In high school I was told that I was "bad at math" and had to take extra classes in order to pass the state-required math tests. As an undergraduate, I had no concept of computer programming, despite having friends who were computer science/engineering majors. So it is kind of surprising to me that I now have a research career in statistical and computational evolutionary biology.

Prior to starting my PhD, I managed a DNA sequencing lab and was involved in research projects investigating the phylogenetic relationships of waterfowl and the phylogeography of lizards. Thus, I started graduate school thinking that I'd spend a lot of time in the field and the lab as an empiricist and herpetologist. But, once I was there, I began a small project on salamanders –– and nothing worked! I was unable to get any DNA to amplify in the lab. Additionally, even though the lab work was difficult, I became more excited in the idea of pursuing a computational project because that seemed like an even bigger challenge.

In that same semester, a fellow graduate student (Derrick Zwickl) encouraged me to work with him on a small simulation study that would be part of a paper on dwarf boas. Derrick introduced me to coding in C/C++ by giving a series of workshops to our lab group and writing the simulation program with me. The project demonstrated that adding a computational component to an empirical study bolstered our results and clarified our conclusions. I realized that working on theoretical problems in biology led to general conclusions with wide-ranging applications and implications. As that project continued, I stopped working in the lab, became involved in an integrative research training program (IGERT) on computational phylogenetics, and was motivated and encouraged by my PhD advisor (David Hillis) the whole way. I started taking programming and statistics courses, as well as quantitative biology courses, and discovered that I enjoyed statistics and computer programming. In fact, I forgot that I was "bad at math"...turns out I'm actually quite good at math.

My research requires a varied set of tools and skills. I love the fact that each day brings different challenges whether it's writing software or thinking carefully about the representation of the fossil record in evolutionary analyses. But these are some of the skills I use almost every day:

• An understanding of evolutionary biology, especially phylogenetics
• Programming in C++ and Python
• Statistics and probability theory (particularly Bayesian inference)
• Version control using Git
• Critically thinking about published research by carefully reading papers (this also helps me to be a better writer)

As a graduate student, I was initially introduced to programming in C/C++ by my colleague, Derrick, as I mentioned above. To build my skills I took "Introduction to Computer Programming", a freshman-level course in the Electrical & Computer Engineering Department at U.T. Austin in the 2nd year of my PhD. This course was helpful because the required homework assignments––although not directly applicable to my research––forced me to practice and introduced issues that I might not have encountered on my own. My programming abilities improved further as I wrote software for my PhD and postdoctoral research. Often, I look over other people's code to learn new techniques and programming styles (this is one reason why I am a proponent of open-source software).

My understanding of probability and statistics comes mostly from reading reference books and papers, as well as auditing classes. As a PhD student, I sat in on statistics courses, namely probability theory, stochastic processes, and Bayesian statistics. I also took the Woods Hole Workshop on Molecular Evolution which strengthened my grasp on statistical phylogenetics and population genetics. After coming to U.C. Berkeley for my postdoc, I attended seminars in statistics and an excellent course on statistical learning theory. Generally, my comprehension of a statistical model or concept becomes solidified once I write a simulator for it; so improving my statistical acuity goes hand-in-hand with improving my programming skills.

I am very conscious of how much my technical skills and understanding of abstract mathematical concepts have developed over the years. Thus, I am certain that there is much more for me to learn and many ways to become a better programmer and scientist. Luckily, my job and career put me in an environment with many opportunities to do this! In particular, I teach courses and workshops on statistical phylogenetics and scientific programming; and there is nothing like preparing course materials and teaching that improves your knowledge of any subject.

The field of computational phylogenetics is inherently interdisciplinary and advancements in the theory of phylogenetic inference can have far-reaching implications on a wide range of important biological questions. I am currently focusing my research on developing, testing, and applying methods for fully integrating genomic data and information from the fossil record to reconstruct phylogenetic relationships, estimate divergence times and rates of molecular evolution, and investigate patterns of diversification and phenotypic evolution. In general, my work contributes to research investigating fundamental questions in evolutionary biology, including:

• How have rates of molecular and morphological evolution changed across the tree of life?
• What are the forces that cause variability in rates of evolution?
• How do patterns of fossilization, preservation, and recovery change across different taxa?
• Can we detect relationships between geological events and species diversification?
• What are the evolutionary processes acting on different regions of the genome and how have those factors shaped the evolution of different genes?

Since I started research in evolutionary biology, I have had my "dream job": studying evolutionary processes, teaching, and working with wonderful, smart people. I have traveled to New Zealand, Switzerland, the Czech Republic, Greece, the U.K., and all over the U.S. giving talks, collaborating with awesome scientists, teaching in workshops, and just talking about science. This amazing good fortune is due in no small part to my incredibly supportive mentors and colleagues; and I hope to continue to pay-it-forward.

I am really excited to start the next phase of my career as an Assistant Professor at Iowa State University. Iowa State has very strong interdisciplinary graduate programs, so if you're interested in joining my lab, please contact me.