Divyansh Jha is a research engineer and applied deep learning specialist with nine years of experience building production-ready computer vision and remote sensing pipelines for organizations like Woven by Toyota, Esri, and NYU Langone Health. He has led end-to-end workflows from data preparation to deployment—training and scaling models such as DeepLabv3+, Oriented RCNN, Mask R-CNN and PointCNN— and automated HD map production across 10,000+ km using Kubernetes, Pachyderm and Airflow. As a core contributor to the ArcGIS Python API, he added support for diverse deep learning and 3D geometric models and published practical notebooks (including a well-received satellite imagery pool-detection workflow) that bridge research and practitioner use. Comfortable in both research and production settings, he combines a fast.ai deep learning background and a Georgia Tech MS with hands-on experience integrating renderers for robotics and benchmarking complex systems. Colleagues rely on him to translate cutting-edge models into scalable, reproducible pipelines that accelerate developer productivity.
9 years of coding experience
4 years of employment as a software developer
High School CBSE course, High School CBSE course at AVB public school
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Georgia Institute of Technology
Bachelor of Technology - B.Tech., Bachelor of Technology - B.Tech. at Maharaja Agrasen Institute Of Technology, Delhi
fast.ai International fellowship Deep Learning, fast.ai International fellowship Deep Learning at University of San Francisco
Documentation and samples for ArcGIS API for Python
Role in this project:
Data Scientist
Contributions:2 reviews, 34 commits, 9 PRs in 2 years 6 months
Contributions summary:Divyansh added a notebook detailing a deep learning workflow for detecting swimming pools using satellite imagery. They prepared training data, trained a model using transfer learning, and detected and visualized swimming pools in the validation set. Their contributions focused on image classification and object detection within the context of remote sensing.
Contributions:26 commits, 15 pushes, 1 branch in 3 years 6 months
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