Education
University of Texas at San Antonio
Ph. D. in Electrical Engineering, advised by Paul Rad (Peyman Najafirad).
Dissertation: Multimodal Learning for Mapping in Remote Sensing
Ph. D. in Electrical Engineering, advised by Paul Rad (Peyman Najafirad).
Dissertation: Multimodal Learning for Mapping in Remote Sensing
2024
University of Texas at San Antonio
M.S. in Electrical Engineering, advised by Yufei Huang.
Thesis: Deep Learning Super-Resolution for EEG Spatial Interpolation
M.S. in Electrical Engineering, advised by Yufei Huang.
Thesis: Deep Learning Super-Resolution for EEG Spatial Interpolation
2019
Texas A&M University - Kingsville
B.S. in Electrical Engineering, advised by Lifford McLauchlan.
Minors in Mathematic and Security Engineering.
B.S. in Electrical Engineering, advised by Lifford McLauchlan.
Minors in Mathematic and Security Engineering.
2016
Employment
Zeitview (formerly DroneBase)
Senior Machine Learning Scientist. I research, develop, and deploy computer vision, vision-language models (VLM), and 3D Reconstruction methods for enhancing renewable energy inspections and analytics, including solar farms, wind turbines, commercial and residential rooftops, transmission and distribution stations, and telecom towers.
Senior Machine Learning Scientist. I research, develop, and deploy computer vision, vision-language models (VLM), and 3D Reconstruction methods for enhancing renewable energy inspections and analytics, including solar farms, wind turbines, commercial and residential rooftops, transmission and distribution stations, and telecom towers.
2021 - 2024
San Antonio, TX
San Antonio, TX
Microsoft Research
Ph.D. Research Intern. As an intern in the AI For Good Lab I researched multimodal pretraining methods for large-scale geospatial vision-language datasets.
Ph.D. Research Intern. As an intern in the AI For Good Lab I researched multimodal pretraining methods for large-scale geospatial vision-language datasets.
2024
San Antonio, TX
San Antonio, TX
SLB (formerly Schlumberger)
Machine Learning Researcher on the Artificial Intelligence Hub team. I researched using deep learning to estimate global xCO2 using in situ ODIAC Fossil fuel emissions and OCO-2 and GOSAT-2 datasets.
Machine Learning Researcher on the Artificial Intelligence Hub team. I researched using deep learning to estimate global xCO2 using in situ ODIAC Fossil fuel emissions and OCO-2 and GOSAT-2 datasets.
2022 - 2023
San Antonio, TX
San Antonio, TX
Spruce
Senior Machine Learning Engineer. I applied state-of-the-art Optical Character Recognition (OCR) and Text Summarization methods to parse real estate and financial documents.
Senior Machine Learning Engineer. I applied state-of-the-art Optical Character Recognition (OCR) and Text Summarization methods to parse real estate and financial documents.
2021 - 2022
San Antonio, TX
San Antonio, TX
BlackSky
Senior Computer Vision Engineer. Developed and deployed models to drive the Spectra AI platform's satellite image analytics as well as served as the PI and lead engineer on the IARPA SMART program
Senior Computer Vision Engineer. Developed and deployed models to drive the Spectra AI platform's satellite image analytics as well as served as the PI and lead engineer on the IARPA SMART program
2021 - 2022
San Antonio, TX
San Antonio, TX
HouseCanary
Senior Data Scientist. Developed and deployed computer vision models for extracting insights and features from real estate property images for improving HouseCanary's Automated Valuation Model (AVM) and property recommender system utilized by real estate investors.
Senior Data Scientist. Developed and deployed computer vision models for extracting insights and features from real estate property images for improving HouseCanary's Automated Valuation Model (AVM) and property recommender system utilized by real estate investors.
2019 - 2020
San Antonio, TX
San Antonio, TX
Booz Allen Hamilton
Senior Data Scientist on the AI Cybersecurity team. I researched and developed prototypes for deep learning based image steganography detection and removal as well as adversarial domain generation detection.
Senior Data Scientist on the AI Cybersecurity team. I researched and developed prototypes for deep learning based image steganography detection and removal as well as adversarial domain generation detection.
2018 - 2019
San Antonio, TX
San Antonio, TX
Southwest Research Institute (SwRI)
Research Engineer in the Defense & Intelligence Solutions Division. I developed and deployed software updates to the A-10 Warthog aircraft as well as researched machine learning methods for detecting engine stalls and exploiting the MIL-STD-1553 communications bus while working under Kenneth Holladay.
Research Engineer in the Defense & Intelligence Solutions Division. I developed and deployed software updates to the A-10 Warthog aircraft as well as researched machine learning methods for detecting engine stalls and exploiting the MIL-STD-1553 communications bus while working under Kenneth Holladay.
2016 - 2018
San Antonio, TX
San Antonio, TX
Oak Ridge National Laboratory (ORNL)
Research Intern in the Intelligent Systems Group. I recorded and annotated a dataset of seismic signals of human and vehicle activity and trained machine learning methods to detect this activity.
Research Intern in the Intelligent Systems Group. I recorded and annotated a dataset of seismic signals of human and vehicle activity and trained machine learning methods to detect this activity.
2015
Oak Ridge, TN
Oak Ridge, TN
Publications
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation
We present a method for efficiently adapting monocular depth estimation models for tree canopy height estimation, providing superior or comparable performance to state-of-the-art methods with significantly less training data.
ECCV 2024 CV4E Workshop
Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking
We present a novel approach for estimating earthquake magnitude from Sentinel-1 imagery using a combination approach of regression and ranking based on earthquake magnitudes, significantly outperforming traditional regression only based methods.
ECML-PKDD 2024 MACLEAN Workshop
Barely-Visible Surface Crack Detection for Wind Turbine Sustainability
We present a novel dataset for evaluating the performance of surface crack detection models on wind turbine blades under challenging conditions with barely-visible cracks.
IROS 2024
A Change Detection Reality Check
We present an analysis of the current state-of-the-art in change detection literature. We find that a simple baseline of U-Net, an architecture from 2015, is still a top performer on several benchmarks and consistently outperforms many recently proposed methods.
ICLR 2024 ML4RS Workshop
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
We introduce a novel remote sensing dataset for evaluating a model's ability to learn long-range spatial dependencies in aerial imagery by performing road extraction while containing large gaps occluded by tree canopy.
IGARSS 2024
ZRG: A Dataset for Multimodal 3D Residential Rooftop Understanding
We present a novel dataset for 3D understanding of roofs containing imagery, digital surface models, and 3D roof geometries captured from over 20k residential inspections.
WACV 2024
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
We perform a fair and large-scale evaluation of remote sensing foundation models and find chip/tile size and normalization preprocessing to be paramount for achieving peak performance for each individual pretrained model.
CVPR 2024 PBVS Workshop
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
We introduce SSL4EO-L, the first ever dataset designed for self-supervised learning for Earth Observation for the Landsat family of satellites.
NeurIPS 2023
Solar Panel Mapping via Oriented Object Detection
We detail Zeitview's deployed end-to-end deep learning framework for detecting solar panels using rotated object detection architectures in large scale solar farms.
ICLR 2023 Tackling Climate Change with Machine Learning Workshop
TorchGeo: Deep Learning with Geospatial Data
We introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem.
ACM SIGSPATIAL 2023
Supervising Remote Sensing Change Detection Models with 3D Surface Semantics
We propose Contrastive Surface-Image Pretraining (CSIP) for joint learning a latent space which extracts surface level features from optical RGB imagery which we show improves the performance in surface relevant tasks e.g. building segmentation and change detection.
ICIP 2022
Self-Supervised Representation Learning Enhances Broad Area Search in Multi-Temporal Satellite Imagery
We propose pretraining on satellite image time-series (SITS) using the Contrastive Predictive Coding (CPC) self-supervised learning method which dramatically improves the performance of Broad Area Search (BAS) tasks in limited labeled data settings.
IGARSS 2022
Destruction of Image Steganography using Generative Adversarial Networks
We propose a Generative Adversarial Network (GAN) based method we coin Deep Digital Steganography Purifier (DDSP), which is optimized to remove steganographic content embedded in image pixels without compromising the perceptual quality of the original image.
ArXiv 2019
DomainGAN: Generating Adversarial Examples to Attack Domain Generation Algorithm Classifiers
We propose DomainGAN which generates adversarial domains with near identical characteristics to benign domains and which greatly evades SOTA DGA classifiers.
ArXiv 2019
Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks
We train a Generative Adversarial Network (GAN) to upsample the spatial resolution of EEG datasets by generating realistic signals, eliminating the need for expensive EEG hardware.
IEEE Biomedical & Health Informatics (BHI) 2019