Online Interdisciplinary Big Data Analytics in Science and Engineering
This REU Site program will provide 8-week summer online research experiences to undergraduates 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. The REU Site program will be conducted purely online to allow students to conduct research without traveling and work with experts nationwide. 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 $5,000 stipend and support to conference traveling to present his/her research.
We will host at least eight undergraduate students each year in 2021, 2022, and 2023 (2 teams each year). In 2022, the eight week REU program will be 06/06/2022-07/29/2022. The two project topics planned for summer 2022 are 1) Big Data and Machine Learning Techniques for Atmospheric Remote Sensing and 2) Big Data and Machine Learning Techniques for Medical Image Classification. Application form can be found at https://forms.gle/pQKfHek45YhT2XLd7.
More information of the program can be found at https://bigdatareu.umbc.edu/.
The tentative application deadline is 03/01/2022.
Questions? Please contact Professor Matthias Gobbert (gobbert@umbc.edu) and Professor Jianwu Wang (jianwu@umbc.edu) with any questions.