Professor at University Paris Diderot (since 2017) and part time professor at Ecole normale supérieure (since 2019). Previously associate professor at Ecole polytechnique (2012-2017).
Topics of interest
Statistical learning theory, online learning, optimization for machine learning and applications of machine learning in healthcare
Project in Prairie
« PERHAPS: deeP learning for ElectRonic HeAlth records and applications in ProStatic pathologies ». The aim is to exploit a set of electronic health records of patients with prostatic problems in order to improve heath care of such medical conditions. An example is deep learning models that predict longterm complications of surgery based on the full health care pathways of patients prior to it.
Recent successes in machine learning and deep learning applied to health are mostly concerned with computer vision (medical imaging) and biological signals (such as ECG data). A challenge is with huge electronic health records (such as accounting databases that contain codes for diagnoses, drug prescription and medical acts) and a combination of such databases with clinical data. The use of deep learning models for such huge databases that contains indirect clinical signals is an important challenge, that this chair will try to address.