2024 Research

Team 1 Project: Accurate and Interpretable Radar Quantitative Precipitation
Estimation with Symbolic Regression

Team members:

  • REU Student: Brianna Grissom, Department of Statistics and Applied Probability, University of California, Santa Barbara
  • REU Student: Jonathan He, Atholton High School, Columbia, MD
  • REU Student: Kenia Munoz-Ordaz, School of Computing and Design, California State University, Monterey Bay
  • REU Student: Julian Pulido, Department of Computer Science, California State University, Sacramento
  • REU Student: Olivia Zhang, Department of Geography and Department of Statistics, University of Florida
  • RA Student: Mostafa Cham, PhD student, Department of Information Systems, UMBC
  • Collaborator: Haotong Jing, Department of Geography, University of Florida
  • Collaborator: Weikang Qian, Department of Geography, University of Florida
  • Collaborator: Dr. Yixin Wen, Department of Geography, University of Florida
  • Research Mentor: Dr. Jianwu Wang, Professor, Department of Information Systems, UMBC

Abstract: Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learned equations. Accuracy and simplicity of the learned equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods.

Deliverables:

 

Team 2 Project: Using Neural Networks to Sanitize Compton Camera Simulated
Data through the BRIDE Pipeline for Improving Gamma Imaging
in Proton Therapy on the ada Cluster

Team members:

  • REU Student: Michael O. Chen, Departments of Mathematics, Dartmouth College
  • REU Student: Julian Hodge, Department of Mathematics and Statistics, UMBC
  • REU Student: Peter L. Jin, James M. Bennett High School, Salisbury, MD
  • REU Student: Ella Protz, Department of Mathematics and Sciences, Florida Atlantic University
  • REU Student: Elizabeth Wong, Department of Mathematics, Brookdale Community College
  • Undergraduate assistant: Ruth Obe, Department of Computer Science, University of Houston-Clear Lake, Houston, TX
  • RA Student: Ehsan Shakeri, PhD student, Department of Mathematics and Statistics, UMBC
  • RA Student: Mostafa Cham, PhD student, Department of Information Systems, UMBC
  • Collaborator: Zhuoran Jiang, Department of Radiation Oncology, Stanford University
  • Collaborator: Dr. Vijay R. Sharma, Department of Radiation Oncology, University of Maryland School of Medicine
  • Collaborator: Dr. Lei Ren, Associate Professor, Department of Radiation Oncology, University of Maryland School of Medicine
  • Collaborator: Dr. Stephen W. Peterson, Department of Physics, University of Cape Town, South Africa
  • Collaborator: Dr. Jerimy C. Polf, M3D, Inc.
  • Collaborator: Dr. Carlos A. Barajas, Department of Mathematics and Statistics, UMBC
  • Research Mentor: Dr. Matthias K. Gobbert, Professor of Mathematics, Department of Mathematics and Statistics, UMBC

Abstract: Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedforward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel ‘Big-data REU Integrated Development and Experimentation’ (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research.

Deliverables: