The CoreMotion team has created innovative ways for users to improve their health with their devices through motion. As part of this dynamic team, I have the chance to work on industry-leading software features that are incorporated in the products used by millions of Apple customers every day. My role involves designing and implementing plans for evaluating performance of the motion algorithms for the next generation of devices.

We are the pioneer and recognized leader of integrated motion controls. Since 1987, we’ve been designing and delivering smart motion control products as well as automation solutions for semiconductor, automotive and aerospace industriesAs a Software Development & Applications Engineer, I’m responsible for supporting the development, commissioning, and troubleshooting of our smart machines as well as identifying and communicating product improvement ideas and insights• Successfully programmed SmartMotor robotics and motion control software applications in AniBasic and Python, leading to delivery of high-performance motion controls in areas such as automotive test and simulation• Provided technical leadership in troubleshooting industrial communication networks: CANopen, DeviceNet, Ethernet/IP as well as architecture design, hands-on verification, and benchmarking of deployed systems on site• Conducted hands-on research and data analysis to select motors, drives, and actuators for autonomous systems based on requested parameters from multiple clients and applications with hardware and software components• Efficiently built product demos to help our business development and sales team during technology exhibitions, and trained distributors bi-monthly on operation and features of SmartMotor products and proprietary software• Coordinated software development programs by facilitating conversations with technology vendors, managing equipment and part suppliers, while working closely with the product development, sales and marketing teams

As a Self-Driving Car Engineer, I was responsible for applying computer vision and deep learning to automotive problems such as detecting lane lines and predicting steering angles, while studying sensor fusion, localization, planning and PID controls• Autonomous Highway Driving – Utilized Sensor Fusion data to predict location of neighboring cars to 0.02 seconds • Sensor Fusion Using Extended Kalman Filter (EKF) – Applied an EKF to data from Lidar and Radar sensors’ signals• Autonomous Vehicle Localization with Particle Filter – Localized car on map within 0.15m RMSE for over 1 minute • Road Lane Finding – Successfully performed Camera Calibration using checkerboard images and OpenCV in Python