Team 1 Project: Artificial Intelligence and Big Data Research for Earth Applications
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, UMBCCollaborator: Haotong Jing, Department of Geography, University of Florida
Research Mentor: Dr. Jianwu Wang, Professor, Department of Information Systems, UMBC
Abstract: We will study AI and big data challenges for one specific Earth application and how to leverage advances in AI and data science techniques to solve Earth problems.
Team 2 Project: Machine Learning in Real-Time Imaging for Proton Beam Radiotherapy
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: Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors, mitigating unnecessary radiation exposure to surrounding healthy tissues. Utilizing real-time imaging of prompt gamma rays can enhance the effectiveness of this therapy. Compton cameras are proposed for this purpose, capturing prompt gamma rays emitted by proton beams as they traverse a patient’s body. However, the Compton camera’s non-zero time resolution results in simultaneous recording of interactions, causing reconstructed images to be noisy and lacking the necessary level of detail to effectively assess proton delivery for the patient. In an effort to address the challenges posed by the Compton camera’s resolution and its impact on image quality, machine learning techniques, such as recurrent neural networks, are employed to classify and refine the generated data. These advanced algorithms can effectively distinguish various interaction types and enhance the captured information, leading to more precise evaluations of proton delivery during the patient’s treatment. To achieve the objectives of enhancing data captured by the Compton camera, a PyTorch model was specifically designed. This decision was driven by PyTorch’s flexibility, powerful capabilities in handling sequential data, and enhanced GPU usage, accelerating the model’s computations and further optimizing the processing of large-scale data. The model successfully demonstrated faster training performance compared to previous approaches, but achieved only fair accuracy. We propose to test other machine learning models as well as extend the hyperparameter tuning to significantly improve the model accuracy in order to advance real-time imaging of prompt gamma rays for enhanced evaluation of proton delivery in cancer therapy.