Pythons in Python: Wildlife Trade Data Analysis Using Python
Monitoring the wildlife trade is essential for conservation and disease mitigation, requiring computational tools to manage large datasets. We created pycites for streamlined access, assembly and analysis of CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora) data.
I am a data scientist and field biologist with research interests in social ecological systems. I am a PhD candidate at Oregon State University, co-advised in disease ecology and social-environmental analysis labs. For my PhD, I am using network analytic tools to understand properties of wildlife and human-wildlife collectives, through a lens of disease spread and conservation. I am particularly interested in the integration of field biology methods with computational approaches to solve real-world conservation problems.
I am originally from Singapore, and now live in Corvallis, Oregon, USA, where I love cycling, hiking, climbing, and cooking. You can read more about me on my website.
I am a software engineer and data scientist at CorticoMetrics, a Boston-based company translating neuroimaging research to clinical applications.
My background is in molecular neuroscience, and over the past 6 years I have shifted focus to more computational domains. I learned Python and Unix tools though self study and on the job training, and now use Jupyter on a daily basis.
Professionally, I am interested in reproducible science, data visualization, open source software and translational research. I currently live in Corvallis, OR, USA and enjoy hiking, cycling and rock climbing when not coding.