Rate My Professor Sophie Hautphenne

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.