“Untargeted data can be revisited years later to answer questions that did not exist at the time of acquisition. That turns every dataset from a one-off deliverable into a lasting resource, and I think the cumulative effect on analytical science will be larger than any individual technical advance.”
What does your research focus on, and what problem are you trying to solve?
Untargeted mass spectrometry of small molecules produces more data than researchers can interpret, and too much of their time goes into handling software rather than answering scientific questions. My team develops computational methods and open infrastructure through MZmine so that data processing stops being the bottleneck and scientists can focus on the science.
What aspect of your current research excites you most – and why is this an important moment for it?
What excites me most is a step most people find unglamorous. Every year, instruments get faster and their data more insightful, and every year the gap between measurement and interpretation grows. Large-scale AI is a promising way to close it, but it depends on data that are comparable between labs and instruments, and most mass spectrometry data are not. Because MZmine is vendor-agnostic and open, it is where raw vendor data become structured and reusable. That is what we are now building on with FAIR-MS, applying our processing capabilities to make mass spectrometry data findable and reusable at the scale that machine learning actually needs. This is the moment it matters because the models are finally good enough to use the data – if we can hand it over in a usable state.
Looking five to 10 years ahead, what emerging trend do you think will have the greatest impact on analytical science?
Untargeted measurement becoming the default rather than the exception. Targeted assays were trusted because they were manageable, and untargeted work was exploratory. Software has now caught up with the data volumes, so that trade-off is disappearing. Targeted methods will remain essential wherever a validated answer is required, but they answer the question you designed them for and nothing else. Untargeted data can be revisited years later to answer questions that did not exist at the time of acquisition. That turns every dataset from a one-off deliverable into a lasting resource, and I think the cumulative effect on analytical science will be larger than any individual technical advance.
What is the biggest challenge facing analytical science today?
Analytical science is still widely treated as support rather than as a scientific contribution in its own right. The analytical step is often where the real measurement problem sits, but in project plans it appears as support to be delivered rather than a question to be answered. The consequences follow from there: method development is undervalued, analytical expertise is counted as a cost, and the software the field runs on is funded as a research byproduct rather than as infrastructure. That is a recognition problem, not a technical one, and it limits what the field can do.
Do you have any strong opinions with which the rest of the field tends to disagree?
The quality of the whole analytical workflow determines everything, and no single part of it can rescue the rest. Study design, sampling, sample preparation, acquisition, and processing: each one caps what the others can achieve. This sounds uncontroversial, but the field is currently behaving as though it were not true. The assumption behind much of the enthusiasm for AI is that enough data and a good enough model will compensate for what happens upstream. Scale is a good remedy for random error. It is no remedy at all for systematic error, and the errors that dominate real analytical workflows are systematic: matrix effects, ion suppression, batch drift, degradation during storage, and so on. A model may not average those away; it learns them as signal. My concern is that this failure is quiet. Poor data do not announce themselves in a model’s output, and confident answers are harder to question than obviously unstable ones.
What decision, opportunity, or unexpected event has had the greatest influence on your career so far?
Founding mzio. What makes it the most influential decision is not the idea, which I already had at the end of my PhD in 2020. It is that the idea alone was not enough. The conditions were not right: MZmine was not yet at the stage it needed to be, and my future co-founders were at points in their careers where a startup made no sense, as they were starting a PhD or a postdoc.
So I joined Bruker Daltonics instead, and that turned out to matter enormously. I learned how a major player in this market actually operates, and I was given responsibility, freedom, and decision-making power early, which taught me more than any other part of my training. I remain grateful to my managers there for that.
By 2023, things had converged. MZmine 3 had been published in Nature Biotechnology, community interest was growing quickly, and Robin Schmid, Steffen Heuckeroth, and Tomáš Pluskal were all in positions to take it on. In early 2024, we founded mzio. I could not have done it without the three of them. Between them, they bring the scientific depth, engineering ability, and community standing that mzio actually runs on. The lesson I took from it is that the founding team and the timing matter more than the concept, and neither can be forced.
Who has changed the way you think about science – or life more broadly – and how?
Two people at the University of Münster, at different stages. As a chemistry student, I took Uwe Karst’s lecture on modern instrumental analytics, and that was the moment the field stopped being something on my timetable and became what I wanted to do. Heiko Hayen then gave me the chance to work on mass spectrometry in his lab, and when my interest moved toward the computational side, he supported it. That was not the obvious direction at the time, and having a supervisor who made room for it shaped everything that followed. One of them made the field compelling; the other made space for me to pursue it in my own way.
What interest or skill outside science has made you a better researcher?
Sport climbing and running. I climb for the challenge and the fun of solving a complex problem, and it is the one activity where I think about nothing else. I run because I like to compete, and because a race result is not open to interpretation. Ideas I have been stuck on tend to arrive on a long run.
Ansgar Korf is CEO and Co-Founder of mzio GmbH, Germany.
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