5 minutes with Juliana Freire
“So, I flipped a coin, and computer science won.” For International Women’s Day, we catch up with the stunningly accomplished Juliana Freire
Juliana Freire is a Professor of Computer Science and Data Science. She is the Lead PI and Executive Director of the Moore-Sloan Data Science Environment at NYU CDS. After earning her Ph.D. in Computer Science from SUNY (Stony Brook), she joined the Database Group at Bell Labs. In 2002, she moved to academia, having held faculty positions at the Oregon Health and Science University and at the University of Utah before joining NYU in 2011. She has co-authored over 180 technical papers and received multiple grants from the NSF, DARPA, Gordon and Betty Moore Foundation, Sloan Foundation, Google, Amazon, Microsoft Research, Yahoo!, and IBM for her research projects. She is an ACM Fellow, and in 2017, she was elected as the first female chair of the Association for Computing Machinery’s SIGMOD group in its 42 year history. Her recent research has focused on large-scale data analysis and integration, visualization, provenance management, computational reproducibility, and web information discovery.
1. You have pursued computer science since you were an undergraduate at Universidade Federal do Ceara in Brazil. What (or, perhaps, who) inspired you to go into this field?
In Brazil, we have to choose a major before we take the entrance exam for the university. Since there are many medical doctors in my family, I was inclined to go to medical school. But I had just gotten my first computer, and since I always loved Math, I thought that Computer Science would also be a good choice. So, I flipped a coin, and Computer Science won.
2. When you reflect upon your long and accomplished career, what research projects or moments are you most proud of so far?
VisTrails was my first large-scale project and it has inspired many other research problems that I still work on.
Our goal in this project was to develop new methods and systems that enable domain experts to more easily construct the computational pipelines required in scientific exploration. We introduced many new concepts around provenance management, and in addition to authoring patents and publications (which include 2 best-paper awards), we built the VisTrails open-source system. VisTrails systematically captures and maintains detailed provenance (history) of the steps followed and data derived in the course of an exploratory computational task. Besides enabling reproducible results, VisTrails leverages provenance information through a series of operations and intuitive user interfaces that help users to collaboratively analyze data.
Since its initial beta release, VisTrails has had tens of thousands downloads. The system been adopted in several scientific projects, both nationally and internationally, in different areas, including environmental sciences, climate data analysis, psychiatry, astronomy, cosmology, high-energy physics, molecular modeling, and quantum physics.
Working on VisTrails was rewarding in many ways — not only did we find and addressed new computer science problems, but we also had practical impact in many different scientific domains.
3. Do you have any advice for young researchers in the data science field who are hoping to follow in your footsteps?
Young researchers have to find their own paths. To be successful at anything, you have to be passionate about what you do. So my advice is find your passion!