Computer Vision News - March 2018

30 the amount of data that we have nowadays. At that time, it seemed a bit hopeless to develop some learning algorithms given the amount of data that we had. Maybe we had 13 subjects and 13 medical volumes. That was definitely not enough so that’s why during my PhD, I shifted my research direction toward more general computer vision. Then I saw it as a good opportunity when I started my postdoc. I decided it was a great opportunity and I learned a lot. What I enjoyed the most about working in this intersection between domains is that machine learning is a very cross- disciplinary field. It allows me to look around and see the problems that exist in a field that I don’t know much about. I can learn a lot from the important problems, and also about things I didn’t know in biology or the medical domain. You speak mostly about your work as a researcher and haven’t mentioned much your role as a teacher. Do you feel more attracted to research rather than teaching? My career has been focused on research mainly because I went through a PhD. As a postdoc, it’s true that you change your role a bit. You’re still learning a lot, but you also become a kind of mentor to your students. I was also invited to Barcelona this year and last year to give some lectures. I was just accepted as an adjunct professor at McGill University so it’s something that I just started. I’m looking forward to working with Master’s and PhD students, but it’s something that I haven’t explored that much yet. Adriana Romero Women in Science Computer Vision News “ If I am a researcher at Facebook today, it’s because I was lucky throughout my whole path”