Ethan Zuo

- Leading an 8-member multidisciplinary robotics project focused on language-guided surgical retraction using the uFactory xArm platform - Designing system architecture connecting LLM-based command interpretation, perception, and dexterous manipulation - Owning mechanical integration and task-level action abstraction for safe retractor control - Collaborating closely with surgeons and medical professionals at UPMC

- Working at the Foam Robotics Lab on the open-source DexKit soft robotic hand project - Developing learned pose estimation pipeline using MANO and HaMeR for real-time joint angle and fingertip estimation in tendon-driven soft robotic hands - Produced an AI-augmented soft robotic hand system with embedded motor control and 12-tendon actuation - Integrating computer vision for manipulation planning, scene estimation, and pose estimation - Supported fabrication and prototyping of soft robotic components, integrated 3-DOF wrist and gantry system - Enabled grasp adaptability across ±20% object size variation - Characterized force/tactile feedback response in soft robotic hands, conducted contact and haptic feedback experiments

- Built a FastAPI web server to expose financial automation workflows through REST endpoints - Implemented NLP-based automation pipelines using large language models (Anthropic API) to parse and process unstructured email data - Integrated Python libraries (pandas, yFinance) for real-time market data retrieval and automated reporting - Developed regex-based parsers and ETL scripts to convert unstructured text into structured JSON/Excel, reducing manual processing time

- Implemented autonomous steering control and camera system for autonomous driving - Worked collaboratively with regenerative braking and emergency braking subsystems

- Taught children ages 6-11 to explore creative engineering with LEGO robotics - Worked closely with campers to develop lego-building and basic programming skills - Taught middle school and high school students (grades 7-12) the fundamentals of Financial Literacy

- Designed and built a full-size, fully functional self-driving prototype; authored a 22-page research paper - Implemented object recognition using Arduino, Raspberry Pi Pico, Raspberry Pi 4B, sensor fusion, and TensorFlow - Integrated IMU, camera module, and motorized flywheel into closed-loop balance control - Awarded 1st Prize at Synopsys Science Championship and CSEF Award