Interview: Miriah Meyer, Univ. of Utah on the Art and Science of Visualization

We discuss insights from the best paper at ACM AVI 2014, increasing interest in visualization, infographics, trends, challenges, advice and more.

Miriah MeyerMiriah Meyer is a USTAR assistant professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute. Her research focuses on the design of visualization systems for helping researchers make sense of complex data. She obtained her bachelors degree in astronomy and astrophysics at Penn State University, and earned a PhD in computer science from the University of Utah. Prior to joining the faculty at Utah Miriah was a postdoctoral research fellow at Harvard University and a visiting scientist at the Broad Institute of MIT and Harvard.

Miriah is the recipient of a NSF CAREER grant, a Microsoft Research Faculty Fellowship, and a NSF/CRA Computing Innovation Fellow award. She was named both a TED Fellow and a PopTech Science Fellow, as well as included on MIT Technology Review's TR35 list of the top young innovators and Fast Company's list of the 100 most creative people. She was also awarded an AAAS Mass Media Fellowship that landed her a stint as a science writer for the Chicago Tribune.

Here is my interview with her:

Anmol Rajpurohit: 1. I would like to start with your paper “Reflections on How Designers Design With Data”, which won the Best Paper Award at the ACM International Conference on Advanced Visual Interfaces 2014. Congratulations! How and when did your team come across the idea for that paper? What were the key insights from that research study?

Miriah Meyer: The idea came about when a co-author and I started sharing notes about conversations we were having with graphic designers. We both were surprised to learn that designers largely were creating infographics in Illustrator and drawing visualizations by hand. As computer scientists, we thought this was a terrible thing when we have so many tools for automating this sort of thing! We wanted to learn more about why designers would go through this manual process, and how it effects their designs. So, we developed some studies and conducted a series of interviews.
What we found was really interesting. First, manually creating visualizations isn't always a bad thing for designers -- often this process served as a way to get into the data and explore. Second, our observations showed some really interesting short-comings in current tools and provided some insights how to create new software systems that could support designers more effectively.

AR: Q2. Amid the Big Data buzz, do you think Data Visualization is getting the appropriate attention? How has the recently increasing interest in Big Data and Cloud impacted Data Visualization?

MM: Definitely. So many people now are aware that visual representations are great for summarizing complex data and helping us to see unexpected things. Unfortunately, though, there is still a big misunderstanding about what a visual data-visualisationrepresentation can give you (and what it can't). Many people I talk to that are struggling to make sense of their data think that if they could *just* visualize their data in some new way then insight will magically happen.
The reality is that there is a ton of hard work to do before even thinking about the visual representation -- the hardest part is articulating what the specific questions are that someone needs to answer from the data, and how to structure (or restructure) the data to support those questions.

AR: Q3. Since past few years, infographics have become widely popular as a story-telling tool for data-based reporting. How has this impacted the interest and innovation in the field of Data Visualization?

MM: A whole subfield of visualization has popped up that focuses on telling stories with data. It is quite interesting -- how do you tell a linear story while giving the user tools to explore data in a nonlinear way? What are the important elements of an infographic that brings a story to life, and how can we automate that? Can we take manually created visualizations (such as this one: and automatically produce them with different data sets? These are some of the interesting questions that people are now exploring.
AR: Q4. How has Data Visualization evolved over the last few years? What key trends do you expect to dominate the next 2-3 years?

MM: There is a trend to look at how we can apply existing visualization techniques to real-world problems, basically taking visualization research into the wild. I expect this trend to continue, along with seeing more of a marriage between visualization techniques and statistical methods.

AR: Q5. What are some of the most underrated challenges of data visualization?

MM: One interesting challenge is how to quickly prototype with data. We know from design that creating rapid prototypes, or "failing fast", is really important for discovering novel and effective ideas. In visualization this is really challenging to do if you need to include real data in your prototypes.

AR: Q6. Among the big gamut of visualization tools available in market today, which ones are your favorite? What features are most important to you when selecting a visualization tool? processing

MM: I'm probably not a good person to ask about this because I prefer to code rather than use a tool! For creating visualizations programmatically I'm a huge fan of Processing. Processing is a fantastic environment that abstracts away many of the annoyances of graphics programming. It also has been used extensively in the design community so there are loads of interesting projects out there.

AR: Q7. What motivated you to work on data visualization?

MM: My first love has always been science, and I saw visualization as way to enable science while getting to build cool things. I have grown to love what I do because it lets me learn about many different fields from people at the fore-front of those fields. For someone with a short attention span, visualization is great!

AR: Q8. What is the best advice you have got in your career?

MM: Learn to follow your gut. This is surprisingly hard!

AR: Q9. What advice would you give to students and researchers aspiring for a successful career in data visualization?

MM: Become a data scientist that understands that there is a living, breathing human-being on the other side of a computer screen. People that can reason about data while also being empathetic to the needs of data consumers are a rare breed. bone-clocks

AR: Q10. What was the last book that you read and liked? What do you like to do when you are not working?

MM: I don't remember the last time I read a book as I have a nine-month old at home. But I did recently purchase The Bone Clocks from David Mitchell because I loved reading Cloud Atlas. I hope to get to read it sometime soon. In my spare time, I'm working on keeping my crawling baby out of trouble.