I have studied psychology at the university of Tours (France) and Bucknell (USA). In 2016, I then pursued a PhD supervised by Danilo Bzdok (RWTH Aachen university, Germany) and Danielle Basset (UPENN, USA) and supported by a PhD scholarship of the International Research Training Group (DFG-IRTG 2150). I worked on the default mode network in schizophrenia. We exploited novel machine-learning techniques to complement previous researches in the field. Our methodology further enabled quantifying the structure-function correspondence by analogous analyses on resting-state connectivity fluctuations and brain volume variability. As a second PhD project, we opted for a paradigm that would improve clinical workflows since personalized medicine is an emerging agenda in psychiatry. We decided to deploy a comprehensive analytic strategy that emphasized prediction performance and direct clinical relevance using the PANSS, the most widely used questionnaire to assess schizophrenia symptoms severity.
During my PhD, I received intensive training in machine learning and brain imaging working in collaboration with Gaël Varoquaux (INRIA, Paris, France) and Simon Eickhoff (Düsseldorf, Germany). Being supervised by academics from very different fields (medicine, computer science, and physics) really made clear that I have a deep interest for combination of rigorous methodology and innovating approach to investigate psychopathology. I defended a PhD thesis in computational neuroscience in 2019. Currently working as a postdoc in Chi-Chun Lee's lab in Hsinchu (Taiwan), we are working on learning predictive models based on behaviors, brain-region and brain-network priors using machine-learning tools such as semi-supervision and structured sparsity penalties.
Check out my publications here