Publications

. Multimodal Deep Gaussian Processes. arXiv.org, 2018.

PDF TeX arXiv

. Bayesian Alignments of Warped Multi-Output Gaussian Processes. NeurIPS, 2018.

PDF Slides Poster TeX arXiv Proceedings

Experience

 
 
 
 
 
September 2016 – Present
Neuperlach

PhD Candidate

Siemens AG

Research interests include:

  • Hierarchical probabilistic models
  • Reinforcement learning under uncertainty
  • Incorporation of exper knowledge into model assumptions

Besides my research, I work on probabilistc models for industrial applications and related infrastructure problems.

 
 
 
 
 
September 2015 – June 2016
Neuperlach

Master’s Thesis

Siemens AG

Title: Incorporating Uncertainty into Reinforcement Learning through Gaussian Processes.

  • Model based reinforcement learning with Gaussian Processes
  • Propagation of predictive uncertainties
  • Evaluation on the bicycle benchmark
 
 
 
 
 
September 2015 – July 2016
Garching

Tutor

Technical University of Munich

Teaching assistant in multiple undergraduate courses:

Projects

Multimodal Deep Gaussian Processes

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.

Bayesian Alignments of Warped Multi-Output Gaussian Processes

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.

Sparse GP Approximations

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.

zfix-docker: Dockerized deployment of my server infrastructure

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.

Incorporating Uncertainty into Reinforcement Learning through Gaussian Processes

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.

Incidence-Structures of Power Diagrams

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: A Scala-Library that emits LLVM Intermediate Language

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.

Theoretical Computer Science Tutorial

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 and Minimum Bisection

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.

Discrete Structures Tutorial

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.