Sophie Hautphenne

Sophie Hautphenne is an Associate Professor in Stochastic Modelling in the School of Mathematics and Statistics, Faculty of Science, at the University of Melbourne. She received her PhD in Mathematics from the Université libre de Bruxelles in 2009, with a thesis entitled 'An algorithmic look at phase-controlled branching processes' supervised by Guy Latouche and co-supervised by Marie-Ange Remiche. She also holds a Diplôme d’Études Approfondies from the same institution in 2006 and a Degree in Mathematical Sciences in 2005, graduating with first class honors (Summa cum laude) and receiving the University Medal. Earlier, she was awarded the Ruth and Joe Gani Prize for the best student in Probability during her Bachelor’s degree in 2004. Her career trajectory includes Senior Lecturer at the University of Melbourne from 2017 to 2022, Research Fellow there from 2011 to 2019, part-time Scientist at the Chair of Statistics, École Polytechnique Fédérale de Lausanne from 2015 to 2018, Postdoctoral Researcher at Université libre de Bruxelles in 2010, and PhD Student and Postdoctoral Researcher funded by the Belgian National Science Foundation from 2005 to 2010.

Hautphenne's research specializations encompass applied probability, stochastic modelling, and branching processes, with applications to population biology, phylogenetics, and demography. Key publications include 'Consistent least squares estimation in population-size-dependent branching processes' by Braunsteins, Hautphenne, and Minuesa (Journal of the American Statistical Association, to appear 2026), 'Approximate Bayesian computation for Markovian binary trees in phylogenetics' by He, Chan, and Hautphenne (Journal of Theoretical Biology, 2026), 'Linking Population-Size-Dependent and Controlled Branching Processes' by Braunsteins, Hautphenne, and Kerlidis (Stochastic Processes and their Applications, 2025), 'Birth-and-death Processes in Python: The BirDePy Package' by Hautphenne and Patch (Journal of Statistical Software, 2024), 'A fluid approach to total-progeny-dependent birth-and-death processes' by Hautphenne and Li (Stochastic Models, 2023), 'Parameter estimation in branching processes with almost sure extinction' by Braunsteins, Hautphenne, and Minuesa (Bernoulli, 2022), and 'A structured Markov chain approach to branching processes' by Hautphenne (Stochastic Models, 2015). She has obtained major grants such as the ARC Discovery Early Career Researcher Award DE150101044 (2015, $315,000) for computational approaches for branching processes in population biology, ARC Discovery Project (2020, $380,000) on population-size-dependent branching processes, and co-investigator roles on additional ARC projects including a 2025 Discovery Project ($551,600). Hautphenne serves on the editorial boards of Stochastic Models (since 2017), The Applied Probability Trust journals (since 2020), and Statistical Inference for Stochastic Processes (since 2022). She coordinates research higher degree students and supervises PhD and Master's students.

Professional Email: sophiemh@unimelb.edu.au