How would you describe your work in 90 seconds?

What I’m trying to do is to make large and complex data more accessible and understandable for people. My team and I do this by designing, building, and validating visualizations and visual interfaces so that we can help people explore data interactively. This is one of the goals of my research, but there are a couple of challenges attached to this. One of them is that we need to find real-time solutions for visualization algorithms so that we can process and show the data in real-time. Also, we need to think about effective visual encoding, because we have a lot of data, and we don’t want to produce visual clutter. I also investigated guiding visual attention in complex visualizations to make people look at important parts that are displayed, especially for dynamic visualizations. We are also increasingly investigating how to incorporate Machine Learning (ML) into the interactive exploration process. We want to help people make sense of large and complex data. The methods for that are making appropriate visualizations and interaction design, building interactive prototypes that are effective and that work, and then also validating them with end-users from different domains, or just a general audience.

How did you get in touch with informatics?

I took some detours; it was really not very direct (laughs). I went to a high school with a focus on languages, so I had no natural science focus and no math focus. My primary interest in school was more in creative subjects like arts and music, and computer science or informatics was not on my radar at all. We had, I think, one year of informatics where we learned to switch on the computer and use Excel. But that was about it, so I didn’t really get in touch with computer science at that point, there was just no opportunity, really. Some of my friends took informatics as an elective. Interestingly, they were almost all male. I found what they did fascinating, and I asked some of them to show me what they had learned. That’s when I started scripting simple games and other things, so that’s how I got in touch with computer science. When I finished high school, there were many things that I found interesting, so I was considering my options, and computer science was not one of them. It was this big thing where I didn’t even know what it meant. In the end, I decided to study media technology and design because I felt that it was more focused, and I could learn something useful about technology. During my studies, I learned programming, and it was not love at first sight (laughs), but when I reached a level where I could build stuff and make things happen, I realized all of the opportunities that were now open to me. I went abroad and worked in research institutions in Germany and New Zealand, I went to conferences and presented my work there. We even showed some of the projects that we did during our master studies at the Ars Electronica, so there were a lot of interesting things going on, and that’s when I knew this is the direction I want to go into. So, I took quite a detour before I ended up here.

Where do you see the connection between your work and everyday life?

I think everyone is confronted with and is producing a lot of data, and so my research focus lies on helping users make sense of these data and helping them make informed decisions based on that. It’s also about giving people the feeling that their decisions are well-grounded and that they receive all the information that they need to make that decision. Increasingly important, of course, is the role of ML, so we are investigating how ML can support users in their decision-making process, rather than having ML make decisions for them, so this is important. Visualization, my research area, is also a very powerful explanation tool that can help people understand the inner workings of ML models and what they have learned from the data they have been trained on. One of the things we are currently investigating, for instance, is which biases these models have, how that influences their decisions, and which unexpected behavior they might have because of these biases.

What makes you happy in your work?

A lot of things. I think one of the things is that you’re confronted with a problem that seems straightforward but isn’t. It’s completely overwhelming at the beginning, but over time, a solution starts to crystallize. Producing things that are visual, where you can see the result, is something that is very rewarding, and you learn a lot on the way. There’s always something new, and the problems are never trivial. One thing that’s really fascinating about informatics when it comes to creativity is that we’re able to build entire products. We are in the driver’s seat from start to finish, and I think there are very few areas where you can contribute to so many different aspects of a solution as in computer science. That’s what makes it an interesting field for different types of people and an extremely creative area. It’s also important to pass this knowledge and experience on to the next generation, make them understand what you did, and lead them to a path where they themselves can produce visual results. Getting there, having students create interesting projects, that’s also something that’s rewarding and makes me very happy. And of course, getting papers accepted, presenting them at conferences, making people aware of what we do, and getting inspired by them.

Why do you think there are still so few women in computer science?

I think the problem starts quite early, and there’s still a stereotype of computer scientists that is not very attractive to girls, at least the way they have been socialized. Some of us are also celebrating this image somehow and emphasizing it, like this image of these extremely hard problems that not everybody can understand.This image is used to delineate ‘softies’ who are working with people and solving ‘soft’ problems. I think this is not appealing to girls, and they do not identify themselves with that image. Also, this image doesn’t necessarily represent a complete picture – computer science is such a broad field, and there are so many opportunities for bringing in different talents and perspectives. In that regard, I think that TU Wien is actually a very inclusive place. I never had the feeling that I have to work harder because I’m a woman. In my view, what we need to do is to show kids, no matter if they’re male or female, what we do and what problems we solve, and eduLAB is a good example of how this can be done. We need to show kids what we do not only in the field of computer science itself, but we’re also collaborating with other domains. We’re very often working with an interdisciplinary approach, so it’s also about showing how computer science contributes to solving tricky problems in other domains.

Manuela Waldner is Associate Professor at the Research Unit Computer Graphics at TU Wien Informatics. In her currrent projects Joint Human-Machine Data Exploration and Visual Analytics and Computer Vision meet Cultural Heritage, she explores innovative interactive visual analysis approaches to explore large data sets, such as historical media collections, and make them accessible to a wide range of users. The projects are funded by FWF and will continue until 2026 and 2027, respectively.

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