I just finished my MEng degree in Electrical and Computer Engineering with an AI specialization at the University of Waterloo. I love being involved with all areas related to making robots, and I particularly have a strong interest in aspects such as hardware design and robotic controls.
So far I have gained experience in various technical fields including both hardware and software from past projects and interships. Regarding hardware, I am familiar with PCB design and embedded software development. As for software, I have a strong interest in machine learning and computer vision.
In addition, during my earlier years of undergrad, I have also had the chance to try out web development, game development and iOS App develpment. Below are some of my selected skills; for more details, please see my resume.
Developed a sales trend forecasting model by employing exponential regression using Python and TensorFlow and achieved a 72% product sales prediction accuracy with 15,000+ samples with a deep neural network prototype
Designed a two-layer PCB with redundant power in PADs Logic for product GPIO testing; verified signal integrity of various interfaces/protocols (SDI, ASI, TCP, UDP, fibre optics).
Prototyped a wirelessly-controlled magnetic docking station to charge a drone when landed; Designed two-layer PCBs for controlling electromagnets, drone onboard charging, and landing station signaling; Implemented drone landing detection and charging process control in C using Arduino
Researched and integrated IMU motion recognition features with an STM32L4 for the next generation Nymi Band; Designed a two-layer programmable constant current load Arduino shield for LiPo battery discharge characterization; Investigated and Implemented a reliable voltage-based LiPo SoC estimation algorithm to achieve 82.16% of the accuracy of a 16 bit ADC using a 3 bit voltage sensor to reduce 2% of production cost
Lead designer for Forcen’s 2nd generation force-sensing module for medical/defence applications; Architected a six-layer, mixed-signal, multi-rail powered, and IPC High Density compliant DAQ, featuring 10 mN resolution, 1.4 μV noise floor, I2C/CAN /USB communication, and an ultra-compact footprint of 15 mm x 15 mm; Developed two-layer flexible PCB force-sensing films with integrated half-bridge strain gauges; Assisted in the preliminary firmware development in C during the PCB bring-up
Conducted in-depth research in high-speed, high-resolution ADC/DAC architectures and embedded passive technology; Designed, bring-up, and tested various power line filters and analog sensing architectures to further reduce sensor size while significantly improving the noise performance by 67.7%; Created drivers in C for the DAC8563 and AD5689 DACs to interface with the STM32L4 MCU
Check Out My Awesome Works
Project Demos
A compact gesture recognizing cursor input device in the form of a ring that allows the user to freely control their computer mouse position with a simple move of a finger.
An autonomous robot utilizing an intelligent sensor feedback network along with an Arduino 2560 to navigate through a terrain, searching for a target, and returning to base.
A minimalistic drone design utilizing an STM32F446 micro controller, an MPU9250 IMU, and other electrical components.
A small-scale motorized robot that exploits feedbacks from various sensors for line-following.
A research project on using behavioral-based artificial potential fields to control an Epuck robot to autonomously reach a target while avoiding obstacles.
Origami