Bruno Goncalves is a Fellow at NYU’s Center for Data Science, and a recent recipient of the CSS Junior Scientific Award. His interdisciplinary approach to social science combines data science with traditional research methods for a greater understanding of human behavior.
What did you study in school?
Originally, I was a physicist with a dash of computer science. I spent my undergraduate days studying statistical physics, a branch of physics that focuses on understanding how microscopic behaviors result in macroscopic phenomena.
How does your background in physics apply to your work in the social sciences?
For me, the common denominator between physics and the social sciences has been connecting macro and micro elements to solve problems.
When did you start to incorporate data science into your research?
From my early days as a physicist, I would write simulations to generate data that I would analyze separately. However, the real transition to data science was during my doctoral studies. I started working on complex networks using the “raw” data generated by web-servers to look at how users navigate through web pages.
How does data science connect with the projects you are currently working on?
Right now I’m using geolocated online data sets from sites like Wikipedia and Twitter to study how human mobility and social behavior are interrelated. And with some of the same data sets, I’m using natural language processing tools to look at how the use of language changes regionally, and how language evolves over time.
Can you tell me about your new book, Social Phenomena?
I think the subtitle, “From Data Analysis to Models” not only expresses the idea behind the book, but also the guiding principles of my research. The main question being: how can we use the ever increasing availability of large scale online data sets, coupled with recent developments in data science, to model and understand individual and societal human behavior?
What has been the biggest affect data science has had on the social sciences?
Data science allows researchers to harness large data sets on how we behave and interact; this gives us a holistic birds eye view of society. We’re complimenting our work in data science with more traditional social science studies that rely on in depth interviews and surveys of smaller groups. Combining both perspectives (the macro and the micro) is paramount for the future development of social science as a whole.
What drew you to the CDS program at NYU?
The richness and diversity of the program. Being part of an institution that hosts leading researchers from economics, politics, neuroscience, psychology, computer science, statistics, music, business, and beyond is a very exciting opportunity.
The Masters in Data Science program also has the advantage of attracting a lot bright students. I’m extremely proud to be a part of CDS and hope to be able to contribute, in some small measure, to its success during my tenure.
On a chalk-board at CDS we have the question: “What does it mean to be a data scientist?” Could you answer this question?
Over the past few years we’ve seen a lot of hype surrounding “Big Data” and “Data Science” so there are many ways to interpret this question. I prefer using unexplored and diverse data sets to answer larger societal and scientific questions, as opposed to only looking at data fitting a certain model.