Hi all! I’m Jung Dayeon 👋🏻

📬 Contact Information

🎓 Education

📖 Bachelor

  • University: Chung-Ang University, Korea
  • Major: Industrial Security, Computer Science Engineering
  • GPA: 4.2 / 4.5
  • Years Attended: 2021-Present

💼 Professional Experience

🧪 CAU SVIL Lab

  • Lab: SVIL (Security Vision and Intelligence Lab)
  • Role: Undergraduate Researcher
  • Period: July 2022 - July 2023
  • Achievements:
    • Explored YOLO (You Only Look Once) object detection technology and its applications.
    • Conducted practical exercises in data analysis and model performance evaluation.

☁️ Init Cloud

  • Company Name: Init Cloud
  • Role: Backend Engineer
  • Period: September 2023 - August 2024
  • Achievements:
    • Built and maintained scalable backend systems.
    • Enhanced system performance, reducing response time by 15%.
    • Collaborated with a team of engineers to develop a cloud-based security solution.

📊 CAU BigData Lab

  • Lab: BigData Lab
  • Role: Undergraduate Researcher
  • Period: September 2024 - present
  • Research Interests:
    • Distributed systems
    • Generative AI platforms (LLM, sLLM)

🏆 Awards and Recognitions

  • 🥉 Encouragement Award – 2021 Idea Contest by the Electronic Commerce Association
  • 🥉 Bronze Award – Industrial Security Paper Competition, 2021
  • 🏅 Excellence Award – 2022 Davinci SW/AI Competition
  • 🏅 Excellence Award – CUAI Winter Conference, 2022
  • 🏅 Excellence Award – Sports Data Analysis and Utilization Competition, 2022
  • 🏅 Excellence Award – Financial Security Institute Paper Competition, 2022
  • 🥉 Bronze Award – 2023 Student Paper Competition, Korean Digital Content Association
  • 🏅 Special Award – K-PaaS Digital Social Innovation Service Development Contest, 2024
  • 🥈 Second Place (Promotion Award) – 2024 KOPIS Big Data Contest
  • 🏅 Contribution Award – Open Source Contribution Academy by Korea IT Business Promotion Association, 2024
  • 🏅 Excellence Award (1st Place) – University Patent Contest for Secure Data Utilization, 2024

🚌 Backend Projects

☁️ Init Cloud

  • Stack: Python, Java, Spring, JPA, Docker, K8S, AWS

🚀 Rocket Service

🚀 Rocket User and OAuth Service Implementation

  • Key Contributions:
    • Integrated GitHub with Rocket services (2023.08–2023.09)
    • Developed User Service (2024.02–2024.03, 2024.06)
    • Enabled Team Management through User Service (2024.02–2024.03)
  • Achievements:
    • Optimized JPA Queries: Enhanced API response times by 30–50% through resolving N+1 issues, minimizing lazy loading, and leveraging Fetch Join and Batch Size.
    • Strengthened Authentication:
      • Implemented Multi-Factor Authentication (MFA).
      • Enhanced password encryption using PBKDF2.
      • Optimized JWT strategies, reducing authentication-related incidents by 70%.

🛡️ Rocket Security Scan Report Service

  • Contributions:
    • Developed PDF generation for user-specific security scan results (2024.01–2024.02)
  • Achievements:
    • Expanded scan result accessibility by offering file-based downloads.

🔍 Rocket Scan Service

  • Contributions:
    • Built Rocket Checkov Scan Service (2023.10–2023.12)
    • Developed TeamPolicy functionality for team-based scans (2023.12–2024.01)
  • Achievements:
    • Migrated monolithic scan services to a Redis-based architecture.
    • Automated compliance document uploads to the database for easier management.

⚙️ Rocket Backend Service CI Implementation

  • Applied CI pipelines using GitHub Actions (2023.10).
  • Transitioned from monolithic to MSA with module-specific CI.

🐶 AWW Service

🛠️ K-PaaS-Based Terraform Visualization Service

  • Date: 2023.10–2023.11
  • Contributions: Enabled Terraform visualization on NHN Cloud K8S services.

🌟 OSSCA: Terraform Framework API with Go

🛠️ S-Developer Project (in Genians)

🔒 SASE Application Development Using Open Source Software

  • Date: 2023.07–2023.12
  • Contributions:
    • Automated Keycloak deployment on K8S.
    • Designed infrastructure for integrating Keycloak with internal systems.
  • Repository: Keycloak K8S

☕ Cazait: “Is there a seat available at the cafe?”

📱 Android App for Real-Time Cafe Seat Availability

  • Date: 2023.01–2023.02
  • Key Contributions:
    • Developed CRUD APIs for master accounts.
    • Implemented JWT-based authentication and authorization.
    • Collaborated on JPA-based database implementation.
    • Configured EC2 and Nginx.
  • Repository: Cazait Server

🤖 AI Projects

💳 Credit Card Fraud Detection using GAN and LGBM

  • Date: 2022.07–2022.10
  • Achievements: Designed ensemble models for imbalanced datasets.
  • Repository: FDGAN-L

🏅 Sports Data Analysis Competition

🧑‍💻 SNS Data Collection and Persona Analysis

🖱️ ETRI Research Project

🔒 CNN Model Development with OpenFHE

  • Date: 2024.04–2024.07

🛠 OpenSource Contribution Activities

🌐 Naver Cloud Terraform Provider

  • Period: July 2024 – August 2024
  • Technologies: Go, Terraform, AWS
  • Contributions:
    • Migrated Naver Cloud Load Balancer implementation from SDK-based to Framework-based version.
  • Repository: GitHub Pull Request #430

🔄 Litmus Chaos

  • Period: September 2024 – October 2024
  • Technologies: Go, Fuzz Testing
  • Contributions:
    • Developed fuzz tests for Chaos Infrastructure to enhance testing coverage.
  • Repository: GitHub Pull Request #4816

☕ Litmus Chaos JDK

  • Period: November 2024 – December 2024
  • Technologies: Java, Kubernetes
  • Contributions:
    • Developed a Java Development Kit (JDK) for Litmus to support Java developers using the framework.
  • Repository: GitHub Pull Request #29

📚 Publications and Papers

🎶 Analyzing K-POP Idol Popularity Factors Using Music Charts and New Media Data Using Machine Learning

  • Authors: Jiwon Choi, Dayeon Jung, Kangkyu Choi, Taein Lim, Daehoon Kim, Jongkyn Jung, & Seunmin Rho
  • Journal: JOURNAL OF PLATFORM TECHNOLOGY, 12(1), 55-66
  • Publication Year: 2024
  • Description: Explored the factors influencing K-POP idol popularity using machine learning techniques on data derived from music charts and new media platforms.
  • Link: KCI Article

📊 Advanced R-GAN: Generating Anomaly Data for Improved Detection in Imbalanced Datasets Using Regularized Generative Adversarial Networks

  • Journal: Alexandria Engineering Journal, Volume 111
  • Publication Year: January 2025
  • Description: Proposed Advanced R-GAN, a novel approach leveraging regularized GANs to generate synthetic anomaly data, enhancing anomaly detection in imbalanced datasets.
  • Link: ScienceDirect Article

🧑‍💻 Skills

  • Programming Languages: Java, Python, Go, SQL
  • Frameworks and Tools: Spring Boot, Docker, Kubernetes, Git, Terraform
  • Cloud Platforms: AWS, Naver Cloud