Andrés Nigenda is a lead data scientist at the AI Hub for Education within the SCALE Initiative. They apply natural language processing, machine learning, and causal inference methods to generate rigorous evidence on how AI can benefit students, schools, and learning. Andrés is committed to contributing to efforts that steer AI toward public good.
Andrés previously worked for Mathematica, where he led data science, research, and engineering efforts for federal agencies including the National Science Foundation and the Department of Labor. He holds an MS in Computational Analysis and Public Policy from the University of Chicago and a BA in Economics from the Instituto Tecnológico Autónomo de Mexico (ITAM).
natural language processing; education policy; inequality; science of science; economics of AI; causal inference
