Hi, I’m Emanuel! 👋
I’m a passionate ML Researcher, Mathematician & Data Scientist with a proven track record of crafting and deploying Statistical / Machine Learning Models. These range from high-dimensional Financial Risk Models to large-scale Learning-to-Rank Models, serving thousands hourly.
With a B.Sc. & M.Sc. in Mathematics from TUM, I’ve built a strong foundation in Statistics & ML. Following industry experience as an ML Practitioner, I’m now pursuing a PhD in Statistics, focusing on sampling-based inference for Bayesian Neural Networks to advance uncertainty quantification in modern Deep Learning. My research has been published at top venues like ICML and ICLR.
Beyond Research & Tech, I’m an enthusiast for hiking, food, travel, often sharing these experiences with my amazing girlfriend. I value mathematical elegance, but I’m most energized when that theory leaves the whiteboard and starts driving real innovation. 🚀🌍
Experience
PhD Candidate in Statistics & Machine Learning
Munich Uncertainty Quantification AI Lab | LMU Munich | MCML Oct 2023 – Present
- Research: (Sampling based Inference for) Bayesian Deep Learning.
- Reviewing: NeurIPS (Top Reviewer 2025), ICLR, AISTATS, UAI.
- Teaching: Graduate & Undergraduate Courses in Statistical Modeling and Deep Learning.
Junior Data Scientist
Technology Hub | CHECK24 June 2022 - Aug 2023
As a Data Scientist in the central Data Science team TechHub, I was at the forefront of driving customer success by delivering innovative ML solutions for various products within the company.
- Responsibility for the Design, Implementation and Maintenance of E2E Learning-to-rank services serving thousands hourly to effortlessly match customers with the products fitting their needs, constantly innovating and as a result improved conversion significantly.
- Collaboration with stakeholders in cross-functional teams across multiple products required me to be highly flexible and a quick learner, with strong business acumen and strong communication skills.
- Equipment of stakeholders with rich dashboards in order to not only monitor but also to foster the explainability and transparency of the ML services.
- Improved the decision making process with cutting-edge statistical methodology in the realm of sequential A/B testing.
Data Scientist Intern
Financial Services Core/ Risk Banking | KPMG Sep 2019 - Dez 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 Juli 2013 - Aug 2013
Got a first taste of the reinsurance business and of the company.
Education
M.Sc. in Mathematics in Data Science
Technische Universität München (TUM) April 2020 - Mai 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 |
|---|---|---|---|
| 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 |
|---|---|
| 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 8+ Bachelor and (mostly) 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