Silvia Robles
About Silvia Robles
Silvia Robles is a researcher specializing in higher education policy research, with a strong focus on data analysis and policy analysis. She has worked at Mathematica since 2019 and has a Ph.D. in Economics from Harvard University.
Work at Mathematica
Silvia Robles has been employed at Mathematica as a Researcher since 2019. In this role, she focuses on higher education policy research, applying her expertise in data analysis and policy analysis. Her work contributes to the organization's mission of providing evidence-based insights to inform policy decisions. Mathematica is known for its rigorous research and evaluation practices, and Robles plays a key role in advancing these initiatives in the context of higher education.
Education and Expertise
Silvia Robles holds a Doctor of Philosophy (Ph.D.) in Economics from Harvard University, where she studied from 2009 to 2016. Her academic background also includes a Bachelor of Science (B.S.) in Mathematics and Economics from the Massachusetts Institute of Technology, completed from 2003 to 2007. Robles specializes in higher education policy research, with strong skills in data analysis, particularly using tools such as Matlab and Stata, which are essential for her research projects.
Background
Before joining Mathematica, Silvia Robles served as a Postdoctoral Fellow at the Gerald R. Ford School of Public Policy from 2016 to 2019. In this position, she continued to develop her research skills and focus on higher education policy. Earlier in her career, she worked as a Project Associate at Innovations for Poverty Action from 2007 to 2009, where she gained experience in project management and research in the context of poverty alleviation.
Research Skills and Tools
Silvia Robles possesses strong analytical skills, particularly in the use of Matlab and Stata for complex data analysis. These skills are crucial for her work in higher education policy research, enabling her to conduct thorough analyses and contribute to evidence-based policy recommendations. Her proficiency in these tools supports her ability to handle large datasets and perform detailed statistical evaluations.