Evidence before assumptions
Start by understanding what the data can support and where it may mislead.
About
From biological form to computational questions
I began in biology and physical anthropology. Over time, the part of the work that held my attention most was how to structure difficult data, test an idea reliably and make the result understandable.
The questions became computational
My training began with biology, skeletal anatomy, human evolution and forensic anthropology. Working with bones and biological form soon meant working with coordinates, scans, surfaces and uncertainty: data that is rich, but rarely tidy.
Geometric morphometrics and 3D imaging gave me a way to turn that complexity into testable questions. They also made the quality of the workflow impossible to ignore. Segmentation, landmark definitions and observer decisions shape every downstream result.
From 2012 to 2018 I was co-responsible for the 3D department in the Laboratory of Physical and Forensic Anthropology at the University of Granada. I surface-scanned thousands of skeletal elements for my own work and for other researchers, working across scanner systems with particular depth in Artec Eva, Artec Spider and Artec Studio.
Research support is problem-solving work
In my current role at the University of Zurich, I move between data collection, CT segmentation, landmarking, analysis, figures, 3D warps, teaching material and methodological support. The official title is Technical Assistant; the functional role is broader: Research Technician and Scientific Data Analyst.
Much of the work happens at the point where a good research question meets an awkward dataset. I enjoy helping collaborators and students make the next step explicit—what needs checking, what analysis is defensible and how to explain it.
Why software is a natural next step
Repeated analytical problems invite reusable solutions. Outside my formal research role, I build applications and automated systems that turn recurring manual work into clearer processes. That shift is less a change of identity than a continuation of the same habit: understand the problem, preserve the important decisions and make the result useful.
I am moving toward data science, research technology and software-oriented work because those roles bring analysis, practical systems and interdisciplinary communication together.
Working principles
How I approach a problem
These are less slogans than checks I return to when the data or the project becomes complicated.
Clear visual communication
A strong analysis still needs a result that another person can read and question.
Reproducible processes
Important decisions should be explicit enough to inspect, repeat and improve.
Honest uncertainty
Limitations are part of the result, not a footnote to hide.
Tools for actual work
Build around a repeated need, not around a technology looking for a use.
Independent and collaborative
Take ownership of the technical work while making it easier for others to contribute.