Meet Flavio Antonio Franchina

Flavio develops application-driven workflows for targeted and non-targeted environmental analysis and bioanalysis. His work brings together sample preparation, separation, quantification, and data processing to address a central problem: modern instruments produce far more data than scientists can convert into knowledge.

“What excites me most is seeing the results come in and discussing them with my collaborators: the moment when the separate pieces of a workflow finally connect,” he says. Yet increasingly sensitive instruments make separating meaningful signals from noise more difficult. “Detecting more is not knowing more. A compound found in a sample may come from the sample, the environment, or unintended transformations along the way.” 

Flavio expects generative AI and machine learning to become embedded in analytical workflows, with instruments refining their own methods and models proposing compound identifications. “My one concern is a generation of scientists who can run an optimization but cannot explain why the optimum sits where it does,” he says. “I hope we will not trade understanding for speed.” He identifies data validation as another pressing issue: “Producing data has been democratized; validating it has not. Numbering features is not enough until someone has demonstrated how reliable they are, whether someone else can reproduce them, how the compounds are identified, and what the result can and cannot support.”

Moving first to the USA and then Belgium transformed Flavio’s career, taking him from instrumental and method development into application-led workflows. “I was suddenly a beginner again. That discomfort paid off,” he says. “Adapting to new cultures, inside and outside the lab, gave me a resilience no course could teach.”