About the department:

I’m a new assistant professor in the Department of Biology at Washington University in St. Louis. Not only do I really love my department and university, but St. Louis as a whole is home to a diverse cast of biodiversity researchers, which makes the city a wonderful place to be.

About the research:

I study how evolutionary processes shape biodiversity over deep timescales. I’m particularly interested in biological and statistical problems related to phylogenetics, historical biogeography, and the evolution of ecological interactions. Some questions that have really seized my attention include: What can biogeography and paleogeography tell us about phylogenetic divergence times? How do lineages move and evolve among habitats that move and evolve? and How do ecological networks among hosts and parasites evolve? Most of my work involves designing statistical models of evolution, developing computational inference methods, and learning about evolution through simulated and biological datasets.

What has been the biggest challenge as a new PI so far?

I don’t think there’s been one challenge that stands out above the rest. But what I've found challenging is the sheer number and variety of challenges you face when starting as a PI. When getting started, there seems to be a new challenge every day — designing courses, writing grants, hiring, mentoring, admin stuff — so you're constantly learning on the fly, making little mistakes, adjusting goals, and so on. In a way, it reminds me of being new to grad school.

What has been the biggest surprise so far about being a new PI?

How many decisions you have to make. Time is finite and information is normally incomplete, so you even have to decide what decisions are worth fretting over! This feels different from, say, my PhD, when I most often faced the question, "Can I do it?", while now I'm mostly asking myself, "Should I do it?"

How have you prepared to be a PI?

I solicited advice from friends and mentors to learn what worked for them and what didn't. Most are very willing to share their thoughts and time, I’ve found. You just have to ask. Pooling diverse perspectives helps, since institutions and situations and individuals vary so widely. I also found that my postdoctoral training felt too objective-oriented at times, which left me somewhat distanced from the joy of learning. So, I set aside time for recreational learning, on topics like botany and Earth history, which was really rejuvenating.

When and why did you become an SSE member?

I joined SSE as a postdoc in 2017 at a time when I was realizing how important it is to support the institutions you appreciate.

When was your first Evolution Meeting, and how did it affect your career?

Norman, OK in 2011 was not only the first time I went to an Evolution Meeting, but it was my first conference experience of any kind. My undergraduate degree is in computer science, and I am a latecomer to biology and biological research. Attending exposed me to all sorts of new biological curiosities and scientific approaches. As a new researcher, it also surprised me to learn that the authors of papers that you knew by heart attended the conference and are totally approachable.

Do you remember your first publication in Evolution or Evolution Letters (acceptance or rejection)?

Yes, of course! I submitted my first paper to Evolution in 2017 with Will Freyman and Bruce Baldwin. In it, we demonstrated how one can use island ages to estimate the timing and location of lineage splitting events that gave rise to the silversword alliance, an adaptive radiation of plants that's endemic to the Hawaiian High Islands. Problems like this are difficult because we don’t know exactly how any two lineages are related, when or where any two lineages diverged, when lineages dispersed among islands, or exactly how old any of the islands are. All of those unknowns are random variables in the system that must be inferred (or at least accounted for). Our approach was to design an evolutionary model that combined insights from phylogenetics, biogeography, paleogeography, an integrative approach that we thought was perfect for Evolution. Hoping to submit more soon.

Do you teach evolution? What is the hardest concept to teach?

When I was in grad school, I volunteered with other students to teach evolution in public high schools. This was soon after the Neandertal genome was first published, and so a few of us co-taught a module on the use of ancient DNA for puzzling together human ancestry. It’s extraordinarily fascinating research on everyone's favorite model organism. But teaching high schoolers about genetic differences among human populations is very challenging. How human evolution is communicated can inspire and enlighten people, but it can also hurt people if accidentally mishandled. It has to be done with a tremendous amount of care and sensitivity.

Do you teach evolution? What concept blows students’ minds?

Stuff from historical biogeography that makes you appreciate how much the world has changed over deep time, like India inching all the way from Gondwana to collide with Asia, or Eocene palm trees surviving in the Arctic. Then you tell them about vegetational rafts.

How do you think evolutionary research benefits society?

With how the coronavirus has swept across the globe, it's hard to ignore the explanatory power of evolution. All of us are looking towards medical professionals and scientists to understand what’s happening and why it's happening. Where the virus originated, how it spread across regions, how communicable it is, how it's mutating, and how many will be infected are all being understood through explicit or implicit evolutionary thinking. What if we knew none of these things? I get chills. But what do we see? Phylogenetic trees for the virus are showing up in news and social media, thanks to monitoring tools like NextStrain. I see this as evidence that the light of evolution is making sense of an otherwise senseless disaster, and not just for experts, but for everyone.

Do you have a time management tip to share?

Don't give away your most valuable hours of time! Why spend your most productive time writing emails instead of papers or code? I work best in the early morning, so I postpone all non-urgent work until the afternoon, when I'm a bit duller. This all might sound obvious, but it’s not always easy to identify what times of day are best to pursue different types of work. Track how long you spend on various tasks, and what you’ve produced with that time. You’ll quickly spot undesirable patterns, even after just a few days.

What is your favorite app?

Oblique Strategies

Do you have a favorite science podcast or blog?

A few come to mind: In Our Time, The Taproot, In Defense of Plants, Crime Pays but Botany Doesn't (YouTube)

What book should every evolutionary biologist read?

Not surprising, but On the Origin of Species!

What one piece of advice would you give to a starting graduate student?

Read broadly and deeply, and trust in your initial reactions. Don't feel rushed the first 2-3 years. Just absorb. Keep watch for topics that you find fascinating, but don’t make sense to you, even after a careful reading. Often it’s the case that your confusion is shared with others, and that the field is still struggling to explain the phenomenon or mechanism. These areas are perfect for doctoral research, especially the old and “solved” problems.

What one piece of advice would you give to a postdoc?

Earnestly apply for at least one job during your first year, if you want to continue into a permanent research position. Send your application to your mentors for review. It will ensure you're ready to apply to jobs without disrupting your research when your dream job appears next year.

Do you remember making any mistakes as a trainee; how did you recover?

So many mistakes. Most of my work is computational, so mistakes mostly cost time, but they can be very instructive. One of my favorite mistakes happened when I was first learning about phylogenetics in John Huelsenbeck’s lab. Early on, he mentioned that most phylogenetic models assume all nucleotides evolve independently of one another, and while the assumption isn’t very realistic for protein-coding genes, it’s computationally useful. Why would that be! To get a concrete sense of the problem, I wrote a little C++ program to build a rate matrix for all nucleotide sequences of length L. So, I ran the program with the modest length of L=100, my computer froze, and in that instant I understood what John meant. I felt like a character in a zen kōan.

What is something most people don’t know about you?

I’ve never gone fossil hunting, but I really want to!

What do you enjoy doing in your free time?

These days I'm back to the simple pleasures. Cooking, reading, biking, hiking. Laughing deliriously with my wife and two little kids.