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Deep learning driven model discovery in biology and physics, a sciences in context conference

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Deep learning driven model discovery in biology and physics, a sciences in context conference

Lecture by Remy Kusters,

Remy Kusters is long term research fellow at CRI and group leader of the Physics Inspired Machine Learning (PhIMaL) team. With a background in theoretical biophysics and soft matter physics, his objective is to leverage the increased power of data science and in particular machine learning to discover physical models form experimental data.

PRESENTATION

Sciences in Context is a series of public lectures organized in collaboration with the Institut d'études avancées de Paris by Muriel Mambrini and Pascal Kolbe, aimed at bringing new concepts and perspectives from the frontiers of the social sciences to the CRI community and beyond. The topics of the conference is prepared at a public session of the Practical Philosophy Club on the Friday before each conference, in order to encourage discussion with the guest speaker.

SUBJECT OF THE CONFERENCE

As scientific data sets become richer and increasingly complex, machine learning (ML) tools become more useful and widely applied. Discovering a mechanistic model, rather than predicting the outcome is paramount in the scientific endeavor and its lack in present day ML is limiting further integration of ML in quantitative science. In this talk I will present our development of quantitative tools to extract human interpretable models from quantitative biological and physical data sets. The work combines the predictive power of neural networks with the interpretability of symbolic regression to develop a framework of interpretable AI and discover mechanistic models from biological and physical data.

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