CV

General Information

Full Name Brandon Shen
email brandonshen123@gmail.com

Experience

  • May 2026 - Present
    Research Intern
    SHAPE Lab, Stanford University
    • Developed Touch2Learn, a custom modification of Sony's Toio tangible robot platform featuring a redesigned hot-swappable side attachment system and improved electrical pin connection reliability based on the HERMIT architecture, extending the platform for accessible spatial communication of basketball play replays to blind and low-vision users
    • Independently designed and built a fleet of haptic tabletop micro-robots targeting UIST publication, encompassing full mechanical design of 30mm omnidirectional chassis, dual custom PCBs per robot (nRF52832, Nordic ESB radio, optical localization via PMW3360, vibrotactile haptics via DRV2605L), and bare-metal embedded firmware in C on the nRF5 SDK
    • Iterated through extensive physical prototyping to validate core mechanical unknowns, including SLA-printed omnidirectional wheel geometry at 30mm scale, magnetic coupling strength through a Delrin panel, and 90° worm gearbox torque transmission, diagnosing and resolving failure modes such as axle-induced roller binding misattributed to surface friction, and antenna RF path shorts traced through systematic electrical bring-up of custom PCBs
  • Aug 2023 - Jun 2026
    Chief Technology Officer/Co-Captain
    FRC Team 199, Carlmont High School
    • Previously served as the Lead Design Engineering, and a Project Lead for a subsystem of the robot
    • Own technical roadmap and systems architecture for a 120lb competition robot, making final decision across mechanical, electrical, and software subsystems
    • Coordinate cross-functionally across sensing, actuation, programming, and fabrication teams to maintain design coherence from requirements through competition
    • Establish and enforce mechanical and electrical standards; mentor and train junior engineers in DFM/DFA principles, GD&T, and design review processes
    • Led robot architecture definition for 2025 season, translating competition requirements into integrated system design; team placed 8th of 55, becoming alliance captain
    • Served as primary developer for end effector and elevator subsystems in Java across two competition seasons, implementing PID control loops for precise motor positioning
  • Jun 2025 - Aug 2025
    Robotics Engineering Intern
    Verdant Robotics, Hayward CA
    • Designed and fabricated a modular gantry-style motorized test track from the ground up, integrating mechanical, electrical, and control systems to support continuous 24/7 operation
    • Developed the complete mechanical system in Onshape using DFM/DFA principles and FEA validation, optimizing for manufacturability, structural rigidity, and ease of assembly
    • Engineered and implemented a high-power electrical system capable of reliably driving a 150+ lb payload at 5 mph, with integrated safety features and efficient power distribution

Open Source Projects

  • Sept 2024 - Present
    Search Vision
    • Open-source platform for end-to-end custom object detection model creation. The system integrates Google Custom Search API, diversity-aware image selection via ResNet50, automated web scraping, and YOLOv8 fine-tuning into a unified no-code workflow. Available on GitHub under AGPL-3.0.

Skills

  • Hardware & Mechanical Design
    • Fusion 360, Onshape
    • CAM & CNC Milling
    • Mechanism Design, Rapid Prototyping
    • DFM/DFA Principles, GD&T, Design Review Processes
    • Custom PCB design, RF Antenna Matching, Power Architecture, Microcontroller Integration, Sensor Integration
  • Software & Controls
    • Python (OpenCV, NumPy, Matplotlib, Pytorch)
    • C++, Java, JavaScript, C#, MATLAB, C
    • ROS, PID Control, Embedded Systems, Microcontroller Programming

Academic Interests

  • Human Computer Interaction
    • Touch2Learn
  • Quantum Machine Learning
    • Factorial Benchmarking of Exact and Finite-Shot Gradient Resolvability in a Four-Qubit Hybrid Quantum Neural Network — [DOI](https://doi.org/10.21203/rs.3.rs-10646614/v1)
    • Gradient usability in few-qubit quantum Neural Networks: A signal-to-noise ratio framework for evaluating mitigation strategies — [DOI](https://doi.org/10.64336/001c.166201)

Other Interests

  • Hobbies: Photography, Wushu, etc.