The longer version

I was born in Germany of American, German and French parents. We soon moved to France, and then to Cambridge in the U.K. from the age of 3 to 5. After that I spent the rest of my childhood in the western most tip of France, in Brittany. There, I attended the Lycée Amiral Ronarc'h, were I obtained a scientific baccalauréat with honors ("mention très bien"). This international upbringing allowed me to learn three languages (French, English and German) that I still speak fluently to this day.

My studies then led me to the lycée François René de Chateaubriand in Rennes for the next two years of my life. There, I had a focus on math, physics and computer science (MPSI->MP*). At the end of these preparatory classes, I passed the entrance exam for the Ecole Normale Supérieure de Lyon, where I finished my Bachelor's degree, and am currently pursuing a Master's in fundamental computer science.

During my studies, and my internships, I developed a particular interest towards the field of machine learning. I had the opportunity to discover many of its aspects: Deep learning, Transfer learning, Metric learning and even some Quantum machine learning, which were all fascinating. In my personal life I have a strong attachment to traveling, which comes in part from my multi-national origins. I love discovering new cultures and landscapes, and believe it also makes me a more tolerant and open-minded person.

Deep learning for brain imaging

I did a six week internship at the Institut Mines-Télécom Atlantique were I developed Autoencoder networks to transform one modality (type) of MRI images into another.

Quantum algorithmic

I went to Singapore for three months, to do an internship at the Center for Quantum Technologies. There, I worked on quantum algorithms, and even devised a new one for a variation of the Subset-Sum problem called Pigeonhole Subset-Sum.

Imbalanced metric learning

I spent four months at the Université Jean Monnet in Saint-Etienne, working on a way to combine metric learning with the famous supersampling algorithm SMOTE, used to handle the class imbalance problem.

Transfer learning

During a 5-month interniship at Worline in Lille, I used novel transfer learning techniques to leverage credit card fraud data on smarthpone e-payment fraud detection. The report is not available due to the confidentiality of the data.

Scalable reader-writer locks

I spent four months in Switzerland, at the University of Neuchâtel. There, I studied the scalability of reader-writer locks, and designed new ones balancing the requirements of phase-fairness and massiviley parrallel access.