Sohini Ramachandran discusses her research in computational biology and population genetics and what makes the Data Science Institute at Brown special. 

Note: This interview has been edited for clarity. 

Who are you and what do you do at the Data Science Institute (DSI)?

Hi, I’m Sohini Ramachandran. I am a Professor of Biology and the Founding Director of the Data Science Institute at Brown University. I have been at Brown my entire faculty career. I came here after my postdoc, and this is my 15th year on the faculty. 

What have been your roles at DSI and CCMB?

When I got tenure, I was hired through the Center for Computational Molecular Biology, one of the centers within the DSI. At the time, the Directorship for the Center was open, and I ended up taking that on. I loved shaping the faculty community at CCMB; I had the opportunity to chair the search committee that hired our colleague, Ritambhara Singh, and I, along with multiple colleagues, secured our T32 training grant from the NIH in biological data science. I enjoyed helping to build an interdisciplinary community at CCMB while I was the Director there. 

In 2020, when the Data Science Initiative directorship opened up, I was interested because it felt like a chance to continue shaping a new and emerging discipline at Brown. I was the Director of DSI when it became an Institute at Brown in 2023, though I’ve since passed the torch on to the current DSI Director. 

What do your research focus on?

My lab’s fundamental research is on both inferring deep history from the human genome and predicting complex trait values in individuals, while also highlighting the challenges of doing so and identifying genomic targets of selection. We develop a variety of methods for this work, drawing on classical population genetic theory, mathematical modeling, and increasingly, machine learning.  

What are some research projects you are currently excited about?

One research project I’m especially excited about is with my graduate student, Cole Williams, in computational biology, focusing on interchromosomal phasing. Phasing is the process of figuring out which parts of an individual’s genetic data came from which parent. Typically, algorithms do this chromosome-by-chromosome, which means we can’t link information across chromosomes. Cole developed a new algorithm that uses relatives and large datasets to determine parentage across chromosomes. This has many potential applications, such as determining which parent a specific type of ancestry came from. That ability could reshape how we interpret recent history in the genetic datasets we already have. 

We’re also interested in how genetics research is consumed by the public. For example, how direct-to-consumer genetic testing shapes people’s ideas about genetics and ancestry, and how genetics is represented in public resources like Wikipedia. Genetics has long been used to discriminate or create inequities, especially in the United States. While that history continues, genetics now plays a growing role in healthcare. We hope for personalized medicine in the future, but I’m interested in whether that goal can be achieved equitably. 

I’ve also begun a collaboration with Suresh Venkatasubramanian through the Center for Technological Responsibility with graduate and undergraduate students, focusing on genetic privacy and the need for guardrails to protect individuals if their genomic data is compromised. The challenge with genetic data is that a breach affects not only the individual but also their ancestors, relatives, and future descendants. This makes it a fascinating problem in the realm of relational privacy. In the U.S., this area has very little regulation. We aim to help policymakers identify blind spots that could lead to discrimination, for example, in setting life insurance premiums where genetic data and pre-existing conditions are largely unregulated.  For me, this has been an exciting new area where the science of population genetics and the scale of data available today provide new ways of thinking about the implications of genetic technologies being widely accessible. 

As a former Director of DSI, what do you see as DSI’s role at Brown?

Conversations around the emerging discipline of data science parallel my discipline of bioinformatics. When I went to graduate school, it was right after the draft sequence of the human genome was published, and “bioinformatics” was the new buzzword. Everyone knew the term, and there were new PhD programs and departments; however, no one fully understood what it could become, only that it held a lot of promise. 

In 2020, when I stepped into the role of Director of the Data Science Initiative, I saw data science in a similar place. Today, people recognize its potential, but there is still a lot of work to do in terms of structuring it. It touches all disciplines, and Brown’s approach to building interdisciplinary units like centers and institutes makes it an exciting place for such work. 

I am proud of the way DSI supports curriculum across the university, both through the Data Fluency Certificate and the Data Science Fellows program. In the latter, undergraduates work with faculty to bring data science into courses across campus. We have worked with at least 40 departments so far, with fellows acting as consulting partners to help integrate data science into assignments or classroom experiences, while keeping domain knowledge at the forefront. I think this is when data science is at its best, in close conversation with the context of the data and its interpretation. That exchange is happening in classrooms across Brown, and it’s something I’m excited to continue fostering. 

I see DSI’s role as being a connector and supportive partner to units and disciplines at Brown. We want people to see data, as well as the absence of data, as something valuable and exciting. By giving students and faculty the tools to analyze data, understand its origins, and see how it moves through a process, we help them become better scholars and more prepared to make a positive impact. Ultimately, I think of data as a shared language that can unite the Brown community, and that’s what DSI aims to contribute. 

What is the most rewarding part of your work? 

The most rewarding part of being a professor at Brown and, previously, serving as the DSI director, has been the people I interact with. I feel privileged to teach and mentor students, and to work with faculty and staff who inspire me. 

What’s exciting about Brown is how people here push boundaries and seek connections that might seem unlikely in more traditional settings. Every day, I learn from others, grow as a person, and contribute to conversations about scholarship, truth, and making the world a better place. 

What do you like to do for fun in your free time? 

When I’m not working, I’m an avid knitter. If you see me wearing something made of yarn, I probably made it myself. I also have three young children who are curious and fun to be with, a husband who is also on the Brown faculty, and a dog. We often bring our kids and dog to campus, so Brown truly feels like part of our home.