I am a PhD candidate in Management Science and Information Technology at MIT Sloan School of Management. I use causal inference and machine learning techniques to study the digital economy. I am also affiliated with the MIT Initiative on the Digital Economy and the Stanford Digital Economy Lab. I graduated from Duke University in 2015, with a B.S. in Computer Science where I was Reginaldo Howard Memorial Scholar. I have also taken courses at University of Delaware, University College London, and Stanford University. Prior to my PhD, I was a Research Fellow at the Stanford Graduate School of Business. My previous engineering roles have included computer networking research, Android and iOS development, natural language processing research, and data mining and analysis. Through each of these roles, I have increased the ease and speed with which I pick up a new language or technical skill, and apply to the problem at hand, all in a fast-paced and collaborative environment. I am particularly proficient in Python, R, SQL, & Stata. I also have a solid background in the fundamentals of machine learning and statistics, with an emphasis on non-parametric and non-linear models. I bring a diverse set of experiences to organizations that translates into a unique perspective and excellent leadership and collaborative skills. I can explain complex technical material to nontechnical audiences in a clear and concise manner. I am passionate about ethical machine learning, access to technology, and constructive communication with stakeholders (be that companies, non-profit organizations, or community members) during the creation of technical products and/or algorithms.