Chairs | AI for Computational Biology and Bio-inspired AI

• Neuronal level: spiking models to better understand neuronal dynamics 
• Cognition: neuronal dynamics for the analysis of learning/perception/action sequences 
• Simulation/electronics: brain models to provide new neuromorphic-biomimetic algorithms/architectures 

Solution landscapes for Machine Learning

We explore machine learning landscapes in the cost function parameter space, which isanalogous to the potential energy surface of a molecule as a function of atomic coordinates. Ongoing advances in methodology developed in chemical physics, can therefore be immediately applied to ML solution landscapes.

Our objectives are to use these tools to design improved predictions, and apply them to problems in molecular science and health care. In particular, we seek improved machine learning tools for clinician diagnostic support, to provide earlier detection of the deteriorating (and improving) patient. Specific applications include prediction of readmission to intensive care, which represent a failure in down-transfer to the ward, and are often associated with patient mortality.

Human Lung Atlas (2020-2024)

The project elaborates on state-of-the-art approaches in genomics and cell biology to describe complex biological samples at the single-cell resolution. Multidimensional biological experiments result in large scale descriptions of DNA, RNA and protein expressions that can be integrated in time and space. The project aims at: (1) developing novel data-mining approaches based on machine learning and AI; (2) apply them to the study of the normal and pathological lung, in the context of serious threats that touch this organ (COVID-19, asthma, cystic fibrosis, cancer,...).

Imaging for biology (2019-2023 / 2023-2027)

Recent advances in microscope technology provide outstanding images that allow biologists to address fundamental questions. This project aims at developing new AI methods and algorithms for (i) novel acquisition setups for super resolution imaging, and (ii) extraction of valuable quantitative information from these large heterogeneous datasets. 

AIMS: Artificial intelligence for molecular studies (2019-2023 / 2023-2027)

By learning essential features of proteins and their complexes, we shall deliver biologically relevant information for large molecular systems on biologically relevant time scales, leveraging our understanding of biological functions at the atomic level, and providing key inputs for protein design and engineering, and protein interaction networks. 

Bio inspired AI from neurosciences to embedded autonomous devices (2020-2024)

The research project seeks to draw on the structure and function of the biological brain to develop more energy-efficient AI methods and algorithms. 
The scientific approach ranges from neural dynamics to the emerging cognitive properties of these networks and ultimately to the design of embedded neuromorphic electronic circuits. 
The project will focus on building bridges between the NeuroMod neuroscience institute and the 3IA Cote d'Azur institute.