Three Rice students earn NDSEG Fellowship Awards
Adrian Celaya, Dane Grundvig and Alejandro Diaz are all PhD students in the Department of Computational Applied Mathematics and Operations Research.
Adrian Celaya
About Me
I am a Ph.D. candidate at Rice University in the Department of Computational Applied Mathematics and Operations Research (CMOR). I'm passionate about developing machine learning and AI techniques for real-world challenges. My research focuses on the creation of new approaches that leverage machine learning and traditional applied mathematics for applications in medical imaging. I've also worked on developing machine learning-based methods for applications in fields like consumer health and geophysics. Prior to starting my Ph.D. journey, I served as the Information System Security Manager onboard the USS Carl Vinson.
Research Interests
Fellowships
National Defense Science and Engineering (NDSEG) Fellowship
Sponsor: Department of Defense (Air Force Office of Scientific Research)Dates: September 2022 to August 2025
Media:
Celaya receives National Defense Science and Engineering Graduate Fellowship Award
First-year PhD student earned his B.A. in CAAM from Rice in 2016.
Loewenstern Fellowship
Sponsor: Rice UniversityDates: August 2021 to October 2022
Publications
Here are some links to a few of my publications. For a complete list, please visit my Google Scholar page or my CV.
| A. Celaya, et. al. "MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework ," accepted BraTS 2024 Challenge @ MICCAI 2024. | |
| A. Celaya, et. al. "Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets With Different Contrast Protocols and Liver D isease Etiologies," in Scientific Reports, doi: 10.1038/s41598-024-71674-y. | |
| A. Celaya, K. Kirk, D. Fuentes, and B. Riviere. "Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks," in Computers & Mathematics with Applications, doi: 10.1016/j.camwa.2024.08.013. | |
| A. Celaya, B. Denel, Y. Sun, M. Araya-Polo, and A. Price. "Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO$_2$ Plumes via Deep Learning," in IEEE Transactions on Geosciences and Remote Sensing, doi: 10.1109/TGRS.2023.3273149. | |
| A. Celaya, J. A. Actor, R. Muthusivarajan, E. Gates, C. Chung, D. Schellingerhout, B. Riviere, and D. Fuentes. "PocketNet: A Smaller Neural Network For Medical Image Analysis," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2022.3224873. |
Software
Medical Imaging Segmentation Toolkit (MIST)
The software MIST is a simple and scalable end-to-end framework for medical image segmentation. The framework can seamlessly handle various medical imaging data and is easily expandable to test new ideas (i.e., new architectures, loss functions, etc.). MIST is open source and written in Python for PyTorch.
Contact Me