I’m a Phenomics Engineer and Innovation Lead at the Netherlands Plant Eco-phenotyping Centre (NPEC) at Utrecht University, working at the intersection of robotics, imaging, controlled environments, and AI to turn plant responses into reliable, high-throughput data. With an MSc and PhD focused on imaging science, I’ve developed methods and systems across scales—from microscopy to whole-organism imaging—applied in both materials science and biological systems.

My focus is end-to-end phenotyping innovation: designing and integrating mechatronic systems, sensors and imaging workflows, and deploying data/ML pipelines that make experiments more reproducible, scalable, and actionable. Lately, I’ve been pushing real-time monitoring in controlled environments, bringing deep-learning inference closer to the experiment (inside growth chambers) so we can track growth dynamics, stress responses, and recovery continuously, not only at endpoints.

I also developed MultipleXLab, a portable high-throughput live-imaging platform for seed and root phenotyping that combines an affordable CNC-based imaging approach with deep-learning segmentation and computer vision. I’m driven by one principle: lowering the barrier to high-quality phenotyping while raising the bar for rigor and reproducibility.

What I enjoy most:
• Building phenotyping tools from concept → prototype → validated instrument → deployed facility
• Controlled-environment and stress phenotyping (light × temperature × CO₂ × humidity)
• Computer vision / ML for biological time-series data
• Affordable, accessible phenotyping that can scale across labs and industries

If you’re working on phenotyping infrastructure, automated imaging, or AI-enabled measurement workflows, I’m always happy to connect and collaborate.