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Summer 2021

If you are interested in applying for summer 2021, please submit your application at this application form. Please apply before 04/15/2021 to receive full consideration. Also, the deadline is only for application submission via the form. It is fine if recommendation letters are sent a few days later.

Time schedule of the eight-week summer 2021 program is 06/07/2021-07/31/2021.

Flyer for summer 2021 program. Please also check our Q&A webpage for application eligibility, requirements and additional information on the program.

Tentative Projects for Summer 2021

Team 1 Project: Big Data and Machine Learning Techniques for Sea Ice Prediction
  • Research Mentor: Dr. Jianwu Wang, Assistant Professor of Data Science, Department of Information Systems
  • Collaborator: Dr. Yiyi Huang, Research Scientist at NASA Langley Research Center and Adjunct Research Assistant Professor at UMBC
  • RA Student: Sahara Ali, PhD student, Department of Information Systems

Application background: Over the last few decades, Arctic summer sea ice extent has declined by nearly 50% with accelerated retreat in the early 21st century. These dramatic changes in the Arctic sea ice affect a growing community of diverse stakeholders. Research challenge: A frontier research problem is to investigate how machine/deep learning algorithms could help forecast Arctic sea ice extent. By far, we have accumulated tens of years of satellite remote sensing observation data related to Arctic sea ice with tens of GB in size. REU research project: In this project, we will utilize large volumes of training datasets by designing algorithms that are scalable on multiple computing nodes, especially GPU nodes and study how prediction accuracy could be further improved. Impacts to participants and the discipline: The participants will study how machine/deep learning models could be useful for real-world Earth system applications with global impacts.

Team 2 Project: Big Data and Machine Learning Techniques for Medical Image Classification
  • Research Mentor: Dr. Matthias Gobbert, Professor of Mathematics, Department of Mathematics and Statistics
  • Collaborator: Dr. Jerimy Polf, Associate Professor in the Department of Radiation Oncology, University of Maryland School of Medicine
  • RA Student: Carlos Barajas, PhD student, Department of Mathematics and Statistics

Application background: Proton beams’ primary advantage in cancer treatment as compared to other forms of radiation therapy, such as x-rays, is their finite range. The radiation delivered by the beam reaches it maximum, known as the Bragg peak, at the very end of the beam’s range. By exploiting the properties of the Bragg peak, it is possible to only irradiate cancerous tissues, avoiding any damage to the healthy surrounding tissues. A current major limitation of utilizing Bragg peak is the uncertainties in the beam’s position in the body relative to important organs that should not be irradiated. Research challenge: The Compton camera works by detecting prompt gamma rays emitted along the path of the beam. By analyzing how prompt gamma rays scatter through the camera, it is possible to reconstruct their origin. However, the raw data the Compton camera outputs does not explicitly record the sequential order of the interaction data which represents scatterings of a single prompt gamma ray, which makes reconstructions based on Compton camera data noisy and unusable for practical purposes. REU research project: This project will extend our preliminary work for the reconstruction of the Compton images for better deep learning model prediction accuracy. The team can adjust the size and number of residual blocks of the model, and the method of data concatenation which could have large impact on classification time and accuracy. Impacts to participants and the discipline: The participants will study affects of different deep neural network models and configurations on the accuracy of prompt gamma ray reconstruction and will learn about a cutting-edge medical treatment and the inherent need for improving its efficacy.