I am a second-year Computer Science Ph.D. student at Stanford University advised by Matei Zaharia and Peter Bailis. As a member of the Stanford DAWN Project, I am focused on usable machine learning that enables more than the most well-funded teams to create innovative and impactful systems. This includes reducing the cost of producing state-of-the-art models and creating novel abstractions that simplify machine learning development and deployment.

Prior to starting my Ph.D. at Stanford, I worked at the intersection of machine learning and education. With the MIT Office of Digital Learning, I created new ways of characterizing student populations, predicting student drop out, and personalizing interventions. Based on my own story, I believe that with the right resources, tools, and encouragement we are all capable of doing great work! To this end, my mission in graduate school is to utilize my passion for computer science and machine learning to benefit society at large, while serving as an example of success that will shape the future of our society.

Cody A. Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia
In NIPS ML Systems Workshop 2017
Cody A. Coleman, Daniel T. Seaton, and Isaac Chuang
In Learning@Scale 2015
Yohsuke R. Miyamoto, Cody A. Coleman, Joseph Williams, Jacob Whitehill, Sergiy Nesterko, and Justin Reich
In Journal of Learning Analytics 2015
Dnaiel T. Seaton, Cody A. Coleman, Jon P. Daries, and Isaac Chuang
In EDUCAUSE Review 2015
Jacob Whitehill, Joseph Williams, Glenn Lopez, Cody A. Coleman, and Justin Reich
In Educational Data Mining (EDM) 2015