I am open to supervising projects in Bayesian methods, statistical computing, and/or applied statistics. Example topics might include, but are not limited to:
– Hamiltonian Monte Carlo (HMC) and Riemannian manifold HMC: framing a Bayesian estimation problem as a dynamical system.
– Probabilistic numerics: this is a way to quantify uncertainty in approximation of intractable integrals, a statistical approach to error quantification.
– Statistical interpretations of deep learning: how can currently popular deep learning approaches be viewed from a statistical perspective.
Here are some papers associated to the topics above:
Girolami, M. and Calderhead, B. (2011), Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73: 123-214.
Diaconis, P. (1988), Bayesian numerical analysis. Statistical decision theory and related topics IV, 163-175. Springer-Verlag, New York.
Nalisnick, E., Smyth, P. and Tran, D. (2023) A Brief Tour of Deep Learning from a Statistical Perspective. Annual Review of Statistics and Its Applications 10: 219-246.