This year, the Complexity Science Hub celebrates its 10th anniversary. Throughout our anniversary year, we’re spotlighting one research topic every month. In August, it’s Data Visualization. In this interview, group leader Liuhuaying Yang shares a personal take.
If you had to explain what your group does to someone at a dinner party, what would you say?
We create interactive websites with visualizations to communicate scientific research results.
What are you working on right now that you can't stop thinking about?
I am currently pondering how to continue growing after being in the same role for five years. Over time, I have developed certain routines and ways of working. Some of them have helped me become more effective, but some have become automatic and may limit the way I approach new challenges.
What's the tangled part of this problem that makes it so hard to crack?
The tangled part is that this requires time and space beyond my daily tasks. But to create that space, I need to become more efficient and rethink some of my working habits, including how I prioritize, manage tasks, and make decisions. So it becomes a loop: I need time to improve, but I need to improve first in order to create that time. The things that help me move forward are also the things I need to rethink.
What pulled you into this field in the first place?
I was drawn to data visualization by the idea of making data more understandable and engaging through thoughtful design. I became fascinated, and I still am, by the challenge of bringing together mathematics, aesthetics, and design thinking to transform abstract data into something people can understand and connect with.
What is complexity, to you?
To me, complexity is like searching for the origin of a problem on a Möbius strip. A Möbius strip has no clear beginning or end, and similarly, complex problems often do not have a single root cause. The causes are connected and influence each other.
For example, imagine planning replacement buses. On the first day, planners look at the number of passengers and adjust the number of buses for the next day. But passengers also change their behavior based on their experience from the previous day. If the buses were too crowded, some people might choose another way to travel. This creates a feedback loop where planners influence passengers, and passengers influence future planning. Both sides are constantly reacting to each other, making the system difficult to predict.
What does a typical workday look like for you?
It usually starts with reviewing emails and priorities. Then I focus on ongoing projects. Depending on the project stage, I might be coding, sketching, meeting with colleagues, or presenting ideas to our researchers.
When you're stuck – what do you do?
What's the thing people get wrong about your field that bugs you?
Thinking our work is merely about making things look beautiful. For me, a certain level of aesthetics is fundamental. It’s like a dish, the appearance can make people want to try it ‒ but what matters more is whether it is nutritious and truly delicious. Looking good is only the entry point.
What would you love to have changed five years from now?
I hope people can see and better understand the world through the perspective of complexity science, and visualization is an instrument for this. However, I cannot achieve this all by myself. It requires bringing more people into the field, encouraging them to engage with and pay attention to complexity science.
This is why I started organizing the annual Visualizing Complexity Science Workshop a few years ago, bringing together people from different disciplines to exchange ideas and approaches. I also try to create connections beyond our team by collaborating with external partners, working with interns, and sharing our projects at various events to make complexity science more visible and accessible.
Find more information on this research topic here.
