Dr Pierre Haas



  • 2017-          : Nevile Research Fellow in Applied Mathematics, Magdalene College, Cambridge
  • 2016-2017 : EPSRC Doctoral Prize Fellow, DAMTP, University of Cambridge
  • 2013-2016 : PhD Student, DAMTP, University of Cambridge
  • 2009-2013 : Mathematical Tripos (BA & MMath), Gonville & Caius College, Cambridge


Pierre is a member of the Biological Physics research group in the Department of Applied Mathematics and Theoretical Physics. His research is on the mathematics of morphogensis: how does shape arise in physical systems, living or otherwise?.

Inversion in Volvox

In particular, Pierre is interested gaining a mechanical understanding of the inversion process in the green alga Volvox, spherical sheets of cells that turn themselves inside out through a small hole at the top.

  1. S. Höhn, A. R. Honerkamp-Smith, P. A. Haas, P. Khuc Trong, and R. E. Goldstein, "Dynamics of a Volvox Embryo Turning Itself Inside Out", Physical Review Letters 114, 178101 (2015).
  2. P. A. Haas and R. E. Goldstein, "Elasticity and Glocality: Initiation of Embryonic Inversion in Volvox", J R Soc Interface 12, 20150671 (2015).
  3. P. A. Haas, S. S. M. H. Höhn, A. R. Honerkamp-Smith, J. B. Kirkegaard, and R. E. Goldstein, "The noisy basis of morphogenesis: Mechanisms and Mechanics of Cell Sheet Folding Inferred from Developmental Variability", PLOS Biology 16, e2005536 (2018).
  4. P. A. Haas and R. E. Goldstein, "Embryonic Inversion in Volvox carteri: The Flipping and Peeling of Elastic Lips", sub judice (2018), arXiv:1808.00828.

The first paper on inversion was the subject of a Physics Viewpoint, "How to Turn an Embryo Inside Out", by Arezki Boudaoud.

Other Research

Shape generating chemical systems are a very literal instantiation of Turing's quest for the chemical basis of morphogenesis. Pierre has been interested in developing a theory describing a recently discovered exemplar of such a system: oil emulsion droplets that flatten into a variety of polygonal shapes upon slow cooling.

Another research interest centres around probabilistic models for the prediction of RNA secondary structures, in particular extending models based on stochastic context-free grammars to include kinetic transcription effects.