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Date
  • 13/04/2022 10:00 am
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Massimo Vergassola

Massimo Vergassola

ENS Paris/CNRS

Massimo Vergassola holds a joint position as Professor at the Ecole Normale Supérieure in Paris and CNRS Directeur de Recherche. He is the Director of the ENS-PSL QBio initiative on Quantitative Biology, which is selected to be part of the PariSanté Val-de-Grâce Campus. After his education in Italy and France and his postdoc at Princeton University, Vergassola received a tenured research position at the French CNRS for work on the statistical physics of fluids. His CNRS position was held with joint appointments at the Ecole Polytechnique, and at the Pasteur Institute as the head of the Physics of Biological Systems group. In 2013-19, Vergassola was a Professor at the University of California San Diego and a founding member of the Qbio initiative at the UCSD campus. Vergassola was visiting scientist at Rockefeller University, KITP, IAS, LANL, and IHES, he was a plenary speaker at Statphys25, and he served on the board/leadership of a variety of professional journals and associations. He was Chair of the Biological Physics Division of the American Physical Society. Vergassola’s awards include the Grand Prix EADS from the French Academy of Sciences, the Fellowship and the Outstanding Referee awards from the American Physical Society, the CNRS Bronze Medal, the Biomedical prize Thérèse Lebrasseur from the Fondation de France, the Accademia dei Lincei student award, and grant awards from the Simons Foundation and the Fondation Recherche Médicale.

Learning to navigate complex environments

Living systems face the challenge of navigating natural environments shaped by non-trivial physical mechanisms. Notable examples are provided by long-distance orientation using airborne olfactory cues transported by turbulent flow, the tracking of surface-bound trails of odor cues, and flight in the lowest layers of the atmosphere. Terrestrial animals, insects, and birds have evolved navigation strategies that accomplish the above tasks with an efficiency that is often surprising and yet unmatched by human technology. Indeed, robotic applications for olfactory sniffers and unmanned aerial vehicles face similar challenges for the automated location of explosives, chemical, and toxic leaks, as well as the monitoring of biodiversity, surveillance, disaster relief, cargo transport, and agriculture. The interdisciplinary interplay between biology, physics, and robotics is key to jointly advancing fundamental understanding and technology. I shall review the above natural phenomena, discuss the physics that constrains and shapes the navigation tasks, how machine-learning methods are brought to bear on those tasks, and conclude with the relevant strategies of behavior and open issues.