XTRIPODS: Engaging Underserved Communities of Learners in Data Science (EUCLID): A Data-Science Partnership Between University of Chicago and University of California Merced

Principal Investigator:
Roummel Marcia, University of California, Merced
Collaborator:
Rebecca Willett, University of Chicago
Supported by NSF Grant CCF 2343610.


Abstract. This project establishes the Engaging Underserved Communities of Learners in Data Science (EUCLID) program, which is a partnership between the University of California, Merced and the University of Chicago, as part of the TRIPODS Phase II Institute for Foundations of Data Science. The EUCLID program leverages strengths of faculty from these two institutions to provide undergraduate and graduate students a training experience that prepares them for careers in academia, industry, and government. The EUCLID project aims to broaden participation through research, educational, and workforce development activities. The three main objectives of the EUCLID program are as follows: (1) train graduate students in research in machine learning and optimization; (2) develop undergraduate research projects; and (3) develop a linear algebra course as part of the undergraduate Data Science and Computing major at UC Merced.

Recent work in machine learning has demonstrated that deep learning techniques can be used for signal recovery and image reconstruction. Typically, deep neural networks require a large training set, and a predictor function is learned by solving an optimization problem for some given loss function. In many physics-based applications, the observation operator is known. The framework of deep unrolling guides the design of neural network architectures that explicitly incorporates knowledge of the physics of the sensing model. For certain problem classes, deep unrolling learns representations of the data that reflect the constraints imposed on the architecture based on the sensing model. In this work, the team expands on this approach in three ways. First, the work goes beyond gradient descent to optimize the network parameters and incorporate quasi-Newton methods, which exploit previously computed iterates and gradients. Second, the team aims to investigate alternative loss functions that better reflect the noise distribution of the measurements. Third, the project explores optimization techniques for nonlinear physical models.

Publications. This research grant has thus far resulted in the following articles:

[6] Noise model statistics regularization for deep learning biomedical imaging,
         Y. Lu and R. Marcia,
         Accepted to the 2025 IEEE International Symposium on Medical Measurements and Applications,

[5] Triple matrix factorization for drug-drug interaction prediction using fused Gromov-Wasserstein distances,
         S. Malone, M. Aburidi, and R. Marcia,
         Accepted to the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4] Signal reconstruction for ECG-transmembrane voltage potentials using transformers,
         J. Ornelas-Muñoz, G. Terasaki, A. Ranganath, O. DeGuchy, and R. Marcia,
         Proceedings of the 2025 IEEE International Symposium on Biomedical Imaging.
         [doi]

[3] Defending graph neural networks against adversarial attacks via symmetric matrix factorization,
         M. Aburidi and R. Marcia,
         Proceedings of the 2025 IEEE Artificial Intelligence x Multimedia Conference.
         [doi]

[2] Deep unrolled weighted low-rank approximation for high dynamic range imaging,
         M. Aburidi and R. Marcia,
         Proceedings of the 2025 IEEE Artificial Intelligence x Multimedia Conference.
         [doi]

[1] Quasi-Adam: Accelerating Adam using quasi-Newton approximations,
         A. Ranganath, I. Romero, M. Singhal, and R. Marcia,
         Proceedings of the 2024 IEEE International Conference on Machine Learning and Applications.
         [doi]

Presentations. The results of this grant have been presented at the following conferences, workshops, and seminars:

2025 IEEE Artificial Intelligence x Multimedia Conference, Laguna Hills, CA, February, 2025. (link)

Any opinions, findings and conclusions or recommendations expressed in the publications supported by this grant are those of the author(s) and do not necessarily reflect the views of the NSF.