AI/ML algorithm development for prototype applications in remote sensing:
- Develop prototype AI/ML algorithms and associated software tools using Python and the Python/TensorFlow API
- Train AI/ML models and tune their hyperparameters for a given dataset and algorithm objectives
- Visualize hyperparameter optimization spaces with Tensorboard for selection of optimal parameters for a given parametric (functional) TensorFlow model
AI/ML dataset generation, curation, and management:
- Provide customized solutions to data quality control that ensure accurate functional mappings for AI/ML algorithms on complex remote sensing datasets
- Develop databases / data lakes / data warehouses for organizing both structured and unstructured datasets
AI/ML algorithm R&D:
- Apply machine learning and general computer vision best practices and methods to analyze and exploit large, complex remote sensing datasets from a variety of remote sensing phenomenology
- Keep up with the SoTA practices for AI/ML, perform relevant R&D, and implement new and innovative ideas in machine learning and high-performance computing to solve long-standing remote sensing “big-data” exploitation problems
Software development, documentation, and coding best practices:
- Contribute and adhere to the AI team’s standards for reviewing and unit-testing code, lead or participate in team-wide code reviews, and adhere to standardized documentation practices
- Utilize Python PEP8 standards