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About

Sunil Mallya is a machine learning researcher whose work sits at the intersection of reinforcement learning, autonomous systems, and sim-to-real transfer — the challenging problem of training AI models in simulation and deploying them reliably in the physical world. He is best known as a core contributor to **AWS DeepRacer**, Amazon's groundbreaking 1/18th-scale autonomous racing platform that enables end-to-end experimentation with reinforcement learning using only a monocular camera. This work, published in 2019 and 2020, has garnered over 114 combined citations, reflecting its significant influence on both research and education in applied RL. DeepRacer has become a widely adopted tool for democratizing reinforcement learning, giving students and practitioners hands-on experience with real-world autonomous control challenges. Mallya has also explored zero-shot reinforcement learning, investigating how deep attention convolutional neural networks can help bridge the simulation-to-reality gap without requiring additional real-world training data. His involvement with the AI Driving Olympics at NeurIPS 2018 further underscores his engagement with the broader autonomous systems research community. Mallya's contributions stand out for making cutting-edge RL research tangible, accessible, and practically deployable.