| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | January 29, 2019 |
| Latest Amendment Date: | July 27, 2020 |
| Award Number: | 1852249 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Peter Brass
pbrass@nsf.gov (703)292-2182 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | March 1, 2019 |
| End Date: | February 29, 2024 (Estimated) |
| Total Intended Award Amount: | $359,879.00 |
| Total Awarded Amount to Date: | $399,849.00 |
| Funds Obligated to Date: |
FY 2020 = $19,970.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1112 DALLAS DR STE 4000 DENTON TX US 76205-1132 (940)565-3940 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
TX US 76203-5017 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
RSCH EXPER FOR UNDERGRAD SITES, Special Projects - CCF |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This three-year Research Experiences for Undergraduates site in Data Analytics and Information Retrieval is hosted at the University of North Texas (UNT). The objective of this project is to provide a supportive and engaging research and learning environment for students to build research experiences in data analytics and information retrieval, develop broadly applicable research skills, enhance skills in research collaboration and communication, and explore research interests in data science. It offers a ten-week research program for ten undergraduate students each summer. During the ten-week period, each student engages in closely mentored research, under advising of a faculty mentor, in one major research topic in data analytics and information retrieval. The faculty-student interaction, as well as interaction among students, take many different forms. The project supports continual mentoring of students after the summer program.
The site leverages the expertise of the faculty mentors in data analytics, computational linguistics, and information retrieval to develop state-of-the-art integrated research projects in data analytics and information retrieval for more effectively advising students in research. The research projects cover the fundamental issues in algorithms, models, tools, and applications of both data analytics and information retrieval. The sample research projects include: 1. Data analytics of biomedical images, 2. Content based image retrieval, 3. Information retrieval of large-scale text collections, and 4. Social media information retrieval for disaster research.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The REU program created an excellent environment for high-quality research and mentoring, enabling REU students to engage deeply with data analytics and information retrieval. Throughout the REU training in summer and regular semesters, participants developed interdisciplinary knowledge spanning computing, computational linguistics, and information science, equipping them to craft and assess intelligent, data-driven solutions to real-world challenges. The program not only honed essential research skills such as problem solving, critical thinking, and effective presentation but also fostered collaboration and communication among students, encouraging them to consider graduate studies and careers in science and engineering.
Offering 40 undergraduates from across the nation a chance to engage in cutting-edge research, the program laid a strong foundation in computer and information sciences. The training emphasized the development of research, collaboration, and communication skills. Surveys conducted before and after the program indicated a significant boost in student interest in fields like computer science, information science, and data analytics, and in pursuing STEM graduate studies and research careers.
Post-program, all participants were capable of conducting extensive research in data science and devising data-driven intelligent solutions for addressing real-world problems. They also mastered key research competencies, including oral and written communication, critical thinking, and problem-solving. The project supported an environment that nurtured research collaboration and communication skills, significantly increasing students' interest in pursuing graduate studies and careers in computer science and data science.
Notably, more than half of the participants came from underrepresented groups such as African American and Latinx communities. The REU program provided these students with systematic, rigorous training in data science and machine learning, which might otherwise have been inaccessible. Transitioning from reliance on faculty guidance to independent research, these students acquired vital research skills, setting a strong foundation for their future studies, particularly in data science, and motivating them to continue to graduate-level STEM study.
Last Modified: 05/01/2024
Modified by: Junhua Ding
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