Meike Huber

Centre for Artificial Intelligence and Robotics

Papers

Total Citations

H-Index

About

Meike Huber is a researcher at the forefront of applying large language models to industrial automation and manufacturing. Her work centers on the critical challenge of equipment selection—a decision-making process that directly impacts production ramp-up time, quality, and adaptability to market shifts. Huber’s major contribution lies in designing an LLM-based copilot that assists engineers in navigating the complex landscape of automation equipment, addressing the common pitfalls of limited expertise and resource constraints. Her most-cited paper, "Designing an LLM-based copilot for manufacturing equipment selection" (2025, 4 citations), along with its earlier 2024 version, has already garnered attention for its practical, AI-driven approach to reducing inefficiencies in manufacturing. By bridging the gap between advanced natural language processing and real-world industrial needs, Huber is helping to democratize expert-level decision-making in production environments. Her work is particularly notable for its focus on actionable, user-centered design, making her a rising voice in the integration of generative AI into smart manufacturing and Industry 4.0.