Siddha Ganju
Solutions Architect, NVIDIA

Siddha Ganju is a self-driving solutions architect at NVIDIA. Previously, she developed deep learning models for resource-constrained edge devices at Deep Vision. Her prior work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte-scale data. She was recently featured on Forbes‘s 30 under 30 list, and she’s been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she’s also been guiding teams at NASA as well as featured as a jury member in several international tech competitions. She’s a graduate of Carnegie Mellon University.

Sessions

4:50pm–5:30pm Wednesday, September 11, 2019

Location: Expo Hall 3

Deep learning on mobile

Implementing AI

Secondary topics: Computer Vision, Deep Learning tools, Hardware, Machine Learning, Mobile Computing, IoT, Edge

Siddha Ganju (NVIDIA), Meher Kasam (Square)

Average rating:

(5.00, 2 ratings)

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices. Read more.