One important idea is that science is a means whereby learning is achieved, not by mere theoretical speculation on the one hand, nor by the undirected accumulation of practical facts on the other, but rather by a motivated iteration between theory and practice. Georges EP Box
Since November 2022, I have been a postdoctoral researcher in Machine Learning at Apple under the advisory
of Jörn-Henrik Jacobsen (Health AI) and Marco Cuturi (ML Research).
I explore strategies to derive robust simulation-based inference algorithms for
misspecified simulators and their applications to health technologies.
Before joining Apple, I obtained my PhD (FNRS Research Fellowship) in computer science under the supervision of Professor Gilles Louppe (Uliège - Belgium) in October 2022.
During the summer 2021, I interned at Amazon Web Services where I worked on automated code analysis.
In 2018, I was graduated in computer engineering (M. Sc.) from ULiège. I spent my last year of study at Ecole
Polytechnique Fédérale de Lausanne (EPFL) as an exchange student. There, I
did my master's thesis in Jean-Yves Le Boudec's laboratory,
which was about the parameters estimation of electrical distribution networks' lines.
In my dreams, I want to boost the interaction between fundamental sciences and machine learning methods
with the objective of making scientific discovery
but also for building predictive models that can be deployed more reliably in the real world.
When I wake up, I try to advance simulation-based inference by exploring new means for implementing more
effectively inductive bias into deep generative models.
My main research interests are in deep probabilistic modeling, causal models and simulation-based inference.
I am actively looking for permanent research positions both in academia and industry as my post-doctoral contract with Apple expires in November 2023.
If you feel your research agenda and mine could be a good match, feel free to reach out to me!
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