Sabine Süsstrunk is a full professor and Director of the Image and Visual Representation Lab in the School of Computer and Communication Sciences (IC) at EPFL since 1999. From 2015-2020, she was also the first Director of the Digital Humanities Institute (DHI), College of Humanities (CdH). Her main research areas are computational photography, computational imaging, color image processing and computer vision, machine learning, and computational image quality and aesthetics.
She has a BS in Scientific Photography from ETH Zürich, Switzerland, an MS in Electronic Publishing from the Rochester Institute of Technology (RIT), Rochester, NY, USA, and a PhD in computing chromatic adaptation from the School of Computing Sciences, University of East Anglia (UEA) in Norwich, UK. From 2003-2004, Sabine was a Visiting Scholar in the Computational Color Reproduction Group at Hewlett-Packard Labs in Palo Alto, CA, USA. From 1995-1999, she was the Principle Imaging Researcher at Corbis Corporation in Seattle, WA, USA. From 1991-1995, she was a Visiting Assistant Professor in the School of Photographic Arts and Sciences at the Rochester Institute of Technology (RIT). She has authored and co-authored over 200 publications, of which 7 have received best paper/demo awards, and holds over 10 patents. She served as chair and/or committee member in many international conferences on image processing, computer vision, and image systems engineering.
Since 2021, Sabine is President of the Swiss Science Council (SSC). She is also a Founding Member and Member of the Board (President 2014-2018) of the EPFL-WISH (Women in Science and Humanities) Foundation, a Member of the Board of the SRG SSR (Swiss Radio and Television Corporation), and a Member of the Board of Largo Films. She received the IS&T/SPIE 2013 Electronic Imaging Scientist of the Year Award for her contributions to color imaging, computational photography, and image quality, and the 2018 IS&T Raymond C. Bowman and the 2020 EPFL AGEPoly IC Polysphere Awards for excellence in teaching. Sabine is a Fellow of IEEE and IS&T. Source: Sabine Süsstrunk EPFL
Keywords: Computer and Communication Sciences, Computational photography and imaging, Color imaging processing, Computer vision, Machine learning, Image system engineering
Milan – October 14th, 2022
How did you (decide to) become a scientist? It is difficult to get across because it sounds discouraging and I don’t mean it that way, but to a certain point, I was lucky enough with my parents. I was the fourth child. By the time I came along, they said “do whatever you want to do”. My mother was a politician. Switzerland gave the right to vote and to get elected in 1971. In 1972, she was a politician and my father always supported her. We weren’t rich, my father was an engineer. It was a typical middle-class family. It drove my parents crazy when friends of theirs told their daughters: “you are not going to study because you are going to become a mother anyway”. That still happened when I grew up. At a certain point, I knew I had the freedom to do whatever I wanted, and that is a baggage that not everybody has. That made a lot of my things easier. Support, it doesn’t have to be money, is very important.
I studied photography. I was interested in photography but I decided to do a bachelor at ETH Zurich, where there was [only] one position to study scientific photography per year. I couldn’t get excited by anything else, so I applied and interviewed but I could start only in three years. Afterward, I realized this was a way to see if people would stick with the study or not. So, first, I worked and traveled around the US. Then, I did my first year in chemistry at EFPL. I chose EPFL because where I grew up was in between Zurich and Lausanne and my French was lousy, Lausanne has a nicer lake, and the people are much friendlier in Lausanne than in Zurich. I cannot say that I even wanted to become necessarily a scientist. I actually also know that I would never become a teacher. Afterward, I went to Zurich and during that time I really did a lot of photography. I had exhibits, I did photography posters for a modern dance company, and I won awards, but I actually studied the chemistry and science of photography too.
There was one moment when I had to decide, after my bachelor, which direction I would want to go, because I had the baggage of doing the science of photography but I also loved photography. And then I decided. At that point in time, there were so many people of talent, but to really make a difference you really had to knock on the doors and to make that they opened them or you had to have enough money yourself to do this as a hobby. I also liked to do research, and I found it just as creative. You have to be creative and have new ideas [to do research]. I thought I could always take photographs but I would have more chances to really be successful in a creative area, to do something very creative if I’d go into sciences. In the end, a photograph is a subjective experience, everybody can love or hate it and if they don’t want you, they hate it, right? One plus one is equal to two, independently if a woman does the math. To a certain point, there is or was less bias in the sciences (even though we can talk a lot about biases in the sciences) because in the end, you can say ‘that’s my proof, it works -you can think about women whatever you want, but you cannot question my result, because I can prove it worked’. It was kind of a conscious thing: science.
What is your drive and excitement in science and in doing what you do now? Science is so incredibly creative, and research is so incredibly creative. You think about [an issue] and then you come together, you discuss. It’s very much a team sport, you have to discuss it with other people. My main drive is curiosity. Why doesn’t this work, can we make this work? In the end, I got stuck with photography. All the research we do is around imaging and computational photography. So, I could not let it go totally. One of my successes is that I do know photographs. When you do research in photography, finding quantitative methods is very difficult because when you have a processing chain from the physical signal you capture to a nice photograph, there is no metric you can do, except for the human eye. I did quite well in my research because when I look at photographs I see when something is wrong. You would not believe how many computer scientists working in this field today do not have this ability; they cannot look at an image and say ‘what’s wrong here?”. You need to realize it when something is wrong in your algorithm and cannot be true. Having a photographer’s eye helped me in my research.
Would you have one word to give as a gift to other women and more in general to young aspiring scientists, women or men? Just don’t give up, continue, continue, do not give up. Do not let somebody else tell you what to do and when to stop. You make that decision.
Science is my passion… in your mother tongue. Ich heiße Sabine Süsstrunk and Wissenschaft ist meine Leidenschaft
