5 Minutes with Director Richard Bonneau
Director of the NYU Center for Data Science discusses his exciting vision for the year ahead
Just twenty years ago, Richard Bonneau was a PhD student working as part of the team developing Rosetta, a platform that is now one of the world’s most powerful computational tools for predicting protein structures from genomic sequences, and for helping thousands of biochemists design new life-changing proteins.
Since being appointed as a professor of Biology and Computer Science at NYU in 2005, he has remained a core member of Rosetta’s development, established (and continues to direct) the Bonneau Lab and NYU’s Social Media and Political Participation lab (SMaPP), been selected as one of the top 20 scientists under 40 by Discover Magazine in 2008, and been appointed as a group leader at the Simons Foundation’s Flatiron Institute for scientific research.
Now he is the Director of the NYU Center for Data Science (CDS).
What is his vision for CDS? How does he use data science for his research in computational biology? And, how did he end up in this fascinating field to begin with? Read on to find out!
1. What is your vision for CDS?
One of the things that I like most about CDS is its cognitively diverse culture.
I’m inheriting a really multitalented department. For example, I’m a computational biologist, but Arthur Spirling (our Deputy Director) is a political scientist. We also have computer scientists, psychologists, and linguists — the list is endless.
But what unites us as a Center is our shared skillset in data science, which we use to solve key problems in our domains. Since data science is our joint “language,” so to speak, we have the exciting opportunity to establish more cross-disciplinary collaborations than other departments. Part of my vision, then, is to keep developing this cognitively diverse culture, and this will involve hiring more top-notch faculty and recruiting strong students.
I’m also hoping to expand on CDS’s human diversity. Ensuring that there’s a balanced gender and racial representation in any STEM field is an ongoing challenge, but we’re lucky in that Data Science is a field that naturally attracts applicants and researchers from a range of backgrounds (especially because of the lucrative employment opportunities that await data scientists when they graduate). As a result, our applicant pool is quite diverse.
2. How has data science been involved in your past or current research?
Data science has been a large part of my research since I started working in biology as a doctoral student. Back then, we worked on a smaller scale. In genomics research, for example, we started with annotating small sets of genome sequences, and building detailed models.
But as new technologies were invented, scientists were able to go from single data points to collecting millions of data points. Suddenly, we could annotate thousands of genomes at a time. We’re typically taught that the life sciences are not data intensive — but that has certainly changed now. The rate at which we gather data in biology has gone up by a log unit every eighteen months for the last twenty years of my career.
3. Why did you decide to do the work that you’re doing now?
My grandfather knew how to identify every species of edible plant within a thousand mile radius. He was a naturalist, and taught me how to be in awe of nature.
But he was also a machinist, and had one of the most incredible machine shops that I had ever seen, where he would fix, craft, and build. So I was taught to be mechanically inclined very early on, but also encouraged to embrace nature.
As I grew up, I gravitated towards biology and became interested in genes.
Which gene mutations cause various diseases? Can they be stopped? What genes improve drought tolerance in crops, and how can we build on that for the agricultural industry? How do genes interact to compose living systems?
By the time I was twenty, I had worked in several biolabs and knew that although my skillset was computational, my questions were primarily biological. Computational biology is really the only field for this particular combination of skills and interests, so that’s where I work now.
4. Who is an inspiring figure in your life, and why?
There are all kinds of pioneers, but the people I look up to most are the ones who pursue their own vision or experiment or method without knowing whether it’s actually going to work out or not — but they go for it anyway.
A good example is Barbara McClintock. She worked on corn, and without the genetics tools that we have today, she scrutinized chromosomes through a microscope and devised numerous theories, that turned out to be true, about genetics and epigenetics (before the current epigenetics craze).
While there’s no hard and fast rulebook on how to produce curious thinkers like McClintock, I think one of the best things for encouraging that kind of intellectual growth at CDS is to continue cultivating a stimulating environment where our faculty and our students have the resources to conduct their work, the time to think, and the space for them to collaborate and inspire one another.
Interview by Cherrie Kwok
