Johannes Bleher

Hi, I’m Dr. Johannes Bleher, currently managing the Computational Science Hub at the University of Hohenheim in Stuttgart and coordinating the AI & Data Science Certificate program.

I completed my PhD in Economics and Finance in 2021 at the Eberhard Karls University of Tübingen where I focused on robust estimation techniques and quantile regression-based density forecasts under the supervision of Prof. Dr. Thomas Dimpfl.

In my role at the Computational Science Hub, I coordinate initiatives that integrate advanced computational methods. This includes developing innovative teaching methodologies, overseeing interdisciplinary research projects, and applying machine learning techniques to complex data. Additionally, my experience as a Risk Analyst at Bausparkasse Schwäbisch Hall and as an Accredited Parliamentary Assistant at the European Parliament for Dr. Inge Gräßle has provided me with a unique perspective on the practical applications of econometric models in both the financial sector and policy-making.

Furthermore, as an independent business consultant, I have led projects focused on portfolio allocation and the setup and continuous integration and evaluation of databases. These initiatives have enabled organizations to optimize their financial strategies and enhance their data management practices through tailored, data-driven solutions.

Currently, I am spearheading projects that bridge the gap between data science and empirical economic research, aiming to enhance the quantitative skills of students and researchers alike. My passion lies in leveraging data analysis and econometric methods to solve real-world economic challenges and drive impactful research.

I am fascinated by the power of quantitative methods and their ability to uncover insights in finance and economics. I’m always eager to collaborate on projects that push the boundaries of data-driven research and to engage with others who share a passion for empirical analysis and innovative teaching.

Feel free to reach out if you’d like to discuss collaborative opportunities or share insights on data-driven economic analysis!