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When Science & Tech Meet: A Conversation with Helix's Rani Powers

PowerToFly was thrilled to partner with Helix for a recent, sold out evening for San Francisco-based women in tech. (Check out photos from that event HERE).

One of our panelists at that event was Rani Powers, a computational biologist at personal genetics company Helix. We sat down with Rani to discuss her work at Helix, how continued education has shaped her views, her patent and about how her role lives at the intersection of tech and science.

Helix is hiring! Follow Helix on PowerToFly to learn more about their open roles.


Many folks may be unfamiliar with the term Computational Biologist. Can you tell us a little more about what this role entails and specifically what role you play at Helix?

Helix offers state-of-the-art DNA sequencing and an app store full of DNA-powered products that let you "unlock" a wide variety of insights based on your genetics. At Helix, my role provides a primary interface between our science, engineering, product and business teams. As a computational biologist and product manager, I get to combine my experience and passion for genetics and software to envision, design, and build consumer genetics products that empower anyone to understand, and interact with, their DNA. On a day to day basis, this means that I help direct, and bridge the gap between, a team of scientists, engineers and designers. Ultimately, our team helps Helix and Helix's partners (companies like National Geographic and LoseIt!) take complex data inputs – the raw A's, C's, T's and G's in DNA – and transform it into information that a person can use to improve their health or learn about their ancestry.

How did you first become interested in science and tech?

Like many scientists, I fell in love with science at a young age and started keeping "lab notebooks" describing observations I made about nature in elementary school. A few years later, my parents bought our first computer and I moved to designing digital notebooks and pamphlets, which then led to designing websites, video games, and other small projects. In high school, I became interested in the field of biomedical engineering after a high school teacher suggested it as a way to combine my interests in technology, science, and math. (A year later, I applied to the Biomedical Engineering program at the University of Southern California). Ultimately, I chose to pursue a degree in molecular biology and continued to do freelance programming and design projects to earn an income on the side. It wasn't until a few years after graduating that I would realize how valuable that experience would be!

Your diverse expertise includes molecular, machine learning, genomics, and software development. Can you briefly explain how these various skills are interrelated? Do you need to change your thought process or methodology when moving from one to another?

I should begin this answer by saying that I didn't start my career knowing that the fields I was passionate about would combine into an interdisciplinary opportunity. With science and technology in particular, I've always found myself drawn to exploring the outer edges of our knowledge. I remember spending countless hours on the internet reading about how to write a program that would create a webpage "I Spy" game for my younger sister. I can also remember the exact day in a genetics class in college when something clicked: DNA is, quite remarkably, a program for a human. Because of this, genomics and programming are easier to relate conceptually than people may initially realize. Unlike programming though, a person's genomics data doesn't come with a README. Decades of research have offered clues into how changes to a person's genetic code can lead to them having brown eyes or developing Huntington's disease. Software applications extract this information from the millions of letters making up a DNA sequence. For other traits and diseases, the connection to genetics is less straightforward and this is where machine learning is beginning to shine. With machine learning, we can give a computer millions of inputs and ask it to "learn" how to predict a person's risk for heart attack. So for me, having "diverse expertise" is actually less about context switching and more about sliding up and down a mental spectrum blending genomics, software and machine learning. The goal is always the same: make someone's life better.

You are a PhD Candidate (or pardon me if you've already received your PhD). How has you continued education shaped your view of science and tech?

Yes, I am a PhD Candidate at the University of Colorado Anschutz Medical Campus in the Computational Bioscience program. I will defend my dissertation research in Spring 2019 to receive my PhD! My personal definition of a successful in a graduate career is building advanced expertise in a particular subject, but even more importantly, using that expertise to bridge what we may now think of as separate fields. (This perspective was heavily influenced by Sean Eddy's Antedisciplinary Science and other works I discovered in high school). The crucial takeaway from academia that shapes my view of science and tech is that research is always moving forward. The incredible amount of time and effort that field- and industry-leaders invest to keep up isn't for impressing their colleagues at a dinner party. If you choose to remain in the comfort zone, you are choosing to ignore the discovery that will change someone's life. The strongest science and tech companies this decade will use innovation as fuel – their products will be designed to incorporate new ideas without breaking stride.

For people who aren't able to pursue formal high education, what resources would you suggest they explore in order to stay current on the ever evolving world of tech? Perhaps there is a website, podcast, book or another outlet that you'd recommend.

Yes, I am a PhD Candidate at the University of Colorado Anschutz Medical Campus in the Computational Bioscience program. I will defend my dissertation research in Spring 2019 to receive my PhD! My personal definition of a successful in a graduate career is building advanced expertise in a particular subject, but even more importantly, using that expertise to bridge what we may now think of as separate fields. (This perspective was heavily influenced by Sean Eddy's Antedisciplinary Science and other works I discovered in high school). The crucial take away from academia that shapes my view of science and tech is that research is always moving forward. The incredible amount of time and effort that field- and industry-leaders invest to keep up isn't for impressing their colleagues at a dinner party. If you choose to remain in the comfort zone, you are choosing to ignore the discovery that will change someone's life. The strongest science and tech companies this decade will use innovation as fuel – their products will be designed to incorporate new ideas without breaking stride.

For people who aren't able to pursue formal high education, what resources would you suggest they explore in order to stay current on the ever evolving world of tech? Perhaps there is a website, podcast, book or another outlet that you'd recommend.

We live in an incredible time for learning! There is a staggering number of resources available for those wanting to supplement their formal education, or those unable to pursue it in the first place. As a self-taught programmer, I relied almost completely on library books and free online resources, and there were (and still are!) times when I felt behind the curve. For people who learn best in a more structured manner, I like to recommend online classes at websites like Coursera and edX. Many of the lessons can be viewed for free. If someone is looking to stay current by networking in an active community, I love to recommend Meetups. Some of my other favorite resources are Hacker News and the Y Combinator podcast, Wired, and the Masters of Scale podcast. To get real-time notifications on science topics I'm interested in, I like to use Google Alerts and email notifications from bioRxiv and PubMed.

You have experience serving as a mentor. What advice would you give to fellow women in tech who are seeking a mentor and what do you think are the keys to a strong mentor-mentee relationship.

I am incredibly grateful to the mentors that I've developed relationships with over the years. Their guidance and wisdom has undoubtedly shaped the way I approach problems, interact with others, and think about goals. I believe these relationships are especially important for women in technology. Some advice based on my personal experiences: it's not necessarily important to find a mentor in your exact field. Learning from experts in your own domain has some obvious advantages, but I found that it ended up being more important to me to just find a mentor that I connected with personally. As a result, the mentors I have now happen to work in all sorts of fields! This offers a great opportunity for learning skills that transcend any one job title. Another key to strong mentor-mentee relationship is understanding that it's a two-way street. I make it a point to ask my mentors about what's most exciting to them that week, or what obstacles they're finding challenging and how they're thinking of overcoming them. Finally, my advice is to always be genuine! Take the time to learn about people's interests and get to know them, and you'll find that there are many incredible women in tech looking for others to mentor.

You have a patent! What can you tell us about it?

I'm really proud and excited about that patent! The full document is available here. The quick summary is that we've patented the ability to apply certain types of machine learning to genetics data. Specifically, we describe how a system (e.g. software program) retrieves genetic data for a user and inputs that genetic information into one or more machine learning algorithms to predict a trait or characteristic about that user. Several examples of this can be seen in DNAPassport, an app that we built at HumanCode before it was acquired by Helix. DNAPassport uses genetic data from a user sequenced on the Helix sequencing platform as input to machine learning models trained to predict ancestry or hair color. In addition to this patent, which has officially been issued, I have 11 additional patent applications currently under review. The process of getting a patent approved and issued often takes around 1-2 years.


Rani can be found on Twitter @RaniPowers.

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