My Story

I'm from Vincennes, a small town right outside Paris. Both my parents are physicians, so I grew up around medicine. Patients and diagnoses were constant table talk. I played competitive tennis through most of my childhood and spent a lot of time doing maths. Both shaped how I think.

When smartphones came out I got hooked on technology. My first research project was an attempt to hack Touch ID: lifting fingerprints off glass and casting silicone moulds to get past Apple's biometric security. I started photography young too, drawn to observing what was around me. I never stopped.

At sixteen I left for a year as an exchange student at Arlington High School in Massachusetts. My English was shaky and I knew nobody, so I had to work everything out on my own. It was a step in the dark. The American system turned out to be nothing like what I knew: I could pick the classes I cared about, so I took AP sciences, American history and photography. In physics we built motor cars and paper bridges instead of solving textbook problems. I went to football games on Friday nights and joined the school tennis team. I came back knowing I wanted to study in the US.

The five years I spent at the University of Chicago were the most formative of my life. B.S. and M.S. in Computational and Applied Mathematics, B.A. in Statistics. Hard classes, short nights, and people who pushed me past what I thought I could do. I loved it.

I started working early: teaching assistant, grader, intern every summer. My coursework pulled me deeper into data science and statistical learning. During my master's I did research at the SAND Lab on adversarial machine learning and biometric security, which led to a publication at CHI 2021. Earlier I had worked on 3D signal processing applied to marine biology. By the end I knew I wanted to apply computational methods to real problems.

Growing up around physicians, then studying applied maths and doing biomedical research, clinical AI was where it all converged. After graduating I joined Anumana in Boston, one of the leading cardiovascular AI companies, spun out of Mayo Clinic. The work covers a wide range of cardiac conditions, from common ones like atherosclerotic disease, heart failure and reduced ejection fraction to rare ones like cardiac amyloidosis, aortic stenosis and long COVID complications. All of them detected from a standard 12-lead ECG using deep learning.

Across two companies I worked on major pharma partnerships with Pfizer, Novartis and AstraZeneca, and with clinical teams at Mayo Clinic and hospitals across Europe. At Anumana, the Pfizer collaboration on amyloidosis detection led to an FDA Breakthrough Device Designation. I built coronary disease risk models with Novartis from multi-site hospital data, and developed NLP pipelines deployed alongside a Mayo Clinic clinical trial. At Idoven in Madrid I led data science on the AstraZeneca partnership, validating models across European and US hospital sites. Every project meant different data, a different clinical question and a different regulatory path. The through-line never changed: building algorithms that help detect disease earlier.

The details are on the CV page.

The competitive tennis turned into distance running. A first marathon in Chicago with no real plan, then gradually getting serious about it. The photography turned into film cameras and travel. Both stuck.