Sung-Woo Cho is a researcher who applies artificial intelligence to social policy decision-making. He has more than 10 years of experience in workforce training and career pathways, community college systems, and the administrative data these sources generate. He also designs and implements program evaluations. Cho is a Research Associate Professor at the University of Oregon, where he advises senior leadership on data science and student success issues.
Cho currently oversees several machine learning applications for current and prospective project work. He helped devise the machine learning applications of the Descriptive and Analytical Career Pathways Study for the U.S. Department of Labor (DOL) and led the impact evaluation and data analysis of DOL’s Disability Employment Initiative. Cho directed the Miami Dade College Machine Learning Project, which predicted college completion with high accuracy. Cho helped create and oversees the Abt Data Science Fellowship, which trains Abt staff in machine learning programming and applies the training to current project work.
Before coming to Abt, Cho spent six years at the Community College Research Center at Columbia University, focusing on student progression and success. He has coauthored numerous research reports and academic journal articles, including a study on developmental education in community colleges that has been cited more than 1,400 times. Cho has served as a lecturer in applied statistics at The George Washington University and in research writing at Columbia University.
- Artificial intelligence applications
- Program evaluation
- Research methodology
- Community colleges
- Workforce training
- Descriptive and Analytical Career Pathways Study, US Department of Labor
- Evaluation of the Disabilities Employment Initiative (DEI), US Department of Labor
- Pathways for Advancing Careers and Education (PACE), Administration for Children and Families
- Miami Dade College Machine Learning Project, Miami Dade College
- Can We Predict the Future? The Promise of Predictive Analytics and Recommender Systems
- Predictive Analytics, Recommender Systems, and Machine Learning: The Power of Data for Child Welfare
- A Brief History of Machine Learning from a Policy Researcher’s Perspective (Parts 1 and Part 2)
- AI and Data in Policy Research: Today and in the Future
- Using Data Science to Analyze 50,000 Articles in Two Weeks
- How Can We Eradicate Infectious Diseases Using Machine Learning?
- Flu. Coronavirus. Data. Could Analytics Change the Trajectory of a Pandemic?
- Martinson, K., Cho, S.W., Glosser, A., & Gardiner, K. (2018). Washington State’s Integrated Basic Education and Skills Training (I-BEST) program in three colleges: Implementation and early impact report, OPRE Report No. 2018-87. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
- Jaggars, S. S., Hodara, M., Cho, S. W., & Xu, D. (2015). Three accelerated developmental education programs: Features, student outcomes, and implications. Community College Review, 43, 3-26.
- Brunner, E., Cho, S. W., & Reback, R. (2012). Mobility, housing markets, and schools: Estimating the effects of inter-district choice programs. Journal of Public Economics, 96, 604-614.
- Jenkins, D. & Cho, S. W. (2012). Get with the program: Accelerating community college students’ entry into and completion of programs of study. New York, NY: Columbia University, Teachers College, Community College Research Center.
- Bailey, T., Jeong, D. W., & Cho, S. W. (2010). Referral, enrollment, and completion in developmental education sequences in community colleges. Economics of Education Review, 29, 255-270.