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Thomas Guyet

Research interests

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.

Rémi Emonet   

Journal articles1 document

  • Alice Aubert, Romain Tavenard, Rémi Emonet, Alban de Lavenne, Simon Malinowski, et al.. Clustering Flood Events from Water Quality Time-Series using Latent Dirichlet Allocation Model. Water Resources Research, American Geophysical Union, 2013, 49 (12), pp.8187-8199. ⟨10.1002/2013WR014086⟩. ⟨halshs-00906292⟩