MSc Computer Science
Thesis: Training Efficient Recursive Language Models [Work In Progress at ETH Zurich]
Member of the advisory board of the CS study association.
Lorenzo Steno
MSc Computer Science · University of Twente
Incoming Master Thesis Student at ETH Agentic Systems Lab
About
I am a Master's student in Computer Science at the University of Twente, starting my thesis at ETH Zurich's Agentic Systems Lab in March 2026.
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 autonomous scientific discovery
- Safety and trustworthiness of large language models
- Reinforcement learning for complex decision-making
Education
BSc Computer Engineering
Experience
Master Thesis Student
Training efficient recursive Language Models with RL and LoRA.
Research Assistant
Researched applications of generative AI in digital twins and distributed systems. Designed and deployed cloud-based experiments on AWS.
Solutions Architect Intern
Implemented GenAI capabilities into internal tooling, automating summarization and tagging (~40% reduction in review time). Developed serverless workflows using Lambda, S3, DynamoDB, and Bedrock.
Teaching Assistant
Guided 30+ students through GPS-to-MQTT IoT pipelines. Managed AWS IoT Core, EC2, and IAM policies for the AI and IoT Lab.
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
Certifications
- AWS Certified Solutions Architect Associate
- AWS Certified AI Practitioner
- BlueDot Impact – Technical AI Safety
- Santa Fe Institute – Introduction to Complexity