The questions. I'm broadly interested in many different types of evolutionary questions. The beauty of computational skill is that it can be applied to a range of biological questions. In the past year, I've been able to work on ants, fungus, the Barton Springs salamander, dinosaurs and other extinct vertebrates.

• Programming. I mostly use Python. But the specific language is less important than understanding the core concepts so I can be adaptable to other languages as needed.
• Version control. I use version control to manage my code, but also to back-up and manage my writing and data. Nothing is ever truly lost if you're using a remotely backed-up version control system.
• Writing an visualization. Being computational., my work is often an abstraction compared to how most people think of biology. I often 'translate' between how I'm viewing the problem and the biological relevance.
• Linear, hypothesis-driven thinking. When you start a project, it can be easy to get bogged down in exploration. It's exciting! But keeping an eye on what you need to demonstrate to support or fail to support a hypothesis gets the project done.
• Delegation and people management. This was a struggle for me to learn, but it's really important in research to be pro-active about assigning work to collaborators, if you're the first author on a paper. Delegating effectively keep the pressure off of you and helps everyone be more productive.

I am mostly self taught. But efforts like Code Academy and Software Carpentry have put a lot of very good educational materials online.

I'm mostly interested in phylogenetic methods. Particularly in the era of genomic data, fossils are often an afterthought in phylogenetic and comparative method questions. I'm particularly interested in making the best use of our fossil resources to improve phylogenetic estimation.