Shaping the Research That Informs Inclusive Policy

Ironman grad student combines machine learning, statistics and engineering to increase representation for underserved populations

Octavio Mesner is fascinated by ripple effects — or, as epidemiologists call them, casual pathways.

For instance, he never imagined being diagnosed with a language processing disorder as a child would lead to his focus on math, which led him to a master's degree in biostatistics, which ultimately led him to pursue a combined Ph.D. at Carnegie Mellon University in engineering and public policy and in statistics and data science.

But he now sees the ways in which his early emphasis on STEM contributed to his methodical research habits. He knows that the increased effort he had to put toward learning to read helped him build the resilience necessary to embark upon graduate statistics classes without having studied this subject before.

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