Interests

My research lies at the intersection of statistical machine learning and Bayesian deep learning. I focus on advancing traditional Bayesian frameworks by integrating them with modern deep learning techniques, aiming to develop scalable, interpretable, and robust models for complex, real-world data.

Short bio

I earned my BSc in Economics and Statistics from the University of Geneva in 2018, graduating top of my class among over 100 students and receiving the award for the highest overall grade average. I then completed an MSc in Statistics with Distinction at Imperial College London in 2019, supported by the Department of Mathematics Scholarship for the MSc in Statistics.

In February 2023, I obtained my PhD in Modern Statistics and Statistical Machine Learning from Imperial College London, under the supervision of Dr. Oliver Ratmann and Prof. Samir Bhatt. My doctoral research focused on Bayesian methodology, with a particular emphasis on estimating age-specific infectious disease transmission dynamics and developing scalable non-parametric Bayesian models. My thesis was selected as a finalist for the ISBA Savage Award, recognizing outstanding contributions in Bayesian analysis.

After completing my PhD, I joined Novartis in January 2023 as a Principal Biostatistician in the Advanced Methodology & Data Science (AMDS) team. In this role, I led the development and application of advanced statistical methods for pharmaceutical research, including the use of medical imaging and deep learning for survival prediction.

Since October 2024, I have also been working as a Research Associate in Machine Learning at Imperial College London, continuing my research at the intersection of Bayesian statistics, machine learning, and real-world applications.