Computer Vision News - January 2017

If you could give advice to a young, female scientist who is starting her academic career, but is having a tough time and thinks to give up, what would you tell her? I think there are two things. One is to persevere. It’s tough. It’s not easy, but the keys are perseverance, patience, and resilience . For that, you need your private, support network. You need to have a listening ear at home, where you can talk about your problems. You need someone to talk to privately. The other aspect which people recognize more and more is mentorship . An academic mentor isn’t your closest colleague: your support person is a colleague around you who can also lend an ear, give you advice, and go with you to a person you may have problems with. Without colleagues, you can’t do it very well. That helped me a lot as an early academic. I had an official mentor, but I also had someone who picked me as a “mentee”: Professor Sir Mike Brady at Oxford , who kind of adopted me a little bit. I think what was great about him and my other academic mentor is that they both had daughters my age. They could see it from my perspective as well. Also my head of department had daughters, so even though we worked in hardcore engineering, I could look a little bit beyond that: they also had a private life, they are humans as well. I hear very different definitions of computational imaging. It seems that the concept is still unclear and not uniformly understood by everyone. What is your take on that? I wouldn’t interpret the term too much. It encompasses image processing, image analysis, and modeling, but it also looks at the imaging itself and the underlying imaging physics. It brings image analysis and machine learning into the image acquisition process. I would also call that computational imaging. For example, we just started on a new project between King’s and Imperial College with colleagues from Oxford and Barts/Queen Mary University where we want to basically build a smart MRI scanner. It’s like a push button approach where the images would be acquired in an intelligent way. The image analysis and the machine learning would already be in the acquisition and reconstruction. It’s a fully integrated approach. It takes a very interdisciplinary team to work on that: physicists, clinicians, computer scientists and engineers. For me, that is the perfect example of computational imaging . Computer Vision News Women in Computer Vision 25 Women in Science “ That is the perfect example of computational imaging ” If you look at women in science, you get a patchwork of people. There is not one single way that is the right one because everyone is different as well. Not all female academics are mothers. There are some who are not parents for whatever reason. It’s important that they get represented too because they have different needs and different career problems. “ Perseverance, patience, resilience ”