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Jeremias Knoblauch

Click here for my personal webpage
About Me
I am a 3rd year postgraduate researcher in statistics working at the boundary of computer science as part of the Oxford-Warwick Statistics Programme (OxWaSP) together with Theodoros Damoulas and Chenlei Leng. My interests are focused on scalable spatio-temporal inference procedures for data generating mechanisms in high dimensions that are ill-behaved or difficult to describe. This encompasses modelling and doing inference for non-stationary data streams that may have changing behaviours across time as well as space. The algorithms and inferential procedures developed as part of this research will be used within the framework of the Clean Air London project at the Turing Institute to support London's Major's office in taking well-informed and data-driven policy decisions. For a more detailed look, here is my CV.
Research Interests

Modelling changepoints in a Bayesian way is elegant and computationally efficient. I am currently working to extend this into a spatio-temporal context and to enable scalable robust inference on multivariate data.

Talks & Presentations
  • Doubly Robust Bayesian Inference for Non-Stationary Streaming Data using β-Divergences, Neural Information Processing Systems (NIPS), Montreal (02/12/2018-08/12/2018) [poster presentation]
  • Doubly Robust Bayesian Inference for Non-Stationary Streaming Data using β-Divergences, Facebook's PhD London Tech Talk (25/10/2018) [poster presentation + best poster award]
  • Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, International Conference on Machine Learning (ICML), Stockholm (10/07/2018-15/07/2018) [Talk and poster presentations]
  • Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, Statistics Seminar of the Glasgow School of Mathematics & Statistics, University of Glasgow, 06/07/2018
  • Bayesian Analysis for Non-Stationary Streaming Data, Seminar Series of the CDT in Data Science, University of Edinburgh, 04/07/2018
  • Bayesian On-line Changepoint Detection and Model Selection in high-dimensional data, Workshop on Computational Strategies for Large-Scale Statistical Data Analysis by the International Centre for Mathematical Sciences, Edinburgh 05/07/2018
Software

I have written a substantial amount of software in Python accompanying my research, through which we have been nominated as Turing Reproducible Research Champions 2018 by the Alan Turing Institute. While it is still being worked on, the preliminary version is available here.

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Office: 3.14, Mathematical Sciences Building (MSB)

Contact: j.knoblauch@warwick.ac.uk

Personal webpage: https://jeremiasknoblauch.github.io/