Country: United States of America
Position: Architect, Self-Driving Vehicles
Currently, Siddha is an Architect at Nvidia focusing on the Self-Driving initiative. She works towards stable and scalable training of neural networks on very large data centers, and utilizes simulation to validate the neural networks.
In 2017 Siddha led NASA’s Long-Period Comets team within their AI accelerator, called Frontier Development Lab, where she used machine learning to develop meteor detectors. Recently this project was able to provide the first-ever instrumental evidence of an outburst of 5 meteors coming from a previously known comet, called C/1907 G1 (Grigg-Mellish). As a member of the NASA FDL AI Technical Committee, Siddha is working towards incorporating AI in many space science projects!
Previously Siddha was a Deep Learning Data Scientist at Deep Vision where she worked on developing and deploying deep learning models on resource constraint edge devices. Siddha graduated from Carnegie Mellon University with a Master’s in Computational Data Science and a Bachelor’s in Computer Science and Technology from the National Institute of Technology (NIT), Hamirpur, India. She has also authored a book on Practical Deep Learning for Cloud, Mobile & Edge – O’Reilly Publishers
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would potentially benefit from the new opportunities enabled by deep learning techniques. However, CNN’s are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. We explain how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones and web browsers.