Meet Bob Pirok

Bob develops chromatography-based solutions for societal and industrial problems. A self-described “chromatography lab practitioner,” he combines fundamental separation science with AI, automation, and other computational approaches to extend what chromatography and mass spectrometry can answer.

“What excites me most is the advent of autonomous laboratories and learning analytical workflows,” he says. Bob envisages systems that remember previous experiments, recognize what succeeded or failed, and recommend what to try next. “The promise is not that science becomes push-button, but that researchers spend less time rediscovering what is already known and more time asking better questions.” Such systems could also make sophisticated analytical technologies more accessible in fields including health, environmental monitoring, food, materials, and sustainability.

The hardest part, though, may be deciding what those systems should optimize. “To me, the central challenge is defining what we want,” Bob says. “What does ‘good enough’ mean when the goal may be quantification, classification, discovery, comparison, compliance, diagnosis, or understanding?” He argues that many laboratories already possess more instrumental sophistication than they can fully exploit; the greater need is to define objectives, structure knowledge, combine data reliably, and validate decisions.

That is also why Bob questions the idea of creating a “ChatGPT for chromatography.” “A more advanced method is not automatically a better method,” he says. “A method is only powerful if it answers the right question, with the right level of confidence, for the right context.” Rather than replacing chromatographers, AI exposes the tacit knowledge, intuition, and poorly defined objectives on which the field has often relied. “In that sense, AI is not only a tool for automation; it’s also a mirror.”

Bob’s career has developed by following analytical problems beyond the instrument and into chemometrics, software, data science, and AI. “That was not because I wanted to leave chromatography behind, but because I wanted to understand what chromatography could become if we connected it to the right computational tools,” he says. His team continues to shape that thinking: “Good science is not produced by surrounding yourself with people who agree with you. It comes from creating an environment where people feel responsible for the work and confident enough to disagree.”