Data Scientist | Statistician
Hortencia J. Hernandez recently obtained her Ph.D. in Data Science at the University of Texas at El Paso (UTEP). She holds a Bachelor of Science in Mathematics with a Music Minor from Baylor University (Fall 2019) and a Masters of Science in Statistics from UTEP (Fall 2022).
Her research interests focus on graph theory and social network modeling with a focus on synthetic data generation. She specializes in analyzing longitudinal data and social network graphs with domain experience in educational and healthcare datasets.
Her technical skill set includes R, Python, SQL, MATLAB, C++/C#, and Go. She has experience developing data visualization projects using the ggplot2 library, RShiny applications, and Power BI, among others.
In addition to research, she performed with UTEP's musical ensembles including Mariachi Los Mineros and the UTEP Flute Choir. Music has shaped her approach to collaboration, creativity, and communicating with diverse audiences.
Currently open, but not limited, to:
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Data science / machine learning roles
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Research collaborations in network science, generative AI, personalization, and data visualization
For any inquiries or collaborations, she can be reached at hjhernandez2016@gmail.com
orcid: 0009-0009-0284-8703
SIAM Conference on Mathematics of Data Science - Atlanta, GA
Title: Protecting Data Privacy in Social Network Studies Using Synthetic Data
Developed synthetic data methodologies for social network studies to preserve confidentiality, employing various methodologies, including approximating the joint probability distribution as a series of conditional distributions, GAN modeling approaches, and a combination of Bayesian networks with variational autoencoders.
¡Viva La Salud! 3rd Annual College of Health Sciences’ Health Disparities Conference - The University of Texas at El Paso
Title: Capacity-building assistance for communicable disease surveillance
Developed an end-to-end data pipeline for the City of El Paso Department of Public Health to improve influenza surveillance by addressing data quality issues, resulting in a public-facing dashboard.
UTEP Visualization & Interactive Collaboration Competition
Team: Connected Nodes
Title: Visualizing Network Graph Comparisons
Developed an R Shiny app that depicts a series of network graphs showcasing the changes that occurred before and after a two-year research training program. In particular, the network models depict latent structures of identity, self-efficacy, and self-concepts for students undergoing biomedical research training.