Dear Reader!

Welcome to my website!

Here I am frequently tweeting and blogging on statistics, signal processing, inverse problems, probability theory, telecommunications, history, and software development in English and German. Now it is even optimized for mobile viewing. Since LaTeX, Maxima, and Python are powerful open-source tools for doing research, I give some words of advice and present some code snippets. The software section shows some of my old software projects (C/C++).

Enjoy reading my website and don't hesitate to give me feedback!

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Résumé

Currently, I am with Frequentis AG. I have been a consultant and a service provider in information technology with focus on statistics, programming, communications, and streaming of live events, which reflect my interests and hobbies.

From mid-2008 to mid-2009, I did my master thesis on RFID with NXP Semiconductors Austria. Afterwards, I was researcher in statistics / signal processing and teaching assistant with Institute of Telecommunications of Vienna University of Technology.  From autumn of 2013 to spring of 2014, I did my research at Institute of Telecommunications of TU Darmstadt due to a fellowship of Federation of Austrian Industries. Afterward I was self-employed and became a certified project-management associate. See my LinkedIn profile or contact me for my detailed curriculum vitae.

My research's foci are statistics, signal processing, and probability theory in the area of (mobile) communications. I did my work in cooperation with Prof. Christoph Mecklenbräuker (PhD advisor), Prof. Peter Gerstoft, Prof. Gerald Matz, Prof. Marius Pesavento, and Prof. Norbert Goertz.

Favorite Quotation

If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.

John von Neumann (1903-1957)

Sequential Weiss-Weinstein Bounds

Sequential Bayesian estimation and detection [VB07] inferes temporal evolving states in contrast to non-evolving parameters. The sequential Weiss-Weinstein bound is a lower bound on the mean-square-error matrix of any Bayesian estimator. This article is based on  Bayesian Cramer-Rao Bounds and Weiss-Weinstein Bounds about non-sequential Bayesian inference and performance bounds. Note that here only a short overview is possible and hence I neglect many details that can be found in one of the references. 

Three Paradigmata of Inverse Problems: Algebraic vs. Frequentist vs. Bayesian Inference

Consider a sensor that is measuring physical parameters like temperature, pressure, or velocity. This sensor introduces perturbations and noise and, hence, one key-problem is the optimal inference of parameter $\boldsymbol{x}$ using measurement  $\boldsymbol{y}$ from the sensor.  Such inference of parameters is used in many research areas like telecommunications, finances, medizine, or social science.  When we speak about inference we have to ask: What is optimal inference? Which criterion shall we use? A natural criterion is the inference error. But how shall the error be defined? In the sequel, I address these main questions and hope to give a good overview.