Lorenzo Steno
Founding Researcher at Seldon Technologies
About
I am a Master's student in Computer Science at the University of Twente, currently completing my thesis at ETH Zurich's Agentic Systems Lab.
Previously, I worked as a Solutions Architect Intern at AWS and as a Research Assistant at the AI & IoT Lab at The University of Twente. I focus on post-training methods for large language models, including fine-tuning, reinforcement learning, and agentic systems, with an emphasis on safety and trustworthiness.
Research Interests
- Multi-agent systems and AI for science
- Safety and trustworthiness of large language models
- Reinforcement learning for complex decision-making
Selected Projects
Green AI: Energy Efficiency of PEFT Methods
Co-authored a study evaluating energy-to-performance trade-offs of parameter-efficient fine-tuning for code generation using Qwen 3. Developed energy measurement pipelines with NVIDIA's NVML.
Energy-Efficient ML PEFT NVML