2025 Research

Team 1 Project: Deep Learning Approaches for Cloud Property Retrieval: Comparing Fine-Tuning with Domain-Specific Architectures

Team members:

  • REU Student: Danielle Murphy, Department of Mathematics, University of California, Berkeley
  • REU Student: Caleb E. Parten, Department of Mathematical Sciences, Eastern New Mexico University
  • REU Student: Autumn Sterling, Department of Computer Science, George Mason University
  • REU Student: Haoxiang Zhang, Fairfax Christian School, Herndon, VA
  • REU Student: Kevin Zhang, Department of Computer Science, University of Maryland, College Park
  • RA Student: Xingyan Li, PhD student, Department of Information Systems, UMBC
  • Research Mentor: Dr. Jianwu Wang, Professor, Department of Information Systems, UMBC

Abstract: Accurate and timely retrieval of cloud properties is essential for near real-time weather forecasting. The GOES-R satellites are equipped with the ABI imager which is newer, detects 16 spectral bands, and offers higher temporal resolution than MODIS. SatVision-TOA is a foundation model trained on data from 14 MODIS spectral bands. This study aims to leverage both ABI’s enhanced capabilities and the SatVision-TOA foundation model. Two different approaches were explored: fine-tuning SatVision-TOA on ABI data and training models from scratch. Models were trained to predict four cloud properties: cloud mask, phase, optical depth, and particle size. For each approach, we developed both single-task and multitask models while employing various deep learning frameworks. Finally, we evaluated model performances to assess trade-offs between foundation model adaptation and domain-specific architectures.

Deliverables:

 

Team 2 Project: Improving Proton Beam Radiotherapy by Sequencing Simulated Patient Data in Compton Camera Real-Time Imaging with Neural Networks

Team members:

  • REU Student: Angelo Calingo, Department of Computer Science & Engineering, University of Nevada, Reno, USA
  • REU Student: Bikash Gautam, Department of Computer Science, Alabama Agricultural and Mechanical University, USA
  • REU Student: Peter L. Jin, James M. Bennett High School, Salisbury, MD, USA
  • REU Student: Sidhya Pathak, Department of Computer Science, University of Virginia, USA
  • REU Student: Michelle Zhao, Department of Computer Science, Cornell University, USA
  • RA Student: Harrison Lewis, PhD student, Department of Mathematics and Statistics, UMBC
  • RA Student: Hussam Fateen, PhD student, Department of Mathematics and Statistics, UMBC
  • Collaborator: Dr. Vijay R. Sharma, Department of Radiation Oncology, University of Maryland School of Medicine
  • Collaborator: Dr. Lei Ren, Department of Radiation Oncology, University of Maryland School of Medicine
  • Collaborator: Dr. Ananta Chalise, 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: Proton beam radiotherapy is an advanced cancer treatment technique utilizing high-energy protons to destroy tumor matter. When the proton beam interacts with the patient’s body, it emits prompt gamma rays, which can be detected by a Compton camera. However, image reconstruction of the beam path from these scatterings is plagued by mischaracterized scattering sequences and excessive image noise. To address this, machine learning models were developed to order the scattering events properly. Multiple novel datasets simulating particle interactions with human tissue were generated using Duke University CT scans and GEANT4 and Monte-Carlo Detector Effects (MCDE) software. An automated hyperparameter tuning framework was also built into the Big-Data REU Integrated Development and Experimentation (BRIDE) pipeline. This work implemented a novel event-classifier transformer and a 1D Convolutional Neural Network (CNN) to better understand spatial relationships in the data.

Deliverables: