The ability to test hypothesis faster and more efficiently than in wet lab biology; hypothesis that are directly applicable to society, e.g., infectious disease phylogenetics. Of course, my fascination with computers and my drive to learn new things played a role as well.

• Frustation tolerance
• Analytical thinking
• The recognition that the computer is your friend
• Solid background in biology
• And the drive to keep improving your skill set

During my graduate coursework. My background is in biochemistry so not a lot of statistics and math. However, during my coursework I had to take lots of credits and the biology department didn't offer many new things so I went to the statistics department where I got the fundamental things I use now daily. Also, an important component to me was to learn by myself, signing up for workshops and online courses (MOOCS), where I had the liberty to choose what I thought I needed and just learned it.

In my research I address questions related to genetic diversity and distribution of infectious diseases using phylogenetic and metagenomic tools. Some tangible outcomes of this are the ability to track infections, discover and diagnose new pathogens, and further our understanding on how human activities shape infectious disease emergence.

My advice to any aspiring computational biologist is (cheesy but true) don't let the circumstances of your upbringing dictate what you can and can't do. If computational biology seems unreachable, try harder. If you fail, keep trying!