CV Download
You can download my CV here.
Experiences
- Recruited, managed, and mentored an 8-person R&D and DQA team, specializing in computer vision and embedded Linux, overseeing the entire software development lifecycle.
- Led the development of a next-generation barcode decoding engine, significantly improving decoding rates for complex barcodes by 294% and for general barcodes by 52%.
- Reduced 1D barcode decoding time by 95%, from 60ms to 3ms, and 2D barcode decoding time by 80%, from 100ms to 20ms, while simultaneously decreasing the decoding library size by 37%.
- Leveraged data augmentation and a large image database for robust system stability testing.
- Optimized image processing pipelines with SIMD acceleration across ARM NEON, x86 SSE2, and MIPS architectures, achieving platform-specific speedups up to 6.22x.
- Directed the development of an Auto-Focusing algorithm for a key station on the assembly line, resulting in a 70% reduction in cycle time.
- Spearheaded the launch of 15+ diverse barcode scanning product lines, employing a modular software architecture to effectively accommodate diverse hardware components.
- Led the design and development of several key features enabling real-time camera control and synchronization through integrated SOC, CMOS sensor, and MCU functionality.
- Led cross-functional efforts, collaborating with 4 PMs, and 2 Technical Support Engineers to address real-world decoding challenges and ensure robust product performance.
- Mentored the R&D and DQA teams, achieving the highest average performance rating across the company.
- Developed 1D and 2D barcode decoding and detection systems, leveraging Python/OpenCV for rapid prototyping and C/C++ for efficient execution on SoCs.
- Established a new software department and led the successful transfer of technology from an overseas outsourced team.
- Implemented departmental standards for recruitment, release/debug, and version control.
- Created an image debugging tool that remains the standard development and debugging tool for the image processing team.
Education
Research in Near Field Magnetic Induction Communication and Visible Light Communications.
Master’s Thesis: Induction Communication System using Hall Effect Sensors
- Implemented the very first Near Field Magnetic Induction Communication system that utilized the Hall sensors by taking advantages of its feature such as small volume, high sensitivity and high operation frequency.
- Leveraged knowledge in hardware prototyping with Arduino UNO and Mega 2560, programmed in C, communication protocol prototyping with Matlab, and data parsing with Python.
Coursework:
- Digital Speech Processing
- Computer Graphics
- Natural Language Processing
- Game Programming
Coursework:
- Data Structures & Algorithms
- Computer Arch
- Computer Networks
- Object-oriented Software Design
- Digital Image Processing
- Database Systems
- Web Programming and Applications
Projects
RAISR Python
- Implemented Google's RAISR (Rapid and Accurate Image Super-Resolution) in Python. Utilized hash bucketing and machine learning to enhance the resolution of low-quality images. This project, the most recognized among unofficial implementations, has received over 500 stars and 150 forks on GitHub.
Publications
IEEE International Conference on Internet of Things (iThings), September 2014