Bio: Omayra Y. Ortega is an Associate Professor of Mathematics & Statistics at Sonoma State University in Sonoma County, California. She earned her Ph.D. (2008) and an M.S. (2005) in applied mathematics and computational sciences from the University of Iowa, where she also was awarded her Masters of Public Health. She earned a B.A. in music and in “pure” mathematics from Pomona College in 2001. Dr. Ortega has directed the Mathematical Epidemiology Research Group (MERG), an undergraduate research group, since 2007. Her scholarly interests reflect her expertise in mathematics: mathematical and computational biology, mathematical epidemiology in developing countries, infectious disease epidemiology, the participation of women and minorities in sciences, and data science.

​Dr. Ortega was the President of the National Association of Mathematicians Inc. (NAM) from 2021-2024, she served as the editor of the NAM newsletter from 2018-2021, and was one of the NAM contributors to the MAA Math Values Blog. She currently serves as the NAM Grants Manager and is the NAM representative to the Joint Data Committee, where she chairs the sub-committee on Math Community Data. Dr. Ortega is also very active in the Association for Women in Mathematics (AWM) and became a Fellow of the Association for WOmen in Mathematics. She is also a Lifetime member and Friend of the Society to the Society for the Advancement of Chicanos and Native Americans in Science (SACNAS). In her free time Dr. Ortega enjoys travel, the outdoors and the fiber arts.

Topics include:

The Mathematics of Mathematics (#MetaMath): An Introduction and Some Examples
We present examples of the application of quantitative techniques, tools, and topics from mathematics and data science to the mathematics community itself. Using research and data about Ph.D.-granting institutions in the United States first published by Wapman, Zhang, Larremore and Clauset (Nature, 2022), we quantify, document, and highlight inequity in departments at U.S. institutions of higher education producing Ph.D’s in the mathematical sciences. We introduce the terms “#MetaMath” and “the mathematics of Mathematics” for this project, explicitly building upon the growing, interdisciplinary field known as the “Science of Science” in order to interrogate, investigate, and identify the mathematical sciences itself. We seek to enhance social justice in the mathematics communities by providing examples of the ways in which the mathematical sciences fails to meet standards of equity, equal opportunity and inclusion. Simultaneously, our goal is to rebut, refute, and resist the idea that the mathematical sciences in the United States is a meritocracy by using data and analysis to support our results. We aim to promote, provide, and posit sources of productive collaborations and we invite interested researchers to contribute to this developing body of work.

Undergraduate Research as a Pathway to Quantitative Justice - Summer 2025
I worked with 18 undergraduate students in the MSRI Undergraduate Program (MSRI-UP) on the theme of quantitative justice: applying mathematical and statistical methods to questions of equity and access. Across six distinct projects, students investigated policing trends, modeled epidemic spread from a statistical perspective, applied topological data analysis to resource distribution, and surveyed available data on the mathematics community. In addition to sharing highlights from these projects, I will reflect on the pedagogical strategies and best practices that supported this work, including scaffolding research questions, fostering collaboration across diverse mathematical backgrounds, and cultivating student ownership of projects. The talk will emphasize how undergraduate research can serve both as a powerful tool for advancing social-justice–oriented inquiry and as a transformative educational experience that prepares students to see themselves as mathematical researchers.

Who Is The Conscience of AI?
As artificial intelligence (AI) continues to reshape our world, examples of the links between bias and representation in AI are becoming more and more apparent. For those of us in the mathematics community, understanding this connection is key. In this lecture, we’ll dive into how AI algorithms work, how mathematics is essential to the success of AI, and why diverse representation matters in reducing bias in this field. We’ll also explore how engaging in training in cultural literacy empowers us to create a more inclusive environment in our field.