Work Experiences

Application Engineer Intern

Rakuten Group, Inc. Sept. 2025 - Oct. 2025

• Developed two internal web applications using PHP/Laravel and Bootstrap 5, successfully transitioning manual ticket-based workflows into real-time, self-service platforms.
• Architected a scalable data automation pipeline utilizing Python and Apache Airflow to streamline monthly validation processes for Rakuten Card.
• Maintained high engineering standards by achieving over 85% unit test line coverage and ensuring zero “code smells” in SonarQube across all deliverables.

Development Engineer

Shanghai Dameng Database Co. Ltd. Jul. 2023 - Feb. 2025

• Architected a massive-dataset rendering engine for a next-generation database management tool, optimizing processing logic via the NatTable framework.
• Reduced query display time for 100,000-row datasets from 10 minutes to 3 seconds (a 200x speedup) by replacing pure SWT Tables with optimized frameworks.
• Engineered a Universal Installer framework to standardize deployment across the database suite, decoupling environment-specific dependencies for seamless cross-platform installation.
• Led the implementation of advanced modules, including an embedded interactive terminal for direct SQL execution and a SQL Formatter, significantly enhancing the user experience.
• Managed the end-to-end SDLC for core modules, ensuring compliance with high-quality standards through meticulous requirement analysis and technical documentation.

Research & Development Intern

NSFOCUS Technologies Group Co. Ltd. Feb. 2023 - Apr. 2023

• Collaborated on security solution development, leveraging Python Scrapy to gather intelligence on fraudulent applications and utilizing Django for the backend of a network probing system.
• Provided technical support and devised encryption algorithms to anonymize customer data for use in machine learning training models.
• Conducted technological assessments and managed routine maintenance to resolve customer concerns effectively.

Research Intern

Virginia Tech Apr. 2022 - Sept. 2022

• Supervisor: Prof. Peng Gao

• Theme: Graph Learning Framework for System Provenance-Based Security Analysis

• Conducted research on Graph Neural Networks (GNN) applied to system provenance graphs to identify malicious Advanced Persistent Threat (APT) behaviors.
• Designed and implemented a novel graph representation learning framework using PyTorch to extract topological features from massive, sparse system audit logs.
• Achieved state-of-the-art 97% accuracy in predicting malicious system entities while maintaining a low false-positive rate on large-scale security datasets.