I am a PhD candidate in machine learning at the Technical University of Munich in cooperation with the learning systems research group at Siemens AG. At Siemens AG, I spend about half of my time working on industrial applications of machine learning. My research interests include hierarchical probabilistic models, Gaussian processes, reinforcement learning under uncertainty and the incorporation of expert knowledge into model assumptions.
M.Sc. in Computer Science, 2017
KTH Royal Institute of Technology
M.Sc. in Computer Science, 2016
Technical University of Munich
B.Sc. in Computer Science, 2013
Technical University of Munich
Research interests include:
Besides my research, I work on probabilistc models for industrial applications and related infrastructure problems.
We interpret the data-association problem of multimodal regression in the context of deep Gaussian processes and present an inference scheme based on doubly stochastic variational inference.
We extend multi-output Gaussian processes with nonlinear alignments and warpings. The resulting model connects multiple deep Gaussian processes with a shared layer that allows us to extract shared latent data from multiple time series.
In this talk, I present an introduction to pseudo-input methods for sparse GP approximations. I derive the variational lower bounds for SGPR and SVGP and give some intution for how they should be interpreted.
This project contains the code required for the installation and configuration of the different services running on my Linux server. To simplify dependency management, I use Docker-based deployments.
In my master’s thesis I explore a variant of PILCO for Bayesian model-based reinforcement learning using Gaussian processes. Instead of optimizing a closed-form parameterized policy, I select actions by applying particle swarm optimization to the expected reward, which takes uncertainties about the system dynamics into account.
Power diagrams are a generalization of Voronoi diagrams where the cell centers attract points with different forces. In this report I present an algorithm which calculates the incidence struture of such a diagram using the convex hull of a set of dual points.
LLVM-IL is a Scala-Library used to emit a subset of the textual LLVM-IR Code. Besides the direct commands, it contains some specific OOP features, like the creation of simple V-Tables paired with field access and virtual resolve. It works together with a simple runtime written in C.
The slides I created while teaching the tutorial for theoretical computer science at TU Munich. Theoretical computer sciences is held in the fourth semester of the Bachelor. It is an introduction to automata theory, formal grammars, computability and complexity theory.
Oblivious routing is generalization of multi commodity flows where the actual demand function is unknown. In this report I present a $\mathcal{O}(\log n)$ approximation algorithm using tree metrics. This result is then applied to the minimum bisection problem asking for an vertex bisection with minimal cost in the edges between the sets, also resulting in an $\mathcal{O}(\log n)$ approximation.
The slides I created while teaching the tutorial for discrete structures at TU Munich. Discrete structures is the first mathematical course for comptuer scientists held in the first semester of the Bachelor. It is an introduction to mathematical proofs, combinatorics, graph theory and algebra.