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Program dates in 2025: purely online, Monday, June 09 – Friday, August 01, 2025
For all details and to apply, see the 2025 tab and its sub-pages please!

Welcome to the Online Interdisciplinary Big Data Analytics in Science and Engineering REU (Research Experiences for Undergraduates) Site program at UMBC. This Big Data REU program is funded by the NSF since 2021 to conduct undergraduate research training on how to utilize modern data science and high-performance computing (HPC) techniques to process and analyze big data in many science and engineering disciplines such as Atmospheric Science, Mechanical Engineering, and Medicine.

This REU Site program is conducted purely online to allow students to conduct research without traveling and to experience the increasingly common work environment of multi-disciplinary teams from around the world using state-of-the-art online collaboration tools. Each participant is a member of an interdisciplinary team of undergraduates, supported by a dedicated graduate assistant and faculty mentor, working on a problem posted by a collaborator typically from outside our institution. In this way, you experience the energy and power of multi-disciplinary collaboration in research to overcome the challenges in cutting-edge research.

In recent years, astronomical growth of available datasets in many science and engineering disciplines often requires big data analytics techniques to efficiently and effectively process the large datasets and gain knowledge from them. The program will help students identify frontier research challenges when facing big data in science and engineering, and guide students to conduct research to tackle the research challenges using advanced cyberinfrastructure software technologies (big data, distributed machine/deep learning, HPC, etc.) and hardware resources (including big data cluster, CPU cluster and GPU cluster). The program will provide development of the national workforce in areas of critical need on “Data + Computing + X”.

Each participant who successfully finishes the program and completes all requirements will receive $5,600 stipend and optional support to travel to a conference to present the research.

The training will leverage experiences from previous related efforts: 1) High-Performance Computing (HPC) REU Site at UMBC from 2010 to 2017, 2) Online training on big data + HPC + atmospheric sciences at UMBC from 2018 to 2020, and 3) the previous funding period of this REU Site.

News

  • 2025/01: The 2025 webpage is up and applications can be submitted through the ETAP database system of the NSF. See the 2025 tab for all information and links therein.
  • 2024/12: Both teams present at the IEEE Big Data 2024 conference in Washington, DC, in-person. Team 1 is represented by Olivia Zhang and Brianna Grissom in the main track of the conference, while Team 2 is represented by Julian Hodge and Peter Jin in the REU Symposium special session; Michael Chen had also planned to present, but could not travel unfortunately due to a family emergency.
  • 2024/11: Olivia Zhang won the student travel award (a great achievement for an undergraduate!) to the IEEE Big Data 2024 conference in Washington, DC. Big congratulations!
  • 2024/10: Team 1 had their work accepted as poster at the American Geophysical Union (AGU) Annual Meeting from December 9th to 13th, 2024 in Washington, DC, in the session H13B: Advances in Machine Learning for Earth Science: Observation, Modeling, and Applications. Congratulations!
  • 2024/08: Program concludes with the final presentations of each team, attended by our collaborators. The teams plan future work with the collaborators.
  • 2024/06: Start of program by welcoming all participants and followed shortly by a visit by UMBC President Dr. Valerie Sheares Ashby.
  • 2024/03: Admission of 10 participants complete, including two high school students.
  • 2024/01: Webpage updated for 2024 and application open. Contact bigdatareu@umbc.edu with questions.
  • 2023/12: Our REU Site grant has been renewed for summers 2024-2026. The application for Summer 2024 will be open soon. Please check back our website on application details.
  • 2023/10: The extended version of our 2023 REU team 2’s technical report, titled “Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch”, is accepted by the 2023 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2023 Symposium).
  • 2023/09: The extended version of our 2023 REU team 1’s technical report, titled “Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography”, is accepted by the 22nd IEEE International Conference on Machine Learning and Applications (ICMLA) 2023.
  • 2022/11: Our 2022 REU participants, Jorge López González, Kathryn Chen, Nithya Navarathna and Joseph Clark, received travel award from the 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2022).
  • 2022/10: The extended version of our 2022 REU team 2’s technical report, titled “Multi-Layer Recurrent Neural Networks for the Classification of Compton Camera Based Imaging Data for Proton Beam Cancer Treatment”, is accepted by the 2022 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2022 Symposium).
  • 2022/10: The extended version of our 2022 REU team 1’s technical report, titled “Atmospheric Gravity Wave Detection Using Transfer Learning Techniques”, is accepted by the 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2022).
  • 2021/10: Our 2021 REU participant, Eliot Kim, received travel award for 2021 REU team 1’s publication at 2021 IEEE International Conference on Big Data (BigData 2021).
  • 2021/11: The extended version of our 2021 REU team 2’s technical report, titled “Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy”, is accepted by the 2021 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium).
  • 2021/10: The extended version of our 2021 REU team 1’s technical report, titled “Multi-Task Deep Learning Based Spatiotemporal Arctic Sea Ice Forecasting”, is accepted by the 2021 IEEE International Conference on Big Data (BigData 2021).

Acknowledgement

The program is funded by the grant REU Site: Online Interdisciplinary Big Data Analytics in Science and Engineering (grant no. OAC-2050943 and OAC-2348755) from the National Science Foundation (NSF).