I am a senior research scientist at the Allen Institute for Artificial Intelligence in Seattle, where I work on natural language processing and machine learning. My research focuses on core language model development and applications, including recent work on formal and probabilistic methods for LLMs and agents for scientific discovery.
Previously, I was a researcher at the Institute for Natural Language Processing at the University of Stuttgart in Germany, where I received my PhD in 2018.
See my publications and selected talks below for more details.
NeurIPS: Two papers accepted: Operads for Compositional Reasoning in LLMs and CoTs as Probabilistic Programs: A Programmatic View of Thinking Step-by-Step in Language Models.
EMNLP: From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models was accepted.
TPM @ UAI: CoTs as Tractable Probabilistic Programs was accepted. I also gave a keynote, Tractable Language Model Programming: Themes and Prospects (video).
COLM: Artifact Linker, a benchmark and environment for LLM-driven automated scientific discovery, was accepted.
We released a new preprint on modeling question decomposition with operads, along with a shorter technical report presented at the Combining Theory and Benchmarks workshop at ICML.
We released Probabilistic Programs of Thought, joint work with the UCLA StarAI Lab.
ICLR: Two papers accepted: AstaBench: Rigorous Benchmarking of AI Agents with a Holistic Scientific Research Suite and Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis.
I gave an invited keynote at the International Workshop on Symbolic-Neural Learning in Osaka (slides).
NeurIPS: Language Modeling by Language Models, our work on research agents for autonomous machine learning, was accepted as a spotlight paper.
EMNLP: TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents was accepted.
We released the AstaBench leaderboard and accompanying technical paper for evaluating LLM agents across scientific tasks.
I taught an updated version of our Language Model Programming course at ESSLLI, including new lectures on probabilistic programming for prompting and loss-function decompilation.
I presented Language Modeling by Language Models, a talk on automated scientific discovery, at the NAACL Workshop on AI for Scientific Discovery (preprint).
ICML: Two papers accepted: Understanding the Logic of Direct Preference Alignment through Logic (brief overview) and ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning.
For earlier work and a complete list, see my Google Scholar profile.