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Research activity of Axis 3 during the period 2019-2021
Research topics Axis 3 aims at quantifying biological structures in space and time from new-generation data at all scales (molecules, cells, tissues, organs) in order to understand and model biological phenomena. Data are heterogeneous, massive, high-dimensional, and embed complex correlations. Novel AI technologies are required to characterize normal and pathological biological functions and to understand the adaptation of organisms to their changing environment (computational biology). Conversely, insights from cognition offer exciting perspectives to propose new bio-inspired AI algorithms and architectures (bio-inspired AI).
3IA Chairs A total of 8 researchers have contributed to Axis 3: •
4 Chairs since Oct. 2019 (L. Blanc-Féraud, F. Cazals, P. Reynaud-Bouret, E. Van Obberghen-Schilling)
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2 Chairs since Sept. 1, 2021 (P. Barbry, B. Miramond)
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1 International Chair (D. Wales)
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1 Affiliate Chair (D. Rouquié) since 2020
International chair - since 2019
David Wales - University of Cambridge 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.
