I am a Machine Learning Research Scientist at Apple, within the Health AI team. I work on developing new deep learning algorithms that integrate formal domain expertise, such as that defined by scientific simulators, with real-world data to advance the design of novel sensing technologies for health. While I remain attached to fundamental research and the academic world--co-organizing ML for Science workshops at NeurIPS/ICML/ICLR and actively publishing-- my excitement has slowly drifted towards the industrial world, in particular to demonstrating the industrial value of scientific probabilistic modelling.

In the past years, both my applied and fundamental research has been focused on better understanding Photoplethysmography, often called PPG, and most commonly known has "the blinking LEDs on the back of smart watches". Indeed, while excitement is growing on applying ML on PPG, e.g., to detect hypertension, little is known about the underlying causes and source of nuisance of this signal. To address this knowledge gap, whose list of undesired consequences is long, I have been working on developing new hybrid learning algorithms and models that combine state-of-the-art biophysical knowledge (e.g., biophotonic and 1D hemodynamics) with generative models to better model, hence understand, photoplethysmography.

Before joining Apple full-time, I worked as a postdoctoral researcher at Apple from November 2022 to November 2023, advised by Jörn-Henrik Jacobsen (Health AI) and Marco Cuturi (ML Research). I investigated strategies for deriving robust simulation-based inference algorithms to address issues with misspecified simulators, particularly in their application to health technologies. Prior to joining Apple, I completed my PhD in Computer Science with an FNRS Research Fellowship, under the supervision of Professor Gilles Louppe at the University of Liège, Belgium, in October 2022.

I earned my M.Sc. in Computer Engineering from the University of Liège in 2018, spending my final year as an exchange student at the École Polytechnique Fédérale de Lausanne (EPFL). At EPFL, I conducted my master's thesis in Jean-Yves Le Boudec's lab, focusing on estimating parameters of electrical distribution networks.

My vision is to enhance the interplay between fundamental sciences and machine learning techniques, both to spur scientific discovery and to develop predictive models that can be reliably deployed in the real world. In pursuit of this goal, I actively work on expanding the fields of application of simulation-based inference methods by enhancing their robustness to model misspecification and improving their integration with real-world data.

My research interests are in deep probabilistic modeling, biophysical sensor design, and simulation-based inference.

If you feel your research agenda and mine could be a good match, feel free to reach out to me!

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