I’ve always been fascinated by biology and how we can study complex biological problems by measuring some property, analyzing the data, and translating the results into insights. In the early days of my undergraduate work studying bioinformatics at the Free University of Berlin, I approached this problem through ontological data modeling, but always felt we need more than well-structured and fast databases. After all, it’s ultimately people who assign meaning to data, so we need effective tools that help extract insights. Additionally, as humans, our vision system is the most effective channel to perceive information, so during my graduate work in bioinformatics, I started researching data visualization. But data visualization alone is not enough — in a world that’s drowning in data, we also need to extract, summarize, and present data in intelligent and efficient ways. Or, in other words, we need computational tools such as human-centered machine learning that guide visual data exploration and help us discover insights efficiently. This desire is what pushed me to pursue a PhD in computer science at Harvard where I worked on pattern-driven visual data exploration of genomic data.