I am an full researcher at the Inria Center of Lyon, since 2021. I am hosted currently hosted in Beagle team and working within the AIStroSight project.
I am currently co-holder of an Inria/APHP chair AIRACLES on high-dimensional data for care pathways analysis.
From 2007 to 2021, I was assistant professor at Institut Agro -- AGROCAMPUS OUEST and was working in the IRISA laboratory (LACODAM Team) in Rennes, responsible of the Health and Biology transverse research theme.
In 2020, I defended my habilitation "Enhancing sequential pattern mining with time and reasoning". I received my PhD thesis in Computer Science in 2007 from National Polytechnic Institute of Grenoble. During my Phd ("Collaborative interpretation of time series. Application to intensive care data."), I worked in the Grenoble Informatic Laboratory (LIG) in a wide range of the artificial intelligence domain (cognitive science, data mining and applications) and in the TIMC Laboratory in a team that works in the applied field of cardio-respiratory physiology (PRETA).
My research area is artificial intelligence (AI) with a multidisciplinary approach - algorithmic, design methodologies and cognitive science. I am particularly interested in discovering spatial and temporal patterns in semantically rich datasets. My areas of application are related to agronomy (mainly landscapes) and health (care pathways analysis).
My first research direction is the temporal and spatial pattern mining. Data from the observation of living systems (agricultural and medical systems) have a strong spatial or temporal dimension. But the spatial and temporal information is often underutilized in the data mining algorithms. The challenge lies in identifying new kind of temporal/spatial patterns that have valuable properties to make possible their extraction by complete and correct algorithms. A recent approach I'm developping is the use declarative programming, more especially Answer Set Programming (ASP) with clingo, to mix pattern mining and reasonning.
My second research direction aims at better including the user in the loop of knowledge discovery. Specifically, I am interested in implementing interactive systems to support users in their exploration process of large datasets. To acheive this goal I propose an enactive point of view of the data interpretation that brings creative solutions to take into account the cognitive ergonomy of the knowledge discovery tools.
Current projects in which I am strongly involved in