Hi, I'm David.

I use principal–agent theory to study AI agents and multi-agent systems: what happens when we delegate work to agents whose actions we can only partly observe, how incentives shape what they end up optimizing, and when checking their work is cheap enough to make delegation worthwhile. My methods come from machine learning, microeconomic theory, and econometrics.

Open to collaborations and to supervising semester and master’s theses.

How I like to work.

News

Selected publications

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Read the Paper, Write the Code: Agentic Reproduction of Social-Science Results

With Benjamin Kohler, Johanna Einsiedler, Alexander Hoyle, Elliott Ash · EMNLP 2026, 2026

Novel Ion Suppression-Based MS Approach (IS-MS) for Identification of Liquid Samples: Wine as a Case Study

With Maria Bikaki, Lu Hostettler, Matthias Kleiner, Götz Schlotterbeck, Markus Ehrat · Analytical Chemistry, 2026

Synthetic Cancer: Augmenting Worms with LLMs

With Benjamin Zimmerman · Swiss AI Safety Prize, first prize, 2024

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