Short Summary#
I am a mathematical microeconomist focusing on providing efficient software solutions for large-scale mathematical decision modeling and econometric estimations using big data with novel formats, statistical learning, and distributed technologies. My research focuses on market models, game theory, and micro-econometics. I have previously studied mathematics and economics and have some professional experience in software design and development.
I am occupied as a postdoctoral researcher at the University of Groningen. Moreover, I am a research affiliate of Leibniz Institute for Financial Research SAFE, and a researcher of the EurHisFirm consortium.
If you are looking for something more formal, you can take a look at my Curriculum Vitae. I also include more details for the interested reader about the things that I am passionate about in science, research, and technology.
More Details#
From my point of view, reproducibility, comparability, and accountability are concepts that are essential not only in STEM but also in economics. Therefore, I actively contribute to open-source solutions to promote these concepts in my field. In this respect, I am the maintainer of the statistical R package markets, which is distributed by CRAN, under open access and open source terms, and provides methods for estimating markets in equilibrium and disequilibrium. I am involved with the architecture and the implementation of a German financial historical database based on historical firm-level data extracted from historical archives with semi-automated techniques. Finally, I am developing the entity-matching software package Neer Match (in Python and R) that can be used when linking various types of formatted and unformatted data using artificial neural networks and a novel similarity encoding approach.
For those who share a passion for mathematics, I am interested in recursive, dynamic stochastic problems (e.g., optimal control and Bellman theory), potentially entailing strategic interaction elements and general equilibrium theory (e.g., Riesz spaces). I like to approach these topics from a function-analytic perspective. This approach is also very appropriate when it comes to implementing parallelized, cluster-scalable numerical methods to solve problems without analytical solutions (e.g., fixed-point methods).
For those interested in software development, I write in programming languages following different paradigms, ranging from procedural, object-oriented, and functional, depending on the case. As a LISP enthusiast and because of my mathematical background, I enjoy functional programming the most. I mostly use C and C++ when writing for high-performing cluster computing.
In any case, do not hesitate to contact me for anything that you may think that it is of interest.