Phinna Huang

Support hardware teams to find motherboard related issues during the planning, development, and validation stages of the program:

• Conducted regression testing, analyzed results, submitted observations and improvement recommendations to the development team
• Automated power simulation processes using Python to increase nearly 80% productivity at PCB power simulation
• Validated PCB design specifications with stakeholder requirements and generated reports by retrieving PCB data using Python, HTML, and Javascript
• Designed ER diagram and developed Postgre database for layout constraint data
• Automated ETL processes with python, making it easier to wrangle layout constraint data and reduce time by 50%.
• Maintained, troubleshoot, and debug automated tools

Support iOS Location team to find location related issues on Apple map

• Wrote and optimized test cases to maximize success of manual software testing with consistent, thorough approaches.
• Developed a syncing data tool with multi-threaded method in Python to retrieve extensive GPS location data from more than 15 mobile devices at the same time
• Extracted and converted relevant location data from raw log files into widely used formats like CSV and YAML using Bash and Python
• Visualized location datasets on testing reports using Python data tools including Pandas and Matplotlib
• Generated testing reports using HTML, Javascript, and CSS
• Automated repetitive works using Python and Bash to improve productivity and reduce errors

Conducted research for fault tolerance technology used to repair SRAM defects

• Operated various test tools, such as Memory BIST, brains , DFT Compiler
• Searched and studied traditional fault replacement techniques for embedded SRAMS
• Proposed a novel method of integrating both fault replacement and fault masking techniques enhancing the yield of embedded SRAMS from 67% to 98.4% Implemented the proposed novel method into an embedded SRAMs using synthesis and automatic placement and routing tools on a Linux system, Taxonomic Assignment of Gene Sequences using Hidden Markov Models