A world of opportunity 👋

I apply machine learning methods to solve complex challenges in biomedicine and physical systems.

Currently working at Keysight Technologies as an ML researcher, I focus on developing graph-based world models. My background includes a blend of ML modelling & bioinformatics.

Some highlights of the last years:

• Innovating at Keysight Technologies: In my first year, I submitted 3 IP disclosures focusing on graph-based architectures and explainability of world models for physical systems.
• Contributed to DeepChem integrating a protein language model to predict binding sites as part of the Google Summer of Code fellowship.
• Completed my PhD (yay!), focusing on statistical, ML, and graph representation learning methods to identify blood-based molecular markers for Parkinson’s disease diagnosis and prognosis. Feature selection and engineering, confounding factors & explainable AI. Check it out here!
• During my PhD I got a Fondation de Luxembourg’s fellowship to visit Prof. Pietro Liò (Cambridge University) and Petar Veličković (Google DeepMind). I focused on graph representation learning models for omics data.
• While at Cambridge, racing with the triathlon club the Varsity duathlon against Oxford - quite an experience! Obviously, we won
• Fellow of the 2023 MIT Catalyst program, exploring unmet needs in health(-tech)
• Proud global shaper recognized by the World Economic Forum of Geneva, to make the world a better place.
• Discovered the joy indulging in a good book with the occasional slice of carrot cake or a glass of white wine.
• Some people read my posts on both technical and non technical stuff.

Get in touch if you’re tackling complex ML problems. I’m available for consulting and collaboration 🤗

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

Download my resumé.