About – Kai Ueltzhöffer

Kai Ueltzhöffer

Currently, I’m a postdoctoral researcher with Prof. Karl Friston at the Wellcome Centre for Human Neuroimaging, University College London, and with Prof. Sabine C. Herpertz at the Department of General Psychiatry at the University of Heidelberg’s Center of Psychosocial Medicine.

I studied Physics in Heidelberg, ages ago, which I finished with a diploma thesis on image processing for studying the microstructure of polar ice cores, the only climate archive from which you can get actual samples of the past atmosphere. After that I took a short swing into Astronomy, searching for extrasolar planets with small robotic telescopes using consumer hardware. But soon I got hooked by the human brain and started a PhD in Physics (but rather Cognitive and Computational Neuroscience) at the University of Frankfurt am Main, which I defended in October 2016. During the PhD I implemented a very simple model of working-memory and decision-making, based on recurrent spiking neural networks. The model could be fitted to behavioural data of individual subjects doing a computer experiment, in which they had to switch between two simple tasks. Somehow it was even physiological enough to predict the functional MRI response of the individual subjects in brain areas well known to be relevant for task-switching. During the second year of the PhD, however, I had the feeling that studying the brain without a good overview of the basic anatomy, biochemistry and physiology of the human body would not get me too far (c.f. the idea of embodiment), so I started studying medicine. In December 2019, I passed the final part of the German medical licensing exam. Currently, I’m still working on my MD thesis on reactive aggression in borderline personality disorder.

In my spare time, I like to play at the interface of machine learning, mainly variational inference and sampling methods in combination with deep (recurrent) neural networks (such as the great work by Kingma & Welling, Rezende, Mohamed & Wierstra, Chung & al.), and current theories in computational neuroscience (mainly the active inference framework). Recently, this lead to a publication trying to combine the flexibility of deep-learning with the objective function and architecture of active inference. Furthermore, I like to think about statistical physics, its relations to evolution and the origins of life, as well as random topics related to maths, physics, biology and computer science. So this is what this blog is mainly supposed to be about. But we’ll see…