AI is only as good as the data it runs on. In the life sciences lab, much of that data is born at the analytical instrument. As laboratories advance through automation and digitalization, analytical instruments are becoming the critical interface between experimental reality and AI-driven decision-making. The quality, structure, and accessibility of the data generated here determine whether machine learning can deliver real value across discovery, development, and QA/QC.
This talk will show how advanced software can be integrated with liquid chromatography hardware and external automated workflows to reliably generate high-quality, AI-ready chromatographic data. Building on this foundation, we will present a prototype for automated LC gradient optimization in OpenLab CDS, demonstrating how machine learning can make analytical method development more systematic, efficient, and reproducible.
Speaker: Mr. Christian Haas, R&D Scientist, Agilent Technologies Deutschland GmbH
Lecture language: English
