Harley was the result of my new goal towards do a PhD in Molecular Biology. In May 2021 I started volunteering at the Buchan Lab at the University of Arizona to learn the ropes of a “wet” biology lab and started inquiring about the role of so-called stress granules under oxidative stress in yeast cells. Quickly the project became a quantitive microscopy project and over the course of the next 9 months we developed a full fledged machine learning application that could be trained to detect certain features in microscopy images of yeast.
Here I could leverage my UI and UX skills to create a Python and ReactJS based software that is in use by the Buchan Lab. Consequently we published a paper in Nature Scientific Reports: HARLEY mitigates user bias and facilitates efficient quantification and co-localization analyses of foci in yeast fluorescence images.
This is one of the projects that makes me very proud to have accomplished, as it combines science with the usability and “consumer grade” design – a feature often underlooked in scientific publications.