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Xiaoyi Lin

Graduate Student & Research Assistant
New York University
xl4708@nyu.edu


About Me

Hi, I am Xiaoyi Lin, a second-year graduate student in Computer Engineering and research assistant at NYU. I received my Bachelor’s degree in Mechanical Engineering at Tongji University, Shanghai, China.

Technical Skills

Internship

  1. C++ SDE Intern

    Amazon Robotics, North Reading, MA

    • Adopted and implemented an up-to-date machine learning-based algorithm for collision detection using C++.
    • Effectively integrated the algorithm with motion planning code base; reduced 5.4% overall computational expenses for each planning, improving collision checking run-time from ~1ms to ~100ns.
    • Developed test strategy and conducted simulations on the Nvidia PhysX platform and AWS S3.

  2. R&D Intern

    Volkswagen, Shanghai, China

    • Improved a machine learning-based driver fitness determination system using Python. Resolved the overfitting problem by decreasing the sensitivity of RNN, reducing the error rate of blinking detection by 12%;
    • Adopted CI/CD to deploy the system onto the flagship model VW Tiguan using C++ and AUTOSAR

Patterns

Researchs

  1. Advanced perception for learning-based auto lane-changing
    Control and Networks Lab, NYU
    • Leveraged perception for a learning-based auto lane-changing project using RGBD camera and LiDAR;
    • Developing an end-to-end motion planner with diffusion from learning model, enhanced the safety and efficiency of the vehicle under low-representative situations.

  2. Mapping/Localization/Navigation R&D for Self-Driving
    AI4CE Lab, NYU
    • Optimized a redundant mapping process by utilizing openVSLAM system, kept its robustness on Colmap but reduced offline processing time and supported more features than the current ORB filter.

  3. 3D Reconstruction Based on Depth-Optical Fusion DNN
    Oxford University, UK
    • Constructed an artificial neural network by PyTorch; completed the fusion of optical flow, depth and semantic information; implemented moving average method to optimize the framework;
    • Completed the high-quality reconstruction of three-dimensional images.

  4. Automatic Venipuncture Robot Based on Deep Learning
    Tongji University, Shanghai, China
    • Designed the compact supporting unit, positioning unit, puncturing unit, and imaging unit;
    • Designed a semi-supervised method for vein segmentation - Semi-ResNeXt-Unet, which can determine the depth of a vein in ultrasound images and hence navigate the puncture of VeniBot;
    • Collected ultrasound images of 400 patients, including 100 manually labeled patients and 300 unlabeled patients, to validate the semi-ResNeXt-Unet network.