Teaching Responsibilities 2019/20:
Research Interests: Monte Carlo Methods, Stochastic Gradient Methods, Stochastic Processes, Applications of Data Science.
@Students: Projects in Machine Learning and Computational Statistics are available
- The True Cost of Stochastic Gradient Langevin Dynamics
Tigran Nagapetyan, Andrew B. Duncan, Leonard Hasenclever, Sebastian J. Vollmer, Lukasz Szpruch, Konstantinos Zygalakis
- Stochastic gradient are a key gradient for many machine learning algorithms. This publication shows that there use for rigorous Bayesian inference is questionable?
- Measuring Sample Quality with Diffusions
Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester Mackey
- Contribution in statistics and machine learning: Rigorous a posteriori error bounds for a concrete sample from Monte Carlo computations.
- Contribution in mathematics: New regularity results for Poisson equations.
- The Data Study Group brings together researchers and industry to work on data science challenges posed by top companies. Up to six different organisations will each present a different real world data challenge of their choice. You will choose which challenge is of interest to you, working collaboratively in multidisciplinary teams to address the task at hand.
- You can register your contact details if you are interested in being contacted for future Data Study Groups.
- I have been involved in selecting in supervising data science startup's in the Winton accelerator.
- EPSRC First Grant EP/N000188/1 (£100k )
- LRF Grant on Piecewise Markov Processes (£120k )
- LRF Grant for Data Study Group on Data Centric Engineering (£50k), see blog pos
Arne Gouwy (joint supervised with Mihaela van der Schaar)
Arne obtained a BSc and MSc in mathematics at Ghent University in Belgium. Initially focusing more on functional analysis, he discovered the fields of stochastic processes and stochastic analysis during an Erasmus exchange with the University of Bonn. Interested in using this background to work on problems posed by technological and scientific advances needing automated solutions on large scale data sets, he was stimulated further by a variety of subjects in stochastic filtering and machine learning. This included an internship focusing on Bayesian optimization methods. In his free time, Arne enjoys running, cycling, swimming and reading.
Personal Homepage:vollmer.ms/sebastian (outdated)