Interview with Dr. Raga Krishnakumar

I recently had the chance to interview Dr. Raga Krishnakumar- a computational biologist with Sandia National Laboratories. Apart from studying genomics and bioinformatics, she mentors young scientists and is a passionate advocate for diversity in STEM. We spoke about her background, her experiences as a biologist, and her opinions on representation in STEM.
Tell me a bit about what led you to join computational biology.
“I think I’d credit my highschool bio teacher with inspiring me to join the field,” she says. The nudge towards biology took her from Switzerland to the UK, to pursue natural sciences. “I got more interested in biochemistry- specifically, the regulation and expression of genes, which I learnt more about in my further studies here in the US. My post doc, however, came at a really interesting time.”
There was an explosion of big data and its application in science, and she realised it was the future. “I personally think computation is going to become an integral part of all sciences – one of those things you don’t really question. Funnily enough I never actually received any formal training in this area – I just learned it alongside my degree!”
Why is big data important to biology?
“Biology is very hypothesis-driven,” she explains. “There are a lot of specific questions and specific experiments conducted to answer them, which over time have generated LOTS of data.” Apart from her interest in the field, she thinks data analysis is a great way of making sure this data isn’t wasted. “It’s about the shift from data generation to data interpretation. Analysis helps me revisit data, and look below the tip of the iceberg to make some very interesting conclusions. This sort of intersectional work definitely gives me an edge.” She believes growing interdisciplinary fields have a synergistic effect, bringing subject matter experts together and helping them work better. “I’m really excited for the convergence of AI and biology! It is a nascent field, because biology is so unimaginably complicated, but we can develop algorithms to help decipher this complexity. Machine learning and predictive biology are going to revolutionise the subject.”
Speaking of interdisciplinary work, here’s some really interesting research by Harvard Medical School on paper authorship1. It finds that there are significant differences in gender disparity between fields – for example, biology is relatively gender-balanced, while computer science is quite male-dominated. Interestingly, computational biology falls in the middle. Why do you think that is?
“Oh, ‘male’ and ‘female’ subjects definitely exist- and they stem from the little things.” It starts early – childhood experiences and treatment of subjects in schools have a divergent ripple effect, and by the time we come to grad school, these ideas are already ingrained. “Any action we take to combat this needs to begin at school, too.” She tries to volunteer at schools, and teach people across genders that they are capable of excelling in any field. “It really matters to have representation, to see people like you in the positions where you want to be.”
This follows into the workplace. Despite the fact that the number of women joining the field is increasing, what is known as the ‘leaky pipeline effect’ occurs, in which fewer and fewer women are able to make it to the top. Any thoughts on why?
“The cause, again, would be the little things. For example, women, on average, are more heavily impacted by having kids.” She believes we need to work on providing societal support, and addressing the issue on multiple levels. “There was a really interesting lecture I attended that talked about how, at university, men tended to pass physics more. It showed small changes that helped women pass their class – it was found that removing the student’s name from their paper, and offering a simple prep course, helped their pass rate. It really tells volumes about the small, population-level biases we hold.”
Let’s talk solutions – one potential idea is increasing the number of women mentors in the field. The Harvard study mentioned earlier found that “the presence of senior female scientists appears to lead to less disparity overall.” You’re involved with programmes such as the Junior Academy and STEM-U as a mentor- what are positive experiences you have had, and do you think this idea holds true?
“The important thing to remember when it comes to a solution is that progress is slow because the issue is so complicated. That said, I think mentorship is a vital part of the solution.” Dr. Krishnakumar is involved with many mentorship programmes, such as the Junior Academy, a global problem-solving community2 of STEM students and scientists. “Being a mentor really opened my eyes to the impact I could have. The benefits of mentorship to the mentee are clear – in fact, the lack of a good mentor can be very damaging. A mentor could change the course of your life for good – just like my bio teacher! But mentorship is very much a two-way street, and is just as beneficial to me.” She says that she doesn’t view mentoring as a ‘donation’ of time – it’s passing on what she has learnt on a personal level. It also helps broaden her horizons, and makes her a better scientist.
“My go-to bit of advice: it’s my responsibility to open doors behind me.”
Do you think making mentorship systematic rather than voluntary could help?
“Absolutely! It could go a long way in encouraging scientists to mentor students, and better prepare the younger generation for the field. In fact, mentorship and education is something you need even when you’re older!”
Diversity is an intersectional issue – recently, the much-needed discussion on race in STEM is being brought to light. How important do you think it is to talk about these more uncomfortable topics and what action do you think we could take to actively combat the underrepresentation?
“Having uncomfortable conversations is critical.” She says that her workplace organises events where conversations about things like privilege are had, and they help her educate herself. She appreciates that these conversations are even being had at schools. “I’ve been very lucky throughout my journey. I’ve seen that these discussions are difficult for both parties – the ones privileged and the ones not. It’s also a complicated issue. We need to move on from brushing inconvenient data under the carpet – not all information is convenient, and we need to acknowledge and deal with it.”
However, she also thinks that there is genuine effort being made, and that needs to be acknowledged too. “There are people who want diversity for more than the tagline, and this makes me very optimistic. Employee-led advocacy groups being listened to is a great example of positive change.”
One of the effects of the COVID-19 pandemic has been the acceleration of the change in the job landscape. What is your advice for scientists in these times?
“The job landscape is definitely in a transitional period, with automation on the rise and a (forced) increase in remote work. Acknowledging this shift, and the associated challenges, is important. When I wanted to move to computational biology and get this new set of skills, I was lucky to be able to. We need to find ways in which we can make opportunities to keep learning and adapting more accessible.” She also stresses that this should be done across ages – we can’t leave people behind.
Finally, if there’s something you want to say to aspiring scientists and biologists, what would it be?
“Cliché as it sounds, I want to tell everyone that things really are changing!” She says that focussing on a static moment in the present, and looking at a seemingly despondent situation, can be scary, but we are always evolving. “More importantly, I would ask young people to engage in the change, even if it seems to be against hope. Reach out! This generation is very inspiring. Keep trying – what matters is the one email to a changemaker that gets answered, not the ten others left unread.”

  1. Stefan, Melanie I., Bonham, Kevin S. “Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.” PLOS Computational Biology, 2017.

Thank you to Dr. Krishnakumar for this interview!

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