I am a fourth-year Computer Science Ph.D. candidate 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 Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
To appear in ICLR 2020
Peter Mattson, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Gregory Diamos, David Kanter, Paulius Micikevicius, David Patterson, Guenther Schmuelling, Hanlin Tang, Gu-Yeon Wei, Carole-Jean Wu
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
To appear in MLSys 2020
Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou
arXiv Preprint 2019
Cody A. Coleman*, Daniel Kang*, Deepak Narayanan*, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia
In ACM SIGOPS Operating Systems Review July 2019
Cody A. Coleman*, Daniel Kang*, Deepak Narayanan*, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia
In NeurIPS Systems for ML Workshop 2018
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