Postdoctoral Researcher at IDEAL Lab, ETH Zurich
I am a Postdoctoral Researcher at the Intelligence for Design, Engineering, And Learning (IDEAL) Lab at ETH Zurich, working under the supervision of Prof. Dr. Mark Fuge. My research focuses on leveraging Large Language Models (LLMs) to enable autonomous artificial agents for iterative engineering design tasks.
Research Focus
Current Work at IDEAL Lab
My current research explores the intersection of artificial intelligence and engineering design, specifically:
- LLM Fine-tuning for engineering domain expertise
- Multi-agent frameworks for structured design processes
- Generative 3D modeling using AI
- Multi-objective optimization in design spaces
- Autonomous design agents for iterative engineering tasks
Doctoral Research (2020-2024)
I completed my Ph.D. at the Laboratory for Applied Mechanical Design (LAMD) under Prof. Dr. Jürg Schiffmann at EPFL. My doctoral work focused on:
- Multidisciplinary, robust design methodologies
- Surrogate model-assisted optimization
- Ensemble neural networks and evolutionary algorithms
- Applications in gas-bearing supported turbocompressors for:
- Heat pumps
- Fuel cells
Academic Background
Ph.D. in Mechanical Engineering
EPFL
M.Sc. in Mechanical Engineering
EPFL
B.Sc. in Mechanical Engineering
EPFL
Student Projects & Mentoring
I mentor students working on projects that build upon my research methods:
Research Impact
My work aims to automate the entire engineering pipeline—transforming concepts into manufacturable multi-component, multi-system designs. This spans from initial concept generation through to final optimization, with a focus on:
Multi-component system design
and workflow optimization
AI-assisted engineering
processes for complex systems
Robust design methodologies
for integrated systems
Multi-physics optimization
across system boundaries
I'm passionate about advancing the field of AI-assisted engineering design and creating tools that can accelerate the design process for complex multi-component systems while maintaining high performance standards.