23 Computer Vision News Nadiya Shvai UKRAINE CORNER for the safety of tunnels have been reinforced a lot in France. We are working on the automation of the system to make sure that they are as fault-proof as possible. At the same time, they do not generate a lot of false alarms because a system that generates a lot of false alarms finally becomes not useful at all. What do you do there as the data scientist? My work really considers almost all the aspects of deep learning product development. Starting from the data collection to data selection, to supervising the data labeling, to model training and testing. Then, we put the deep learning models into the pipeline and finally prepare this pipeline for deployment. This is additional to the other research activities that we do. Is this what you wanted to do when you studied? Or was it an opportunity that came to you, and you took it? [Thinks a little moment] It's an opportunity that came to me, and I took it. This has more or less been happening throughout my professional path. I think it's normal for opportunities to come our way, and it's important to recognize them and grab them. Recognize those that speak to you, that are close to your spirit. During your studies, what did you think you would be doing when you grewup? Ahh, it's a very good question! Thank you. I didn’t come for nothing. [both laugh] Well, deep learning as a mainstream activity is relatively new. It comes from signal processing, but this was not my specialization when I was studying. At the core, I'm a mathematician. You can think about this as being relatively far from what I do because I was doing linear algebra, and my PhD is also on linear algebra. But then, slowly, I drifted towards more applied skills, which is how I came to where I am today. So it's not true that women are weaker in mathematics, or are you special? “Every day brings us closer to victory.!” BEST OF ICCV 2023
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