Position | Associate Professor |
Phone (external) | +44(0)20 3108 2260 |
Phone (internal) | 52260 |
Email (@ucl.ac.uk) | j.knoblauch |
Themes | Computational Statistics and Machine Learning, General Theory and Methodology, |
Personal webpage | https://jeremiasknoblauch.github.io/ |
Biographical Details
I am currently an Associate Professor at UCL’s Department of Statistical Science. In July, I will take up an appointment at Assistant Professor/Lecturer at the department, as well as a 3-year EPSRC research fellowship to continue my work on Optimisation-centric Generalisations of Bayesian Inference. I am also a visiting researcher at the Alan Turing Institute’s Data-Centric Engineering Programme.
My research revolves around extending the paradigm of Bayesian inference to cope with the challenges posed by modern large-scale data, simulator models, and machine learning techniques. In this context, I am particularly interested in generalised Bayesian inference, model misspecification and robustification strategies, computational challenges involving intractability, and variational methods. If you would like to learn more about my research, I have summarised some of my key contributions in the following talk.
Prior to UCL, I was a doctoral candidate within the Oxford-Warwick Statistics programme (2016-2022) as well as the first UK-based Facebook Fellow (2020/2021). During that time, I also worked with the research arms of Amazon (2019) and DeepMind (2021). I remain eager to stay in close contact with industry, and am very open to being approached by potential industrial partners for both academic and non-academic work.
Publications
Publications can be found here.