Hi, I’m Emanuel! 👋
I’m a Machine Learning Researcher, Mathematician & Data Scientist with a proven track record of solving challenging problems by developing rigorous methodology and practical systems. My work spans efficient and faithful uncertainty quantification for deep learning, high-dimensional financial risk models, and large-scale learning-to-rank systems, serving thousands hourly.
With a B.Sc. & M.Sc. in Mathematics from TUM, industry experience as an ML practitioner, and a PhD in Statistics & ML nearing completion at LMU Munich, I combine strong foundations in Statistics & ML with engineering and product thinking. My research focuses on scalable sampling-based inference and uncertainty quantification for modern (Bayesian) Deep Learning, with publications at leading venues such as ICML, ICLR, and AISTATS.
Beyond Research & Tech, I’m an enthusiast for hiking, food, travel, strength training, and dancing often sharing these experiences with my amazing girlfriend. I value mathematical elegance, but I’m most energized when that theory leaves the whiteboard and creates real innovation. 🚀🌍
Experience
PhD Candidate in Statistics & Machine Learning
Munich Uncertainty Quantification AI Lab | LMU Munich | MCML Oct 2023 – Present
- Develop efficient (sampling-based) methods for Bayesian deep learning and uncertainty quantification at scale.
- Published 10+ papers, including work at leading venues such as ICML, ICLR, and AISTATS; initiated and led international and interdisciplinary research collaborations.
- Academic service: Area Chair for ProbML 2026. Reviewer for NeurIPS (Top Reviewer 2025), ICML (Gold Reviewer Award 2026), ICLR, AISTATS, UAI.
- Teach courses and seminars in deep learning and statistical modeling; (co)supervise B.Sc. and M.Sc. theses.
Junior Data Scientist
Technology Hub | CHECK24 June 2022 - Aug 2023
- Designed, deployed and maintained large-scale end-to-end Learning-to-Rank systems serving thousands of users hourly leading to double-digit conversion rate improvements.
- Developed statistical methodology for sequential A/B testing to improve data-driven product decisions.
- Built stakeholder-facing dashboards increasing transparency and explainability of ML services.
- Cross-functional collaboration with various product teams to identify and solve business problems with ML solutions.
Data Scientist Intern
Financial Services Core/ Risk Banking | KPMG Sep 2019 - Dec 2019
Data Scientist role on a long-term project at an investment bank.
Planning and implementation of a custom automated data quality testing application for credit risk data in R, Rmarkdown and SQL. The generation of the reports was automated and the whole data quality testing cycle was reduced from 3 weeks (manually) to 4 hours (including review). The designed tests included univariate and multivariate outlier detection as well as other statistical methods.
Teaching Assistant
TUM School of Management April 2019 - Aug 2019
Tutor for the lecture Statistics for Business Administration.
Intern (Schnupperlehre)
EU/LA Property | Munich RE Jul 2013 - Aug 2013
Got a first taste of the reinsurance business and of the company.
Education
PhD in Statistics
Ludwig-Maximilians-Universität München (LMU) | MCML Oct 2023 – Oct 2026 (expected)
- Dissertation: Advances in Markov Chain Monte Carlo for Bayesian Deep Learning for Uncertainty Quantification at Scale
- Supervisor: Prof. Dr. David Rügamer
M.Sc. in Mathematics in Data Science
Technische Universität München (TUM) April 2020 - May 2022
- Thesis on Estimation and Backtesting of the Expected Shortfall and Value at Risk using Vine Copulas
- Focus on Machine Learning & Statistics
- Passed with Distinction
B.Sc. in Mathematics
Technische Universität München (TUM) Oct 2016 - March 2020
- Thesis on Sports Data Analytics: Regression and tree based models
- Focus on Statistics, Probability and Finance
- Minor in Economics
Teaching
| Course | Semester | University | Level |
|---|---|---|---|
| Deep Learning | Summer 2026 | LMU | Master |
| Applied Deep Learning | Winter 2025/26 | LMU | Master |
| Applied Deep Learning | Summer 2025 | LMU | Master |
| Deep Learning | Summer 2025 | LMU | Master |
| Advanced Statistical Modeling | Winter 2024/25 | LMU | Master |
| Applied Deep Learning | Summer 2024 | LMU | Master |
| Deep Learning | Summer 2024 | LMU | Master |
| Statistical Modeling | Winter 2023/24 | LMU | Bachelor |
| Statistics for Business Administration | Summer 2022 | TUM | Bachelor |
| Seminar | Semester |
|---|---|
| Amortization, Meta-, and In-Context Learning | Summer 2026 |
| Theoretical Foundations of Deep Learning | Winter 2025/26 |
| Dynamical Systems in Deep Learning | Summer 2025 |
| Theoretical Foundations of Deep Learning | Winter 2024/25 |
| Uncertainty Quantification in Deep Learning | Summer 2024 |
| Statistical Inference in Data Science | Winter 2023/24 |
To date, I’ve (co)supervised two Bachelor and 10+ Master students on topics in the realm of (Bayesian) Deep Learning, probabilistic modeling and UQ. I find the process of guiding and supporting a student in their research highly rewarding, and I often learn as much from my mentees as they do from me. Our lab is always looking for students who are driven by curiosity, have strong foundations and enjoy digging into the details.
Tech Stack
Below you can find a selection of some of my most valued tools from my tech stack roughly grouped by topic. Most tools are open source packages from my two main programming languages Python & . My command of both of them is advanced.
Data Prep & Wrangling
SQL pandas numpy
tidyverse dbt Athena, S3
Deep Learning & Stats
JAX pytorch sklearn
blackjax lightgbm nltk
tidymodels keras tidytext
Engineering & Scaling
Docker (compose) uv bash
Metaflow MLflow pre-commit
ECS Git Package Dev
Communication & APIs
FastAPI shiny streamlit
ggplot2 plotly datashader
quarto LaTeX MS Office