Control & Robotics
• (Quantum) Physics
• Video Games