Robotics & Computer Vision · M.S. in Electrical Engineering @ Stanford University

I build robot learning and perception systems that work in the real world — sim-to-real, 3D reconstruction, and real-time vision deployed on robots and vehicles.

• 🦾 AI Robotics Research Intern @ Nimble
• 🤖 Graduate Researcher @ Stanford Vision and Learning Lab (SVL)
• 🎓 M.S. in Electrical Engineering @ Stanford University (Robotics / Computer Vision)
• 📷 Aviation photographer & avgeek
• 🌐 More about me → Portfolio · LinkedIn

A collaboration between the Stanford Vision and Learning Lab and NVIDIA GEAR. SimFoundry builds digital replicas of real-world scenes from video and automatically generates diverse digital cousins to train and evaluate robot manipulation policies — improving real-world success rates by 17–40% on average. PythonPyTorchSim-to-RealRobot LearningManipulation

Efficient single-image neural surface reconstruction: reconstructs high-fidelity signed distance fields (SDFs) from a single RGB image in ~10 seconds — an order of magnitude faster than prior methods — and validated through robotic surface-following experiments. PythonPyTorchComputer Vision3D ReconstructionRobotics

Focus areas: Robot Learning · Sim-to-Real · 3D Reconstruction · Real-time Perception · Manipulation

Outside of robotics and vision, I do aviation photography — chasing aircraft at airports around the world.

I believe tech should be playful, open, and maybe a bit unexpected. When I'm not coding, I'm probably out taking photos of aircraft. ✈️