Relevant Experience & Education Highlights

Brings hands-on expertise in ROS, Python, C++, PyTorch, CUDA, sensor fusion, and autonomous robotics development, aligning closely with designing Field Foundation Models, Dynamics Foundation Models, and real-world robotic deployments at FieldAI.

1. Algorithm Development

Designed and implemented AI-powered planning, computer vision, and control algorithms for autonomous robots in dynamic environments.

• Oversaw creation of self-driving framework for automatic floor scrubbers at a robotics startup, achieving 90% area coverage and reducing cleaning time by 25%.
• Supervised development of computer vision algorithms detecting pedestrians, footpaths, and traffic signs for delivery robots, achieving 87% accuracy.
• Utilized deep learning algorithms with PyTorch to enhance machine vision for UR5 robots in vending applications, achieving 84% accuracy in plant identification.
• Optimized collision detection algorithms using IMU, LiDAR, and ultrasonic sensors for healthcare robots, achieving 93% success rate.

2. Software Integration and Testing

Integrated ROS-based modules and conducted simulations to test robotic performance in pick-and-place and machine tending operations.

• Simulated advanced machine tending operations with Delta robots and UR5 for warehouse battery sorting and packing at a robotics lab, increasing sorting speed by 40% and operational efficiency by 35%.
• Led comparative analysis between UR5 and Delta robots in simulated pick-and-place tasks, demonstrating 10% higher accuracy with UR5 and 25% faster speeds with Delta.
• Developed and integrated Robot Operating System (ROS) solutions for autonomous robotic systems across multiple projects.

3. Hands-On Robotics Deployment

Applied sensor fusion, control systems, and machine learning to deploy and optimize robotic systems in real-world and simulated industrial settings.

• Deployed sensor fusion with computer vision, pattern recognition, and deep learning for robust autonomy in airport, warehouse, healthcare, and vending environments.
• Led robotics engineering efforts as founding engineer at startups, focusing on trajectory optimization, collision avoidance, and efficiency improvements.
• Leveraged CUDA, TensorFlow, Scikit-Learn, and NumPy for GPU-accelerated machine learning in robotics applications.

Requirements & Candidate Alignment