Ragav Venkatesan

About Ragav Venkatesan

I am currently a Principal Engineer at NVIDIA. I work with Maxine and Broadcast products. In particular, I work with AI-based video-enhancement models and generative AI models. I focus on bringing these models to production using NVIDIA Inference Microservices (NIMs) and NIM Blueprints. I also focus on efficient enterprise-level ML platforms, with a particular focus on MLops for computer vision teams. I work with multiple-teams and architected cross-cutting infrastructure tools for data governance, management, visualization and efficient, scalable and distributed training systems.Previously, I was an applied scientist at Alexa AI. My focus was on efficient enterprise-level ML platforms that powered Alexa’s model-training infrastructure, used by over 1500 scientists with hundreds of models trained everyday. I primarily focused on cost-efficient, and scalable distributed training environments. I work both in engineering and research capacities. The problem spaces that I focused on were neural network compression via network architecture search and predictive early-stopping algorithms. Some of the work I have done in this team has been featured at EMNLP 2020.Prior to that, I was an applied scientist at AWS AI Labs. I was part of the AWS Sagemaker launch team and was involved in the development of several AWS Sagemaker CV algorithms, with primary ownership stake in Object Detection and Semantic Segmentation algorithms. I also was a member of the launch team of Sagemaker RL. I owned and launched model compression using RL that became a significant part of the Keynote address at re:Invent 2018 and lead to patents and products. I also worked on domain-adaptation algorithms for Sagemaker CV, which lead to products, patents and publications including an oral paper at CVPR 2019.Prior to entering the industry, I received a Masters in electrical engineering and a PhD in computer science, both from Arizona State University. I continue contributing to the academic world by reviewing for top-tier conferences and journals. I was also the author of Convolutional Neural networks in Visual Computing- a textbook designed for one semester graduate-level courses, which is available in both English and Chinese.For more information, please visit my homepage: https://ragav.netOutside of work, I am also a bass player and producer for Sludge Factory and Sugar Bones and a singer and guitar player for my solo music project, Chirality. Check these at https://sludgefactory.band, https://sugarbones.band and https://chirality.band

Experience

1. Principal Engineer

   NVIDIA

   Mar 2025 — Present · Seattle, WA, US

Education

• Anna University Chennai

  Bachelors in Engineering, Electronics and Communication Engineering

  2006 — 2010

• Arizona State University

  Doctor of Philosophy (PhD), Computer Science

  2012 — 2017

• Arizona State University

  Master of Science (M.S.), Electrical Engineering

  2010 — 2012