Empowering stem students through structured computational data science research

The National Science Foundation's Division of Undergraduate Education awarded a $493,617 Project Grant to the University of Houston System (operating as University of Houston-Downtown) effective October 1, 2025 through September 30, 2028, under the STEM Education program (CFDA 47.076). The award supports an Implementation and Evaluation Track project designed to empower undergraduate STEM students through structured computational data science research. The primary deliverables include establishment of a year-long Directed Reading Program (DRP) in machine learning and computational data science, specialized coursework offerings (Special Topics in Machine Learning I & II), and a summer research experience component where students apply theoretical knowledge to real-world data problems while contributing to faculty research initiatives.

Additional products and services include development and dissemination of research findings through faculty and student presentations at professional conferences, scholarly publications, and student-centered outreach events including a Student Research Symposium, open houses, DataFest, and Math and Data Science Club meetings. The project also incorporates development of instructional content and pedagogical tools to support the program's sustainability and institutional capacity. As a Hispanic-Serving Institution (HSI), the award directly supports broader participation in STEM fields among underrepresented populations while enhancing career and graduate school readiness for mathematics, applied statistics, data science, and computer science majors at the institution.

Abstract

With support from the improving undergraduate stem education: hispanic-serving institutions (HSI) program, this implementation and evaluation track project at the university of houston-downtown (UHD) seeks to empower stem students through structured computational data science research. The project will establish a structured undergraduate research program designed to equip students with essential skills in computational data science. Through a year- long experience, undergraduate students majoring in mathematics, applied statistics, data science, and computer science at UHD will gain exposure to machine learning, data analysis, and statistical modeling. The program will integrate academic coursework, mentorship, and hands-on research opportunities, contributing to broader participation in stem ?elds and enhancing career and graduate school readiness. This project will implement a directed reading program (DRP) focused on machine learning and computational data science. A cohort of undergraduate students will engage in specialized coursework, including special topics in machine learning I & II, followed by a summer research experience. During this phase, students will apply theoretical knowledge to real-world data problems and contribute to ongoing faculty research. Additionally, the program will support the dissemination of research ?ndings through faculty and student presentations at professional meetings, scholarly publications, and student-centered outreach events such as the student research symposium, open houses, data fest, and math and data science club meetings, as well as on popular media platforms. Students will also participate in developing instructional content and pedagogical tools. Through these activities, the project will strengthen stem education, increase engagement in computational research, and enhance institutional capacity for high-impact practices in undergraduate stem education. Funded by the HSI program, this project aligns with broader E?orts to enhance undergraduate stem education, positively impact the stem workforce, and build institutional capacity for innovative teaching and learning at hsis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.