| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
|
| Initial Amendment Date: | August 10, 2021 |
| Latest Amendment Date: | August 10, 2021 |
| Award Number: | 2131052 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Subrata Acharya
acharyas@nsf.gov (703)292-2451 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2021 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $299,498.00 |
| Total Awarded Amount to Date: | $299,498.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
CAMPUS RM H402 OLD WESTBURY NY US 11568 (516)876-3125 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
NY US 11568-1700 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | CISE MSI Research Expansion |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
Exposure to research is one of the best predictors of degree completion and success in postgraduate education among students pursuing degrees/careers in STEM fields. However, only a small percentage of undergraduate students are engaged in research. This lack of participation has been attributed to factors such as interest in more applied experience, lack of awareness about research opportunities and their benefits, lack of time, lack of interest, perceived barriers to interactions with the faculty, and personal and financial barriers. The problem further aggravates with little participation from underrepresented minority groups. Additionally, biases in several sectors of computer science are evident due to the historical underrepresentation of researchers. Significant evidence shows that engaging undergraduate students in research builds confidence and efficacy, motivates them to pursue advanced graduate level STEM courses, increases retention in majors having high attrition rates, and acclimates them to the demands of a research environment. In addition, underrepresented minority groups students? participation in research is beneficial to the field as it enhances diversity in the STEM workforce.
The goal of this project is to stimulate, engage, and motivate graduate and undergraduate STEM students, including from underrepresented minority groups to conduct data research in different domains, while supporting expansion and enhancement of research capacity to provide more research opportunities to the diverse student population and the faculty. This goal will be achieved by following a three-pronged approach to 1) enhance and expand the technological infrastructure by acquiring new equipment to facilitate computational processing and big data analyses; 2) embed research within the curriculum for existing courses via CURE and introduce new research-focused programs and courses; and 3) motivate and engage students in research activities and provide hands-on experience via research projects, training, seminars, and workshops through guidance and mentoring by academia and industry experts. The award will improve the academic and professional skills of STEM students, in particular female and other underrepresented minority students, provide an opportunity to work with experienced research mentors and present/publish their work, create educational materials for long-term enhancement of academic curricula, and foster knowledge and promote the developmental skills to prepare students for data science and big data careers.
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.
Recipient Organization: State University of New York (SUNY) at Old Westbury
PI: Renu Balyan
This NSF grant (Award# 2131052) was awarded in 2021 to a team of faculty from the Mathematics, Computer & Information Sciences (Math/CIS) and Biological Sciences departments at SUNY Old Westbury. The goal of this project was to increase the research capacity at SUNY Old Westbury by following a three-pronged approach to: a) enhance the computational infrastructure; b) implement an innovative, interdisciplinary curriculum via course-embedded undergraduate research experience (CURE); and c) organize trainings, seminars, and workshops to engage students and increase research participation.
Computational Infrastructure: was successfully acquired, configured, and installed after evaluating various virtualization frameworks. Essential software (e.g., Python, Jupyter, R, RStudio) was installed, alongside features such as restrictive access, user authentication, logging, and automatic user creation. The infrastructure has been tested and integrated into courses such as Data Warehousing, Databases, and Computer Networks, as well as Data Mining, Machine Learning, and Data Analytics. Plans are underway to use it for benchmarking Big Data systems.
Implementing Innovative Curriculum: A new BS in Bioinformatics program was launched in the Biological Sciences department, and a BS in Data Science program is being developed in the Math/CIS department. A Machine Learning undergraduate course, introduced two years ago, has enrolled over 36 students and is offered every other semester. New courses, including "Big Data Systems" and "Neural Networks and Deep Learning" are being prepared for the BS in Data Science program. A GenEd course, "Ethics and Data Security in AI" was developed for campus-wide enrollment. In the Biological Sciences department, a DNA Barcoding CURE was added to the Basic Biological Sciences Lab I course, and a Comparative Genomics CURE course enrolled 22 students, leading to a presentation at the 55th MACUB Conference. Embedded projects in both new and existing courses have been presented at SUNY Old Westbury Research Day, SUNY Undergraduate Research Conference (SURC), and Graduate Conference (GradCon), and other conferences.
Seminars, Workshops, and Survey Findings: Twelve workshops and seminars were organized, featuring industry and academic experts, with 150 students from diverse departments attending. Faculty-focused sessions on AI professional development drew 60 participants. Pre- and post-event surveys revealed that students recognized research's value for coursework and career development, though 80% initially lacked research experience. Courses and workshops significantly boosted students’ research skills and aspirations, with many showing increased interest in pursuing advanced degrees. Surveys indicated a preference for paid research opportunities over volunteer work, motivating the PI/Co-PIs to apply for additional research grants.
Research Publications, Presentations, and Student Mentoring: During the project period six journal articles, two book chapters, and 30 peer-reviewed conference papers, involving 20 students and faculty were published and presented. Additionally, 39 presentations by 51 students (8 graduate, 43 undergraduate) were made at events such as SURC, GradCon, SUNY Old Westbury Research Day, and BNL Day. Thirty-eight students (6 graduate, 32 undergraduate) received support through NSF-funded projects and CSTEP, including 15 females, 18 from historically underrepresented groups, and 2 economically disadvantaged students. Faculty participated in various conferences and professional workshops to further their research and teaching capabilities.
New Funding and Awards: The PI and Co-PIs secured four NSF awards (#101809, #2219623, #2219587, and #2318636) during the project, including three collaborative grants with institutions in California, Texas, Tennessee, and Chicago. Two Co-PIs received SUNY Old Westbury DEFT microgrants to support research and professional development. Additionally, a Faculty Innovator grant was awarded to the PI and colleagues to integrate Google Professional Certificates into courses across multiple departments.
Student Placements and Higher Education: Students mentored by the PI/Co-PIs have secured internships and jobs, including roles as Computer Scientist at the United States Secret Service, Software Engineer at Walmart, and AI Solutions Designer. One African-American female undergraduate is pursuing a PhD at UMass Amherst.
Resources created: Some resources created during the Project period include a GitHub repository for the course “BS in Bioinformatics” with class projects included in the course and some student projects. https://solomonchak.github.io/R_Intro/BS3910_index.html,
GitHub repository for Cluster management. https://github.com/DataManagementSystemsLab/ClusterMgr.
All the seminars & workshops information that were organized during the project period are available on Research webpage of the University website. https://www.oldwestbury.edu/school-arts-and-sciences/mathematics-computer-information-science/research
List of Project specific Publications
● Khalefa, M., Rayana, S., Poon, K., Noutsos, C., & Balyan, R. (2025, In press). Managing HPC Cluster for Research and Teaching Workload. International Conference on Computational Science and Computational Intelligence, (CSCI, 2024), 11-13 December. Las Vegas.
● Kinning Poon, Solomon T. C. Chak, Mohamed Khalefa, Christos Noutsos, Shebuti Rayana, and Renu Balyan. (July 2023). Undergraduate Student Motivation in Research, Science, and Post-Bachelor Education. In Practice and Experience in Advanced Research Computing (PEARC ’23), July 23–27, 2023, Portland, OR, USA. ACM, New York, NY, USA, 8 pages (161-168). https://doi.org/10.1145/3569951.3597578
Last Modified: 01/30/2025
Modified by: Renu Balyan
Please report errors in award information by writing to: awardsearch@nsf.gov.
