My Personal Webpage
Hello there! I am a Computer Science Ph.D candidate at The University of Texas at El Paso. My dissertation focuses on approaches we can use to improve neural networks trained with limited data based on knowledge of the underlying physics interactions and apply that to segment multi-spectral satellite images of glacial ice in the Hindu-Kush Himalayas using PyTorch. You can find my dissertation proposal here and my full dissertation will be available once I graduate!
My previous research includes:
- Developing and training Physics-Informed LSTMs for next frame prediction of 2D fluid simulations using PyTorch in 2022. Proceedings of the ASME 2022 Fluids Engineering Division Summer Meeting paper
Developing and training a Deep Q-Network for the PowerTAC retail market simulation using Deeplearning4j in Java in 2019. Proceedings of SPIE Defense + Commercial Sensing Symposium paper
Mapping rat brain images to atlases using OpenCV through feature-based matching in 2018. Frontiers in Systems Neuroscience journal article
You can find more info about all my research in my publications page.
Experience
I am highly proficient in Python and have worked as a Teaching Assistant for my University multiple years teaching Python to both undergraduate and graduate students for courses such as Data Structures, Computer Vision with OpenCV, and Deep Learning with Keras, Tensorflow, and PyTorch alongside my research advisor Dr. Olac Fuentes.
I also worked 1.5 years as a Machine Learning Ph.D Intern at The Johns Hopkins University Applied Physics Laboratory where I worked on deploying production level PyTorch models and how to integrate them as part of MLOps with either the Prefect or FastAPI libraries so the models can be used by other applications.
You can find more info about all my experience teaching and working in my CV page.