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Jeremias Knoblauch

I am a 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 into my background, you can also have a look at my CV.
Current research

Modelling changepoints in a Bayesian way can be an extremely elegant and computationally feasible way of doing inference in an on-line way. I am aiming to extend this inherent time series method into a spatio-temporal context admitting non-stationary behaviour in space and time while retaining tractability enabling scalable inference. Applying an algorithm like this to London's air pollution sensor network can reveal whether changes in congestion charges achieve the desired fall in the level of pollutants.

Past research

Before joining OxWaSP, I had a very strong interest in time series methods. This ranged from applied work on European temperature trends as part of my Bachelor thesis ( to investigating the validity of post-model selection inference in my Master thesis (


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