Research

My research examines how scientific knowledge is produced through code, statistical workflows, and research infrastructures, with the goal of improving the credibility and transparency of computational science. I develop theoretical frameworks and evaluation tools for assessing research code, including rubric-based approaches for evaluating reproducible statistical workflows. My broader program advances a systems-level approach to scientific practice, integrating computational epistemology, evaluation theory, and meta-science.

ReproducibiliBuddy Team (ACORN Lab)

I lead the ReproducibiliBuddy team’s code review efforts within ACORN Lab, focusing on reproducible workflows, evaluation rubrics, and practical guidance for transparent computational research.

Publications

• Harris, M.A., & McCoach, D.B. (2025). Classify with Caution: An illustrative Example Using Mixture Models and Machine Learning. Journal of Research in Personality.

Preprints

Code Repositories

Posters

Talks

• Harris, M.A. (2025). Open Science Conversations � Fall 2025 Brown Bag. University of Connecticut Language and Cognition Psychology program, Storrs, CT.