Projects

GeoTorchAI Deep Learning Framework

GeoTorchAI, formerly known as GeoTorch, is a spatiotemporal deep learning framework on top of PyTorch and Apache Sedona. It enable spatiotemporal machine learning practitioners to easily and efficiently implement deep learning models targeting the applications of raster imagery datasets and spatiotemporal non-imagery datasets. Besides deep learning, it also supports scalable and distributed data preprocessing for raster and spatiotemporal datasets.

Project Link: Github Repository

ML Aware Spatial Data Repartitioning

This is a framework which aims at reducing the training time and memory usage of a spatial machine learning model by reducing the number of partitions in a spatial grid dataset. Experiments on four datasets achieved significant reduction in training time and memory consumption while bounding the difference in prediction error within 5%.

Project Link: Github Repository

ExBoost: Model Decomposition Framework

An out-of-box AI/ML-SQL co-optimization approach for end-to-end inference workflows where the users specify a SQL query and a pre-trained model exported in ONNX format, and the end-to-end processing will be automatically optimized reducing the execution latency.