Léo Pio-Lopez has an interdisciplinary academic background. He received his B.Sc. in Mathematics and Computer Science applied to Biology from Claude-Bernard University in Lyon (France). He furthered his education by obtaining an M.Sc. in Cognitive Science from Grenoble Institute of Technology (France), as well as additional M.Sc. degrees in Systems Biology from EPHE, Paris (France), Artificial Intelligence from Claude-Bernard University in Lyon (France), and Political Science from Jean-Moulin University in Lyon (France). He earned a dual Ph.D. in Electronics and Systems from Blaise-Pascal University in Clermont-Ferrand (France), and Psychology and Cognitive Science from La Sapienza University in Rome (Italy).

Léo Pio-Lopez is a computational biologist, and Senior Scientist in the Allen Discovery Center at Tufts University. He earned a dual Ph.D. in Electronics and Systems from Blaise-Pascal University in Clermont-Ferrand (France), and Psychology and Cognitive Science from La Sapienza University in Rome (Italy) focusing on brain-inspired predictive control in artificial and biological systems. Then, he pursued a postdoc on the development of new machine learning methods for network medicine and drug discovery at the Institute of Mathematics, Marseille (France).

In 2021, he joined the Levin Lab, where his research now centers on three primary areas of life and cognition:

  • The evolution and basal cognition of morphogenetic systems. He investigates the computational properties governing the scaling of cognition via homeostasis and bioelectricity. He utilizes machine leaning, neural networks and evolutionary algorithms to elucidate the principles of collective and multi-scale computation leading to higher cognition.
  • The bioinformatics of xenobots, regeneration, cancer and aging. He collaborates closely with biologists, employing machine learning and data science to comprehend the genetic and bioelectrical dynamics underlying these different biological processes.
  • The development of new AI methods to biology and drug discovery and the integration of principles of biology in new AI architectures.

His research has the long-term goal to allow new capabilities in collective AI, regenerative medicine and aging interventions.