I am a Ph.D. candidate in Statistics at Harvard University, advised by Kosuke Imai. I work in causal inference, with a focus on policy evaluation and policy learning. A central part of my research is developing statistical decision theory based on counterfactual utility.
I have an ongoing research collaboration with Netflix, where I interned in the summer of 2025 in the Experimentation & Causal Inference group and in the summer of 2026 in the Machine Learning and Inference Research group.
Before joining Harvard, I earned degrees from the University of Oxford (MSc in Statistical Science), ETH Zurich (MSc in Applied Mathematics), and the University of Vienna (BSc in Mathematics). I also worked full-time as a Data Scientist at QuantCo, focusing on statistical machine learning and pricing. Prior to my academic journey, I served in the Austrian Armed Forces, where I trained to become an army officer.
Research
For an always up-to-date list of publications, see my Google Scholar page.
Lecture Notes
- Causal Inference My personal lecture notes from teaching STAT 286: Causal Inference with Applications at Harvard University (Fall 2025).
- Minimax Lower Bounds Self-contained lecture notes on minimax lower bounds, Le Cam’s method, Fano’s lemma, and Assouad’s lemma.
Teaching
Harvard University
- STAT 210: Probability I (Fall 2024)
- STAT 234: Sequential Decision Making (Spring 2025)
- STAT 286: Causal Inference with Applications (Fall 2025)
ETH Zurich
- Probability Theory and Statistics (Spring 2021)