Michael Mühlbauer
Research Associate in Statistics & Machine Learning
Scientific research associate at the University of Bamberg, working at the intersection of statistics and machine learning. My research focuses on developing novel methods for density estimation from aggregated data and applying random forests to ordinal data problems. Passionate about bridging rigorous statistical methodology with modern AI to drive data-informed decision-making.
Research Interests
My work focuses on applying advanced analytics and machine learning to solve real-world problems, with a growing interest in AI transformation and strategic adoption of AI in organizations.
Small Area Estimation
Developing statistical methods for reliable estimates in small geographic areas or subpopulations with limited sample sizes.
Spatial Data Analysis
Analyzing geographically referenced data, including density estimation from aggregated spatial information and choropleth mapping.
Kernel Density Estimation
Extending measurement error models for density estimation when precise geocoordinates are unavailable due to privacy constraints.
Random Forests for Ordinal Data
Applying and adapting machine learning methods, particularly ordinal random forests, for analyzing categorical and linguistic data.
Education
Academic background in statistics, economics, and business.
Doctoral Candidate in Statistics (Dr. rer. pol.)
University of Bamberg
Bamberg, Germany
Cumulative dissertation: "Reversing the Coarse: Statistical Methods for Recovering Structure from Aggregated, Sparse, and Discretized Data".
Thesis submitted, currently under review.
M.Sc. in European Economic Studies
University of Bamberg
Bamberg, Germany
Advanced studies in economics with focus on quantitative methods and European economic policy.
B.A. in Business Administration
OTH Regensburg
Regensburg, Germany
Foundation in business administration and quantitative analysis.
Professional Experience
Building solutions at the intersection of data science, machine learning, and business strategy.
Scientific Research Associate
University of Bamberg
2021 - Present
Bamberg, Germany
- • Conducting research at the Chair of Statistics and Econometrics
- • Developing novel methods for density estimation from aggregated geospatial data
- • Applying random forest methods to ordinal data in linguistic research
- • Publishing research in peer-reviewed journals and arXiv
- • Teaching and supervising students in statistics, econometrics, and machine learning
Student Research Assistant
University of Bamberg — Chair of Statistics and Econometrics
2020 - 2021
Bamberg, Germany
- • Assisted with data analysis and statistical modeling
- • Contributed to a research project on demographic development
Student Research Assistant
University of Bamberg — Chair of Economic Policy
2020
Bamberg, Germany
- • Developed course materials for an applied econometrics lecture
Bachelor Thesis Student
Krones AG
2018 - 2019
Neutraubling, Germany
- • Analyzed organizational challenges in after-sales management of an international key account
- • Proposed an organizational redesign (LCS partner system) to improve cross-regional coordination
- • Presented findings and recommendations to Key Account Management and Global LCS leadership
- • Conducted a survey among lifecycle service sales staff to identify structural issues
Innovation Management Intern
Continental AG
2016 - 2017
Regensburg, Germany
- • Evaluated incoming ideas from a corporate ideation platform and contributed to innovation decision-making
- • Conducted technology and patent research, providing summaries to senior decision-makers
- • Coordinated internationally across departments and business units on innovation management tasks
- • Analyzed gamification methods and recommended platform enhancements
- • Processed and visualized innovation KPI data for corporate reporting
Technical Expertise
A comprehensive toolkit for turning data into actionable insights and production-ready solutions.
Statistical Methods
Programming & Tools
Machine Learning
LLM & AI Engineering
Publications
Research contributions in statistics, spatial analysis, and machine learning.
Density Estimation from Aggregated Data with Integrated Auxiliary Information: Estimating Population Densities with Geospatial Data
Michael Mühlbauer, Timo Schmid
Ordinal random forests in language data analysis
Michael Mühlbauer, Lukas Sönning