My research focuses on developing automated and data‑driven pipelines for fungal strain engineering, with a particular emphasis on the industrial filamentous fungus Trichoderma reesei.
The main objective is to accelerate and improve protein production strain development by integrating automation, high‑throughput experimentation, and machine learning into the Design–Build–Test–Learn cycle of synthetic biology. Efficient and sustainable production of enzymes, food, and material proteins is a major challenge for the bioeconomy. Current strain engineering approaches for non‑model organisms are often slow and difficult to scale. My work addresses these challenges by designing automated workflows that increase throughput, reduce technical variability, and improve data quality. The research combines high‑throughput fungal transformation and screening with data analysis and predictive modeling to enable faster discovery and optimization of high‑performing production strains.
