About the reviewers

Siddha Ganju, an AI researcher who Forbes featured in their 30 Under 30 list, is a self-driving architect at Nvidia. 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 a comet. Previously at Deep Vision, she has developed deep learning models for resource constraint edge devices. Her work ranges from visual question answering to GANs to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences, including CVPR and NeurIPS. As an advocate for diversity and inclusion in tech, she spends time motivating and mentoring the younger generation. She is also the author of Practical Deep Learning for Cloud, Mobile, and Edge.

Sal Vivona has transitioned from physics to machine learning after completing his Master's Degree at the University of Toronto’s Department of Computer Science with a focus on Machine Learning and Computer Vision. In addition to reinforcement learning, he also had the privilege to work extensively across a variety of machine learning subfields, such as graph machine learning, natural language processing, and meta-learning. Sal is also experienced in publishing at top-tier machine learning conferences and has worked alongside the best minds within Vector Institute, a think tank that was in part founded by Geoffrey Hinton. He is currently positioned as one of the leading machine learning research engineers at a Silicon Valley Health AI company doc.ai.

Seyed Sajjadi is an AI researcher with 10+ years of experience working in academia, government, and industry. At NASA JPL, his work revolved around Europa Clipper, mobility and robotic systems, and maritime multi-agent autonomy. He consulted Boeing at Hughes Research Laboratory on autonomous systems and led teams to build the next generation of robotic search and AI rescue systems for the USAF. As a data scientist at EA, he architected and deployed large scale ML pipelines to model and predict player behaviors. At Caltech, he designed and applied DL methods to quantify biological 3D image data. He is part of the Cognitive Architecture group at the University of Southern California where he actively contributes to the R&D of virtual humans.