Award Abstract # 2050195
REU Site: The future of discovery: training students to build and apply open source machine learning models and tools

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UNIVERSITY OF ILLINOIS
Initial Amendment Date: April 12, 2021
Latest Amendment Date: May 21, 2021
Award Number: 2050195
Award Instrument: Standard Grant
Program Manager: Sharmistha Bagchi-Sen
shabagch@nsf.gov
 (703)292-8104
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: April 15, 2021
End Date: March 31, 2025 (Estimated)
Total Intended Award Amount: $405,000.00
Total Awarded Amount to Date: $405,000.00
Funds Obligated to Date: FY 2021 = $405,000.00
History of Investigator:
  • Volodymyr Kindratenko (Principal Investigator)
    kindrtnk@illinois.edu
  • Daniel Katz (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Illinois at Urbana-Champaign
506 S WRIGHT ST
URBANA
IL  US  61801-3620
(217)333-2187
Sponsor Congressional District: 13
Primary Place of Performance: University of Illinois at Urbana-Champaign
506 S. Wright Street
Urbana
IL  US  61801-3620
Primary Place of Performance
Congressional District:
13
Unique Entity Identifier (UEI): Y8CWNJRCNN91
Parent UEI: V2PHZ2CSCH63
NSF Program(s): RSCH EXPER FOR UNDERGRAD SITES
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9250, 075Z, 079Z
Program Element Code(s): 113900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Machine learning is a powerful tool that has been successfully applied to a variety of problems that until recently were deemed too difficult or impossible for computers to solve. This REU Site project gives participating students experience in many aspects of machine learning, ranging from developing open source machine learning models and tools to applying them in the real world. The work carried out by the students will lead to research advances in the fields of these projects and the models and tools they develop will be open-source, leading to them being available to other fields where these models can be used to make additional advances. Machine learning is an emerging field with limitless opportunities to design innovative services and products that will enhance the lives of billions of people, help to address emerging challenges in climate, food, water, energy, transportation, and healthcare, and advance science and engineering discoveries in ways unimaginable today. The project contributes to the development of a highly specialized workforce trained to utilize advanced machine learning methods, and to contribute to open source software. Students from diverse backgrounds and computational/data-oriented disciplines are being trained to apply machine learning and to participate in research where these tools are at the center of scientific discovery, preparing them to apply machine learning methods in other fields and providing them with the foundation and motivation to pursue advanced graduate studies. This project serves NSF's mission by promoting the progress of science and advancing national health, prosperity and welfare.

The goals of this project are to train undergraduate students, focusing on those from minority serving institutions, in machine learning and open source software, where they will then apply these skills to mentor-guided research projects. This is an on-site summer program at the University of Illinois that brings to campus 10 students per year and is based on matching their preferences and interests to those of a group of mentors, so that each student works with a pair of mentors, one from the project's research area and the other with expertise in machine learning. This program increases the students' knowledge of research and graduate school, and in many cases, stimulates their interest in continuing to graduate school, while in other cases, trains students with skills that enable them to seek data science and data analysis jobs in industry, increasing diversity in these graduate programs and in industry. By their presence in the program as continuing undergraduates, when the students return to their university, they will build a relationship between Illinois and that university, their faculty, and their peers that encourages future students to participate in the program and provides the basis for future joint research projects.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Mitra, Chancharik and Yoo, Jin Young and Madak-Erdogan, Zeynep and Soliman, Aiman "Spatial Analysis of Tumor Heterogeneity Using Machine Learning Techniques" 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS) , 2022 https://doi.org/10.1109/MASS56207.2022.00123 Citation Details
Katz, Daniel S and Kindratenko, Volodymyr and Kindratenko, Olena and Mazumdar, Priyam "Training Next-Generation Artificial Intelligence Users and Developers at NCSA" Computing in Science & Engineering , v.25 , 2023 https://doi.org/10.1109/MCSE.2024.3375572 Citation Details
Bader, Jonathan and Belak, Jim and Bement, Matthew and Berry, Matthew and Carson, Robert and Cassol, Daniela and Chan, Stephen and Coleman, John and Day, Kastan and Duque, Alejandro and Fagnan, Kjiersten and Froula, Jeff and Jha, Shantenu and Katz, Daniel "Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments" SC-W 2023 - Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis , 2023 https://doi.org/10.1145/3624062.3626283 Citation Details

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 FoDOMMaT REU program was created with three main objectives:: 1) for undergraduate students to engage in research activities at the University of Illinois at Urbana-Champaign, where they develop open-source machine learning and artificial intelligence models and tools, and apply them to real-world problems; 2) to foster the interest of undergraduate students in potential graduate studies or careers in industrial research by involving the students in scientific research under the supervision of faculty mentors specializing in machine learning and practical applications; 3) to establish collaborative relationships between the students' home institutions and the University of Illinois at Urbana-Champaign faculty, staff, and students to serve as a foundation for future collaborations. Over three years, twenty-seven students participated in the program. They received training in the fundamentals of machine learning and artificial intelligence and contributed to academic research projects overseen by interdisciplinary faculty members, resulting in the development of open-source models, software, and publications. Additionally, students attended professional development seminars covering topics such as graduate school applications, effective research presentations, scientific writing, and open-source software publishing. Many participants pursued further education in graduate school, specializing in artificial intelligence-related fields.

Machine learning is a powerful tool applied to various problems that were recently considered too complex for computers to solve. Undergraduate students in this program gained experience in many aspects of machine learning, from developing models and tools to applying them in real-world scenarios. Their work led to research advances, and the open-source models and tools they developed are available for further advancements in other fields.

Machine learning is a rapidly growing field with numerous opportunities to design innovative services and products addressing challenges in climate, food, water, energy, transportation, healthcare, and scientific and engineering discoveries. This project contributed to developing a specialized workforce trained in advanced machine learning methods and open-source software development. Undergraduate students from various academic disciplines were prepared to be able to apply machine learning methods in research centered around scientific discovery, motivating them to pursue advanced graduate studies.


Last Modified: 04/28/2025
Modified by: Volodymyr V Kindratenko

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