Adi Livnat

Adi Livnat

Associate Professor


Phone: +972-4-6647990

Office location: Beit Sala, room 104



Research Interests:

We study the fundamental question of how mutational and recombinational mechanisms bear on the process of evolution from both a theoretical and an experimental perspective. We employ diverse techniques, from mathematical modeling and computer simulations to cutting-edge experimental methods. Our goal is to obtain insight and evidence from converging directions on the degree and importance of genetic influences on probabilities of mutation and recombination, as well as on the relationship between mutational mechanisms and the role of sexual reproduction in evolution, also known as the “queen of problems in evolutionary biology.” In pursuing this main goal, we maintain an interest in a broad range of evolutionary questions.


Specific research interests:

  1. Mutational mechanisms and their role in evolution
  2. The role of sexual reproduction in evolution
  3. Research at the interface of evolutionary biology and theoretical computer science


Selected publications:

  1. Livnat, A. Simplification, innateness, and the absorption of meaning from context: How novelty arises from gradual network evolution. Evolutionary Biology, 44:145-189, 2017.
  2. Livnat, A. and Papadimitriou, C. Evolution and learning: Used together, fused together: A response to Watson and Szathmary. Trends in Ecology and Evolution, 31:894-896, 2016.
  3. Livnat, A. and Papadimitriou, C. Sex as an Algorithm: The Theory of Evolution Under the Lens of Computation. Communications of the ACM, 59(11):84-93, 2016.
  4. Chastain, E., Livnat, A., Papadimitriou, C., and Vazirani, U. Algorithms, games and evolution. Proceedings of the National Academy of Sciences, USA 111:10620-10623, 2014.
  5. Livnat, A. Interaction-based evolution: How natural selection and nonrandom mutation work together. Biology Direct 8:24, 2013.
  6. Livnat, A., Papadimitriou, C., Dushoff, J. and Feldman, M. W. A mixability theory for the role of sex in evolution. Proceedings of the National Academy of Sciences, USA 105:19803-19808, 2008.
  7. Livnat, A. and Pippenger, N. An optimal brain can be composed of conflicting agents. Proceedings of the National Academy of Sciences USA 103:3198-3202, 2006.