Atish is a PhD student in Daniel Fisher’s group in Stanford’s Applied Physics department. His primary interest is in studying models of epistasis, and understanding how the statistics of “fitness landscapes” affect the speed and predictability of evolution. He also works at the intersection of evolution and ecology, trying to understand how co-evolutionary processes generate and maintain diversity in complex ecosystems.
Can you tell us a bit about yourself, personally and professionally?
I grew up in San Jose, a diverse city still near and dear to my heart. I’ve always been interested in math and science. I was influenced by my father’s job as an engineer, and my mother always pushed me to succeed academically. When I was 10 years old, I decided I wanted to be a theoretical physicist. For better or for worse, it stuck. I studied math and physics as an undergrad at Swarthmore College, outside of Philadelphia. I love that both fields begin with basic assumptions, and, with those alone, you can discover fundamental truths about the universe.
Near the end of undergrad, I became interested in quantitative biology. It seemed like a more open and underdeveloped field compared to modern physics, which would give me a chance to make a big scientific impact. I applied to Stanford because it had strong research groups in theoretical biology (and because I was tired of being cold in Pennsylvania!). I eventually joined Daniel Fisher’s group, where I now study various aspects of evolution.
Can you tell us about your current research and what you hope to achieve with it?
I use a combination of modeling, simulation, and data analysis to understand what determines the tempo and character of evolution in different scenarios. One of my main projects has been to analyze how epistasis (interaction between mutations) affects evolution. For example, a pair of mutations might do nothing individually, but give benefits to an organism if both are present. Or, they could be beneficial individually, but harmful together. I used a combination of mathematical and computational approaches to show that the linked effects of mutations cause the statistics of future evolution to depend on the statistics of past evolution. My modeling suggests that picking up lots of weakly-beneficial mutations might be better in the long run than picking up a few big, advantageous ones.
Insights like these have practical importance. A quantitative understanding of evolution is critical to solve the most urgent issues in global health today. For example, choosing strategies to curtail antibiotic resistance requires knowledge of how resistances arise in the first place. Cancers require cells to pick up multiple mutations, the probability and timing of which depends on the interactions between them. The flu vaccine needs to be designed to stop next year’s strain of the virus, which requires prediction of which rare strains will be common in the future. Characterizing these complex systems requires theoretical understanding of how different evolutionary processes play off each other.
More recently, I’ve become interested in the intersection of evolution and ecology: what happens when different types of organisms are living together, interacting, and evolving all at once? These co-evolutionary scenarios are important in nature, and can have a very different flavor from evolution within a single population only. I’m currently working on understanding how diversity is stabilized within models of ecosystems, and I hope to branch out and study data from real ecosystems (either in the lab or the wild).
What’s the coolest thing about your work?
It’s exciting that many basic things about evolution are not known. Although there is a lot of great experimental work (enabled by amazing technological development), theory is far behind the data we can collect. It feels akin to what doing physics in the 1700s must have felt like: there is clearly structure in the world, but the overarching understanding still eludes us!
Were there people (or one person) in particular to whom you would attribute your professional success?
I owe a lot to the first physicist who I ever met: Dave Dorfan. Dave was then a professor at UC Santa Cruz who taught at a summer camp for high school students. At that camp, Dave taught me what being a physicist was all about: asking lots of questions, carefully refining them, and, even more carefully, testing them. I also learned two important facts about science: it was, in fact, fun, and also something I could make a career out of.
Dave continued to help me throughout my scientific journey. He was always available with advice about coursework and finding research internships. Dave was also the first person to suggest quantitative biology as an area that I should look into. Without him, I’d likely be in a more traditional subfield of physics.
What are your future plans? Where do you see yourself professionally in the next 5 or 10 years?
I’ve always loved science. From an early age, I was fascinated by space. I wanted to be an astronaut, and I can still remember the glow-in-the-dark map of the solar system I had on my bedroom wall as a kid. In 5th grade, my teacher gave me a copy of A Brief History of Time. I tore through it. At the time, I didn’t understand most of the book, but, nevertheless, I was spellbound by the mysteries of the very microscopic and very macroscopic. I asked my teacher what job Stephen Hawking had, and he told me, “Well, I think he’s referred to as a theoretical physicist.” That day, I made up my mind to become one. Surprisingly, I stuck with it, and even more surprisingly, it panned out!
I hope to continue to do theory. I enjoy my work, in part because it aligns nicely with my skills and temperament. I’d love to run a research group in a few years’ time, with the freedom to set my own research directions as well as the ability to mentor students through the process of becoming a scientist.
What advice would you offer to other grad students or postdocs who are considering pursuing a similar educational and career path as you?
In my mind, one of the biggest determiners of success in grad school is the people you work with. It’s important to find people who you find inspiring, who are fun to work with, and who have a vested interest in your success. Research is difficult enough without having to deal with people who you don’t mesh with.
My other piece of advice for those in the throes of a PhD is: academia is not the only path forward.. I strongly encourage PhD students to learn about and explore other options while they’re still in grad school. The non-academic path is often stigmatized, but it shouldn’t be. Of my friends who started grad school when I did, some of the happiest are those who left academia, either before or after finishing their PhD. Both paths are valid.
Can you speak a bit to the role you see CEHG playing on Stanford campus?
I’ve been fortunate enough to collaborate with the Petrov and Sherlock labs, both of which are heavily involved with the Center. I worked with scientists in both labs (especially CEHG Fellow-mate Yuping Li) to study the character of fitness gains in microbial experimental evolution. Combining their experimental expertise and my theory expertise, we designed experiments and analysis methods to tease apart the tradeoffs made by organisms evolving on short timescales. The type of work we did wouldn’t have been possible without both sides of the equation, and that collaboration has been one of the highlights of my PhD.
Tell us what you do when you aren’t working on research and why. Do you have hobbies? Special talents? Other passions besides science?
I’m into gaming, both board games and video games. I also play jazz flute with a group on campus.
My biggest non-scientific passion, however, is hockey. I play on the Stanford Club Ice Hockey team with both undergrad and grad students, and I’m involved in organizing team activities. My favorite moment on the team was when we played a game at the San Jose Sharks NHL arena. I’ve dreamed about what it would be like to play in my hometown team’s arena, but I never thought I’d actually get the chance!
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