Although I've been away from the world of academia for a little while (my last full-time research position ended in January 2018, and my last teaching position ran until August 2019)- deep down, I'll always be a scientist and researcher. Academically, I'm a disease ecologist and evolutionary biologist by training, having completed my doctorate at Harvard University and a bit of a postdoctoral associateship at Duke University. As a disease ecologist, I've been (and still am) interested in understanding the interactions between parasites, hosts, and the environment; as an evolutionary biologist, I'm more broadly interested in understanding how interactions between organisms have shaped and continue to shape the general course of evolution. No organism evolves in a vacuum- each and every one of us plays a part in the lives of countless other individuals each day in unexpected, but sometimes momentous ways! In fact, it's often the discovery of the most unexpected and inexplicable interactions among organisms that furthers our understanding of evolution. This isn't anything new, though; the father of modern evolutionary thinking himself even saw this an important point to make in his magnum opus:
"Many cases are on record showing how complex and unexpected are the checks and relations between organic beings, which have to struggle together…"
Charles Darwin, On the Origin of Species
Nowadays, I’m a data / decision science consultant, helping companies and organizations around the world to make sense of the unexplained and unexpected. From academia to industry, my "tools of the trade" have largely remained the same (although, of course, always growing): various programming languages, databases, and statistical methods to discover, quantify, and describe phenomena both in datasets and up/downstream, in "the real world" (because, as I always say: data is not reality). Although I now use the Python (3.*) programming language (the modern data science lingua franca) for nearly everything, my language of choice all throughout academia was R, and I used it for nearly a decade for everything from basic statistics to multi-core, parallel simulations of disease outbreaks on modular social networks. I've taught how to use R in traditional classroom settings, as well as in one-on-one peer mentoring and online learning platforms. I'm also a contributor to the R ecosystem, through my development of research-focused packages like 'enss' for Effective Network Size Simulations.
In addition to my research (which I'm always happy to discuss), I also try my best to stay involved with various public science projects both on the web and in the real world, with the purpose of innovating upon the current academic/research model. While in academia, these projects were mainly focused on increasing the accessibilty of research findings, funding, and collaborators. More recently, I've volunteered my expertise toward the data science development on a free, open air quality monitoring platform, SimpleAQ. Even if it's "just" as a guest lecturer for my children's classrooms, or for a local college class around the corner from my house, I remain quite active with "educational outreach" (What can I say? I'm an educator at heart!), I still try to expand and enhance the broader impacts of science on society as a whole, in my own little way...
