Summary
Sheelabhadra Dey is an AI Research Engineer and PhD student specializing in reinforcement learning for robotics, currently bridging academic research at Texas A&M with industry work at Gatik. Over nine years of experience span applied ML roles from autonomous driving and energy extraction to time-series forecasting for railroad maintenance, demonstrating a strong track record of moving research into production impact. His PhD work focuses on making RL safer through demonstrations and interventions, building on prior brain-image analysis and audio-based ML projects that reduced manual workflows and achieved strong performance metrics. He has taught multiple ML and robotics courses, indicating solid communication and mentorship skills alongside technical depth. Based in Mountain View, CA, he combines rigorous academic methods with hands-on engineering in autonomy stacks and simulation environments. A less obvious strength is his history of cross-domain problem solving—from FPGA speech processing and IMU-based stabilization to large-scale maintenance forecasting—showing adaptability across sensors, models, and deployment constraints.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Technology (B.Tech.) Electronics and Communication Engineering, Bachelor of Technology (B.Tech.) Electronics and Communication Engineering at National Institute of Technology, Tiruchirappalli
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Texas A&M University
English, Hindi, Odia, Bengali