Eric Yang

Director, Institutional Research and Planning


Email: Eric Yang
Office Phone: (202) 994-6508
1922 F Street, NW, Suite 421 Washington DC 20052

Eric Yang joined the Office of Institutional Research and Planning (IRP) as the Director of Institutional Analytics, a new position created in 2016. In this role, he provides support to GW’s senior administration in the areas of strategic planning, resource allocations, institutional reputations and rankings, and institutional effectiveness, using data analytics, predictive modeling, ranking analyses, and cost-benefit analyses. In addition, he supervises the office staff and manages the day-to-day operations of the IRP office.

Before joining GW, Eric served as the Director of Institutional Research in the Office of Institutional Research, Assessment, and Analytics at the University of South Carolina (USC), managing office activities including data support and analytics for decision making and planning, and oversight of federal and state reporting requirements and submission in timely manner for the USC system of eight campuses. Prior to that, he worked at Old Dominion University for 16 years, serving as Financial Planning Manager in the University Budget Office and Manager of Institutional Research in the Office of Institutional Research and Assessment.

Eric has worked in the field of institutional research for 20 years. He is an active member of the Association for Institutional Research (AIR), currently serves as a member of AIR’s Board of Directors. He is also the past Chair of the Overseas Chinese AIR (OCAIR) and served in the Nominations and Elections Committee of AIR. He received his BS degree in Applied Mathematics and his MS degree in Operations Research from Dalian University of Technology, Dalian, China, and his PhD in Economics, from McGill University, Montreal, Canada.

Presentations of machine learning project on Association of Institutional Research Annual Forums

Transforming IR with machine learning project: Planning, Strategy, Practice.  2022. Click here to download. 

Identifying students at risk using machine learning models in Python. 2022. Click here to download.

Machine Learning Pipelines for Improving Retention: Privacy and Procedures. 2023. Click here to download.