[Short]
Siddha Ganju, an AI researcher who Forbes featured in their 30 under 30 list, is an Architect at Nvidia focused on medical instruments and Self-Driving. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project at NASA, which ended up discovering multiple meteors. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. Her work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. She has served as a featured jury member in several international tech competitions including CES. As an advocate for diversity and inclusion in technology, she speaks at schools and colleges to motivate and grow a new generation of technologies from all backgrounds. She is also the author of O'Reilly's Practical Deep Learning for Cloud, Mobile and Edge.



[Long]
Siddha is an AI researcher at NVIDIA who leads development of neural networks optimizations for medical instruments and life sciences companies. She leads, designs, and helps deploy real-time and low power AI applications to enable impact of AI in traditional sciences and domains. Previously, she was a Self-Driving Architect at Nvidia, working towards stable and scalable training of neural networks on very large data centers, and utilizing simulation to validate the neural networks. Siddha's work in flood detection has also been converted into an educational course by the UN.

In 2017, as the first engineer and only data scientist in a small startup (DeepVision, now called Kinara), Siddha developed the entire software stack including deep learning models for resource constraint electronic devices.

Shortly after graduating from Carnegie Mellon University in 2017, she was inducted to the Frontier Development Labs (NASA's AI accelerator) as a machine learning lead, for SETI’s CAMS project. She developed meteor detection with AI to automate the recognition, tracking and discovery of meteor showers for CAMS. When it was a manual task, scientists were lucky to get data from a single location, a few nights per year. Now, data flows in from multiple locations every night, and an AI system adjusts for variables that can affect the visual data. Additionally, by growing interest in citizen scientists globally, the network of cameras in this project has now increased six-fold, in multiple new countries, and thus detected the highest number of meteors in NASA’s 63-year history, soon after its release. She also led development of a web portal to track meteor activity globally. The quick turnaround time ultimately led to the discovery of multiple meteor showers and the first-ever instrumental evidence of the Grigg-Mellish comet and discovery of 12 new meteor showers. 

Siddha sees her contribution as both refining computer vision acumen and applying it to new problems. Siddha’s goal is to create advanced, socially responsible applications and help grow the next generation of scientists. She sponsors competitions and community networks like SpaceML, which she co-founded. She’s also a mentor and supporter of the CMU-sponsored Learn-to-Race challenge, which encourages safe, autonomous driving. As an advocate for diversity in technology, she mentors students to grow a new generation of technologists from all backgrounds. Siddha has led Nvidia’s Women in Technology team to develop several mentorship programs amassing over 400+ participants from all career tracks and levels including VPs and Directors. To improve the reach of AI education to the masses, she authored O'Reilly's 600-page book on Practical Deep Learning, which has been translated to 5 languages due to strong reception within a year of publication. She is a regular keynote speaker, reviewer, and advisor for multiple industry-level conferences and serves as a judge of international competitions such as the CES Innovation Awards.