Graph-aware learning
Graph representations and message passing for learning in coupled systems.
Representation learning & sequential decisions
Research
My work in reinforcement learning studies how policy structure and observation representations affect learning in coupled systems. I also contribute to research on recurrent-agent behavior and language-model evaluation.
Current research
Representation learning, coordination and model behavior.
Scalable multi-agent systems
Coordination and action selection for scalable multi-agent reinforcement learning.
Social foraging & agent behavior
Collaborative research on recurrent agents, social foraging, and behavioral analysis.
LLM interpretability & language bias
Evaluating how language models preserve or shift meaning across languages.
Earlier work
Thesis research and collaborative work at RWTH Aachen.
RWTH Aachen · Bachelor's thesis · 2024–2025
RL for sequential decision-making
Hierarchical and multi-agent reinforcement learning for coordinated policies and long-term objectives.
WZL · CIRP ICME 2026 · 2026
Integrative digital factory planning
Co-author. A framework for connecting planning tools and disciplines in circular-economy production systems.
Talks & posters
Energizing@MIT · September 2026
Presenting at MIT
I presented my work on multi-agent reinforcement learning at MITEI's Annual Research Conference, discussing graph representations and the coordination of local policies.
Poster with Cathy Zhang, Dirk Lauinger and Deepjyoti Deka.