Ph.D.
Mathematics
University of Connecticut
2001
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Staff
Staff
Dr. Talitha Washington, Ph.D.
Executive Director, Center for Applied Data Science and Analytics
Department/Office
- Office of the Provost
Additional Positions
-
Executive Director & Sean McCleese Endowed Chair in Computer Science, Race, and Social JusticeCenter for Applied Data Science and Analytics
Biography
Talitha Washington, Ph.D., a distinguished mathematician, began serving as the Executive Director of the Center for Applied Data Science & Analytics (CADSA) on January 1, 2025. A transformational leader, Dr. Washington guides CADSA’s direction in harnessing the power of data to address society’s most pressing challenges through groundbreaking research, expanded educational opportunities, and innovative advancements on a global scale. She also serves as the Sean McCleese Endowed Chair in Computer Science, Race, and Social Justice.
Dr. Washington serves as the Past-President of the Association for Women in Mathematics and is a member of the Census Scientific Advisory Committee of the U.S. Census Bureau. Her research interests include applied mathematics, dynamical systems, nonstandard finite difference schemes, data science, artificial intelligence, and education.
An accomplished scholar and advocate for excellence, Dr. Washington has been recognized with numerous honors. She was elected to Phi Beta Kappa and Sigma Xi, as well as mathematics honor societies Kappa Mu Epsilon and Pi Mu Epsilon. Her accolades include the 2019 BEYA STEM Innovator Award, the 2019 Outstanding Faculty Award from Howard University, and the 2020 NSF Director’s Award for Superior Accomplishment. She is a Fellow of the African Scientific Institute (ASI), the American Mathematical Society (AMS), the Association for Women in Mathematics (AWM), and the American Association for the Advancement of Science (AAAS).
Dr. Washington’s professional journey reflects her commitment to advancing knowledge and equity in STEM. She was a VIGRE Research Associate in the Department of Mathematics at Duke University and served as the inaugural Director of the Atlanta University Center (AUC) Data Science Initiative and the NSF National Data Science Alliance. Her academic appointments include assistant professorships at The College of New Rochelle and the University of Evansville, and a full professorship at Clark Atlanta University. As a former Program Director at the National Science Foundation (NSF), she worked in the Convergence Accelerator within the Directorate for Technology, Innovation, and Partnerships (TIP) and in the Division of Undergraduate Education (DUE), where she led the development of NSF's first Hispanic-Serving Institutions Program, coauthoring solicitations that awarded $85 million.
Dr. Washington’s educational foundation is as impressive as her professional achievements. After graduating early from Benjamin Bosse High School in Evansville, Indiana, she studied abroad in Juan Viñas, Costa Rica. She earned her undergraduate degree in mathematics from Spelman College, which included a semester abroad at the Universidad Autónoma de Guadalajara in Mexico. She later completed her master’s and doctoral degrees in mathematics at the University of Connecticut, which recently awarded her an honorary Doctorate of Science.
Dr. Washington shares a unique connection with Dr. Elbert Frank Cox, as both are mathematicians from Evansville, Indiana. Dr. Cox, the first African American to earn a Ph.D. in mathematics, was a faculty member at Howard University. Dr. Washington looks forward to honoring Dr. Cox’s legacy through her dedication to excellence, truth, and service.
Education & Expertise
Education
M.S.
Mathematics
University of Connecticut
1998
B.S.
Mathematics
Spelman College
1996
Featured News
Published Articles and Presentations
Curriculum Guidelines for Undergraduate Programs in Data Science
These Curriculum Guidelines provide a framework for institutions to develop rigorous and adaptable data science curricula, preparing students for careers in research and industry.
Nonstandard finite difference scheme for a Tacoma Narrows Bridge model
This paper develops two dynamically consistent nonstandard finite difference (NSFD) schemes using the Mickens methodology to accurately approximate the nonlinear, coupled ODE model of the Tacoma Narrows Bridge as developed by McKenna, overcoming the limitations of the standard forward Euler method.
Construction and analysis of a discrete heat equation using dynamic consistency: The meso-scale limit
https://www.sciencedirect.com/science/article/abs/pii/S016892742300137X
We present and analyze a new derivation of the meso-level behavior of a discrete microscopic model of heat transfer. This construction is based on the principle of dynamic consistency. Our work reproduces and corrects, when needed, all the major previous expressions which provide modifications to the standard heat PDE. However, unlike earlier efforts, we do not allow the microscopic level parameters to have zero limiting values. We also give insight into the difficulties of constructing physically valid heat equations within the framework of the general mathematically inequivalent of difference and differential equations.
Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach
https://ai.jmir.org/2023/1/e52888/
AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care.
Distributed image encryption based on a homomorphic cryptographic approach
https://ieeexplore.ieee.org/abstract/document/8993025
The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images.