About
Master's Degree student majoring in Big Data Convergence at Chungbuk National University. Specializes in data analytics and multimodal AI with over 4+ years of experience in software development and 2+ years in data analytics, ML, and AI.
Academic Background
- Current: Master's Degree in Big Data Convergence, Chungbuk National University
- Focus: Data Analytics and Multimodal AI
Professional Experience
- 4+ years in Software Development
- 2+ years in Data Analytics, Machine Learning, and AI
- Experienced in developing scalable AI systems
Research Interests
- Natural Language Processing (NLP): Understanding and processing human language
- Large Language Models (LLMs): Working with state-of-the-art language models
- Applied Artificial Intelligence: Focus on real-world applications
- Data Analytics: Extracting insights from complex datasets
- Intelligent Automation: Developing AI-driven systems for process automation
Publications
QA-SQL: Query-Augmented SQL generation using few-shot prompting with data augmentation
Authors: Sokheang Chan, Vungsovanreach Kong, Anand Nayyar, Tae-Kyung Kim
Journal: PeerJ Computer Science (Accepted, To be published) - View Journal
A Novel Hybrid Rule-based aggregation System for Detecting Abnormal Anomalies in Welfare Subsidy Transaction Operating in Real-time Scenarios
Authors: Sokheang Chan, Vungsovanreach Kong, Tae-Kyung Kim
Conference: BIGDAS Conference 2024 - View Conference
Research Projects
IntelliScrape: An AI-assisted Web Scraping Module Generator
An AI-assisted module development system designed to automate COOCON's scraping module creation process. The system leverages artificial intelligence, network traffic analysis, and browser automation technologies to reduce manual effort, minimize human error, and accelerate development lifecycle.
Key Contributions:
- COOCON System Implementation: Architected and developed core system components
- Req_Observer Component: Chrome extension for network traffic observation and analysis
- Filtering Component: Multi-layered filtering system utilizing rule-based, graph-based, and LLM-based approaches
- Req_Transmitter Component: Advanced request processing with intelligent filtering, iSAS function suggestion, and dynamic module generation
- User Interface Development: Designed and implemented intuitive UI demonstrations showcasing system capabilities
- Documentation & Visualization: Prepared comprehensive figures, diagrams, and video demonstrations for research dissemination
- Quality Assurance: Conducted extensive validation, testing, and deployment procedures to ensure system reliability
Technologies: Large Language Models, Chrome Extension API, Network Traffic Analysis, Browser Automation, Graph-based Algorithms
Research Goals
Aims to contribute to developing scalable and practical AI systems that bridge the gap between academic research and industry needs. Passionate about creating AI solutions that have real-world impact and can be deployed in production environments.
Technical Skills
- Data Analysis and Visualization
- Machine Learning Algorithms
- Natural Language Processing
- Large Language Model Integration
- Software Development (4+ years experience)
- Big Data Technologies
Approach to Research
Believes in practical, application-oriented research that can be translated into real-world solutions. Focuses on understanding both the theoretical foundations and practical implementations of AI systems, ensuring that research outcomes are not only academically rigorous but also industrially relevant.