Senior Applied Engineer

The petitioner proposes to develop scalable data collection and utilization systems for robot control, focusing on general-purpose task execution and dexterous manipulation. This work involves bridging robot learning with video foundation models and demonstration-based training to enable machines to interpret complex environments and perform tasks requiring human-like finesse.

The petitioner proposes to design, improve, and implement domain-specific Generative AI models, Large Language Models (LLMs), and predictive models for critical sectors including transportation, manufacturing, and insurance. His work focuses on addressing challenges in predictive maintenance, quality control, traffic management, and pedestrian behavior prediction to enhance autonomous systems. The endeavor aims to automate routine tasks and provide personalized product recommendations in consumer retail while maintaining the U.S. lead in AI technology.

The petitioner proposes to advance artificial intelligence frameworks for decision-making and control in autonomous robotic systems. This work focuses on developing novel algorithms and machine-learning methods to drive innovation in smart manufacturing. The endeavor aims to enhance production efficiency, reduce costs, and optimize resource utilization in industrial settings.

The petitioner proposes to continue developing state-of-the-art robotic manipulation approaches that enable robots to manipulate objects autonomously in unstructured and unknown environments. This work focuses on endowing robots with human-like intelligence capabilities to improve autonomous operations across various industries.