I am a decision scientist and religious studies PhD living in Decatur, Georgia. I am an experienced researcher, writer, editor, teacher, and project manager. I currently work at an analytics and AI consultancy (all views are my own).
Prior to moving to Georgia, I spent five years as a project manager at an internationally operating, progressive non-profit institution for civic education. My primary responsibilities concerned socio-ecological transformation, particularly in relationship to the Global South.
My educational background is in South Asian religious studies and culminated in a PhD. Read more about my dissertation and scholarly publications on my research page.
So I’ve been learning about neural networks and how to use TensorFlow. I wanted to get in on the trend of using recurrent neural networks to generate predictive texts (inspired by Jacqueline Nolis' banned license plate generator), so I trained one to create text in the style of James Joyce’s Ulysses. Here’s a sample. I think it’s a pretty good approximation of the original. Stephen, he had murdered to.
As part of my ongoing effort to learn Python for data analysis, I created this chart summarizing the relative performance of Atlanta’s major league sports teams since the Hawks moved to Atlanta in 1968. Atlanta has not consistently had an NHL team during this time period, and Atlanta’s MLS team is a much more recent addition. In order to facilitate comparisons across sports, ties have been disregarded. The data is presented here as a 10-year rolling average in order to smooth out spikes from one season to the next.
I’ve started learning Python, so I decided to apply some of my newly developing skills to this Tidy Tuesday from a few weeks ago. The data come from the Ask a Manager Survey, which includes earnings information from more than 24,000 self-selecting survey respondents. The respondents are non-random and skew heavily toward white women in professional jobs in the United States. While exploring the data, I found, unsurprisingly, that formal education and years of experience in a field seem to have a profound effect on compensation.