Welcome to my corner of the internet! 👋

I’m interested in applying machine learning methods to solve challenges in the biomedical realm.

My background includes a blend of machine (and deep) learning modelling & bioinformatics. I currently work at the University of Luxembourg, where I extend my PhD work on graph representation learning for omics data. Additionally, I’m a Google summer of code ‘24 contributor on the deepchem library, where I integrate protein language models for predicting binding sites.

I recently graduated from my doctoral studies (yay!), in which I focused on statistical and ML methods to identify blood-based molecular markers for Parkinson’s disease diagnosis and prognosis. One of the highlights of my PhD was being awarded Fondation de Luxembourg’s fellowship to do a research visit in Cambridge, where I focused on exciting graph representation learning models (aka graph ML or geometric DL) with Pietro Liò (Cambridge Univeristy) and Petar Veličković (Google DeepMind).

Beyond research, I’m a fellow of the 2023 MIT Catalyst fellowhip program, where I explore the space of un-met needs in health(-tech) and I proudly serve as a global shaper recognized by the World Economic Forum of Geneva.

At work and in life, I thrive in collaborative, engaging atmospheres and enjoy sports, from padel tennis to running, with a special fondness for outdoor activities. While at Cambridge, I raced with the triathlon club the Varsity duathlon against Oxford - quite an experience! In my downtime, I enjoy indulging in a good book and the occasional slice of carrot cake with a glass of white wine.

Now that you know me a bit better, please don’t hesitate to get in touch if you’d like to chat about any of the above (or anything in techbio) or if you’re interested in collaborating on a project. I’m always open to new opportunities and connections 🤗

Disclaimer: this site is currently under (active) construction!

Download my resumé.