During my leaving certificate, I wasn't quite sure what to pursue in university. I decided to study psychology because I was interested in human behaviour and felt that no matter what career path I chose, it would involve working with people. Initially, I was drawn to mental health but also found myself intrigued by statistics and numbers, which led me to pursue a master's degree in data science. During this program, I interned at the Insight SFI Research Centre for Data Analytics with Mihael. It was during this time that I came across research on linguistic biomarkers of mental health. Mihael kindly agreed to supervise my thesis as we worked together to expand on this topic.

Specifically, I observed that existing research focused on detecting mental health issues from the perspective of a computer scientist, rather than using standardised clinical scales commonly used in psychology. Our work aimed to bridge these two fields. At the end of the thesis, Mihael asked me if I was interested in pursuing a PhD. While I wasn’t interested in a PhD, I was interested in the idea of setting up a company and bringing our research to the real world. With the support of Enterprise Ireland, we secured a commercialisation fund within the university to explore the possibility of building a company based on this research.

I strongly felt that while there have been significant advancements in the treatment of mental illness, early detection remained a significant challenge. People often didn't receive the necessary treatment until it was too late. According to The Lancet, the average diagnosis of depression takes eight years from onset. I saw the real potential for our research to provide a measurable way to assess one's mental health, similar to how the blood pressure monitor revolutionised the treatment of heart disease by providing an easy way to determine if someone needed support. I believe this technology could have a significant impact in the future, ensuring that those who need help receive it promptly and don't have to suffer in silence.