Singapore, Singapore
I am a Software Engineering undergraduate at Singapore Management University (SMU) with a background in Cybersecurity & Digital Forensics. I don't just study code, I build tools that solve real business problems. Previously, as a Software Engineer Intern at AiDA Technologies, I engineered Python automation scripts that processed over 1.3 million records, improving data extraction accuracy from 80% to 99.5%. I also optimized database pipelines, reducing insertion times by 50% for high-volume datasets. Currently, I am deepening my technical stack in SMU’s School of Computing & Information Systems, tackling advanced algorithms, full-stack web development, and data management. I am also an active member of .hack, SMU's special interest group for security and technology enthusiasts, where I continue to sharpen my problem-solving skills. I am looking for software engineering internships where I can apply my experience in Python, SQL, and System Automation to build robust, scalable applications.
Developed High-Accuracy Data Extraction Script: Engineered a Python script to extract and standardize time fields from OCR output. Improved extraction accuracy from 80% to 99.54% across >1.3 million records by utilizing advanced RegEx and Pytest for unit testing. Optimized Database Performance: Benchmarked SQLAlchemy vs. pyodbc for high-volume data insertion. Implemented a SQL Transaction method that reduced insertion time for 100,000 rows from 30+ minutes to 16 minutes (approx. 50% improvement) while ensuring robust error handling for invalid data. Built OCR Migration Tools: Developed a "Reformatter" utility using the Shapely library to map output from the new Azure Form Recognizer engine to the company’s legacy format. This critical tool bridged the gap between engines, allowing the system to handle rotated text and complex layouts without rebuilding the entire product. Designed Page Quality Algorithms: Created a "Page Quality Score" algorithm that evaluates 7 distinct factors (e.g. confidence, skew, polygon overlap) to automatically grade the usability of scanned documents for downstream processing. Tech Stack: Python, SQL, RegEx, Git, Linux (WSL), Azure Form Recognizer, Pytest, Shapely.