Position: Postdoctoral Research Assistant
Field of research: High-dimensional computational statistics
School of Mathematics,
University of Edinburgh,
James Clerk Maxwell Building,
Peter Guthrie Tait Road,
Edinburgh, EH9 3FD,
My research interests include
I gave a short (5 min) introduction to particle filtering at the Isaac Newton Institute in May 2018, which is available online via this link, and a slightly longer talk on another project in November 2018, found here. A high-level, and hopefully entertaining, explanation of one of my research projects is available on YouTube.
- High-dimensional statistics
- Bayesian inference
- Bayesian neural networks
- Function space sampling methods
I taught the following courses:
- 2017-2020 - Supervisor for Part II Topics in Analysis and Part II Numerical Analysis at the University of Cambridge
- 2014-2016 - Tutor for Analysis I and II at the University of Rostock
- TS, "Advanced Bayesian Monte Carlo Methods for Inference and Control", PhD Thesis, pdf available upon request
- TS, Sumeetpal Sidhu Singh, "Dimension-Robust Function Space MCMC With Neural Network Priors", under review, arXiv preprint available
- Jacob Vorstrup Goldman*, TS*, Sumeetpal Sidhu Singh, "Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-Differentiable Priors", accepted for publication by the Journal of the American Statistical Association, arXiv preprint available. * denotes equal contributions
- TS, "MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster", Essay for Part III of the Mathematical Tripos, pdf
- TS, "A Convergence Analysis for Preconditioned Gradient Type Eigensolvers", Thesis for Bachelor of Science, pdf
In 2020 I co-organised the Mathematics of Data Science conference, and wrote a short lessons learned paragraph about it.
In the academic year 2018/2019 I was one of two directors of the Part III seminar series at the University of Cambridge.
I enjoy reviewing papers within my area of expertise.