Hi, I’m Jan!
Research Associate and PhD candidate at the University of Hamburg who loves turning complex, high-dimensional data into reliable decisions.
Double Machine Learning Causal Inference Deep Learning Data Storytelling
What I’m focused on right now
I design transparent machine learning pipelines for marketing, healthcare, and energy domains—bridging academic rigor with real-world decision support.
Trustworthy AI Systems
Building causal ML workflows that explain why strategies work, not just how well.
Multimodal Deep Learning
Creating architectures that combine text, image, and tabular data to unlock richer signals—especially for medical and marketing analytics with limited labeled data.
Impactful Teaching
Helping students and practitioners master statistics and machine learning through modern, hands-on curricula and open materials tailored to diverse learning styles.
Snapshot
5+
years applying analytics
10+
talks & teaching sessions
∞
curiosity for causal questions
Recent highlights
Estimating treatment effects in marketing
Developed a toolkit to estimate price elasticities using DoubleML with multi-modal features, capturing heterogeneous responses across product groups.
Collaboration with University of Hamburg and MIT · 2024
Teaching data-driven decision science
Co-designed and delivered courses spanning introductory statistics to advanced ML, with interactive notebooks that bring theory into applied projects.
Machine Learning in Business · 2023 – 2025
Software Development
I work on the DoubleML package, contributing to its development and maintenance.
Collaboration with University of Hamburg and Economic AI · 2022 – 2023
Career path
An industrial engineering foundation evolved into research that achieves transparent, human-centered analytics.
Research Associate · University of Hamburg
Pursuing a PhD in Statistics since 2023 with a focus on causal inference, DoubleML, and multimodal deep learning.
M.Sc. Industrial Engineering
Focus on data science and energy technology. Thesis on Causal Machine Learning with Deep Learning approaches.
Working Student · Philips Medical Systems
Built analytics products with Azure Databricks, Synapse, and Power BI to make data-driven planning effortless for service teams.
B.Sc. Industrial Engineering
Focus on electrical engineering and fluid mechanics. Thesis on autonomous control systems for quadcopters.
Working Student · Siemens Gamesa
Automated BI workflows and deployed Python-based tooling to help renewable energy teams act on insights faster.
Let’s collaborate
I’m always excited about projects at the intersection of causal inference, machine learning, and responsible product strategy. Reach out if you’re exploring applied research, data strategy, or engaging workshops.