MICCAI 2019 Tuesday

MICCAI 2019 DAILY Women in Science it in Lagrangian optimization, which is the basis for the constraint optimization. Then we motivate a shift toward an easy adaption, and we prove that it’s very successful. Are you Canadian? I’m French, but I decided to go to Montreal for my PhD. Tell us something about Canada that we don’t know. Montreal is very vibrant. It’s a very big hub for machine learning. You have a lot of universities in a small town. There are students everywhere. It’s not like where I come from. I come from Paris, which is a huge city. Paris also has a lot of universities! In Montreal, they are very close by, and people know each other. You have very different and very good overlaps in AI, and in AI for medical purposes. The general vibe is that everyone is an immigrant, a newcomer. It is very inclusive. They pay a lot of attention to women, to minorities. It’s a place where you can come from a foreign country and feel wide acceptance. Being bilingual also helps. I think most people are bilingual, or trilingual, in Montreal. This is something that I find completely amazing. Can you think of a teacher who taught you something particularly important? My background is in France. I was lucky to do a very good Master ’s in mathematics, vision, and learning. The teachers there were all very amazing. They were experts in their field. It’s very interesting because it’s an old Master ’s, the oldest Master ’s in, what we call today, AI. It was people who have been in the field for years, who have learned to adapt their career forward. Apart from their brilliance, they have taught us to be careful of the hype, to try and diversify what you know, to be curious, not just focus on what the hype is doing, to try to have very good understanding, to criticize what you know, and then persevere. Now we are in the middle of a hype. How can we not be caught in this hype? That’s a good question! That’s my job! [both laugh] I think it’s good to have a very optimistic view. One of the good things about working in this field is the optimism. We also have to be critical. We also have to learn to integrate people from different specialties and 12