Software Engineer @ Google
About Me
I am a Software Engineer at Google, where I work on developing machine learning and AI solutions to advance consumer health and well-being. I earned my Ph.D. in Computational and Applied Mathematics from Rice University, where my research combined machine learning with traditional applied mathematics to address real-world challenges in medical imaging, consumer health, and geophysics. Before graduate school, I served as the Information System Security Manager aboard the USS Carl Vinson, leading the ship's cybersecurity program.
Research Interests
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 Disease 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
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.
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