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muk-jjang/README.md

SEONGJOON YOON

πŸŽ“ Undergraduate Student at Kyung Hee University

I’m interested in solving optimization problems such as logistics and routing problems through reinforcement learning.

πŸ“§ Email: whgdk0911@khu.ac.kr


πŸŽ“ Career and Academic Experience

Company / Lab / School Position Period
Tilda Corp ICT Intern 2024-09 β€” now
AIMS Lab, KHU IE Undergraduate Research Student 2024-01 β€” 2024-08
KHU Software Convergence Undergraduate(Double Major) 2024-02 β€” now
KHU Industrial and Management Engineering Undergraduate 2022-02 β€” now

πŸ–₯ Technologies

Programming Languages

cpp

Data Science Libraries

Machine Learning Frameworks

Pytorch Tensorflow

Databases

Frontend & Mobile Development

Collaboration Tools


πŸ’Ό Project Experience

Project Title Description Period
Casting Process Control Optimization Solution based on Digital-Twin Through domain and data analysis, we selected important control variables in the casting process. XGB, LightGBM, EXT, FFTransformer, etc. were used to build the Digital-Twin model. Based on the learned Digital-Twin Bayesian optimization was used to derive the optimal control value for the state. 2024.11 - 2024.11
Optimizing Bundled Delivery Routes Using Reinforcement Learning in Dynamic Event Situations Route optimization in dynamic events using the RL4CO framework. Example: Assigning drivers to deliveries during dynamic events. 2024.04 - Present
Combining Plastic Texture Data Augmentation Techniques for Reverse Vending Machine System Implementing an RVM system in line with Korean recycling policies(EfficientNet, ResNet ,Light U-Net) 2024.04 - 2024.06
Recommend Accommodations and Routes Based on Jeju Island Travel Log Data Using algorithms like Xgboost, MF-SVD to recommend accommodations. Optimizing routes to recommended destinations using OR-Tools. 2024.01 - 2024.02
2D Object Detection Contest by Gyeonggi-do Autonomous Driving Center Detecting pedestrians, bicycles, trucks, cars, buses, and motorcycles using YOLOv5. Using HIC-YOLOv5 for detecting small objects. 2023.10 - 2023.12
CJ Logistics Distribution Strategy and Optimal HUB Determination Determining the best locations for hub terminals using Gravity Model and Demand Allocation Model. 2023.05 - 2023.07
Analyzing Lecture Reviews with LDA Topic Modeling in Everytime Application Participated in data crawling for analysis. 2023.04 - 2023.05
Textual Emotion Analysis and Image-Based Emoticons-Recommendation System Labeling data sentiment using GPT API. Fine-tuning BERT and analyzing emoji emoticon similarity using CLIP. 2023.01 - 2023.02

πŸ† Awards

  • 2023.12: κ²½ν¬λŒ€ν•™κ΅ μ‚°μ—…κ²½μ˜κ³΅ν•™κ³Ό ν•™μˆ λŒ€νšŒ μž₯렀상
  • 2023.12: 경기도 μžμœ¨μ£Όν–‰μ„Όν„° 2D 객체탐지 은상 (μœ΅ν•©κΈ°μˆ μ—°κ΅¬μ› 주체)
  • 2024.04: κ²½ν¬λŒ€ν•™κ΅ 창의자율과제 곡λͺ¨μ „ μ„ μ •

🎯 Extracurricular Activity

  • 2024.01 - Present: κ΅­λ‚΄ 졜초 데이터뢄석 동아리 BOAZ 데이터뢄석 νŠΈλž™
  • 2024.01 - Present: κ΅­λ‚΄ 졜초 데이터뢄석 동아리 BOAZ κΈ°νšνŒ€
  • 2023.01 - 2023.05: κ²½ν¬λŒ€ν•™κ΅ 데이터뢄석 동아리 KHUDA

🐻 Farm

Pinned Loading

  1. Daily Daily
    1
    🌞 Morning    45 commits  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘  31.9%
    2
    πŸŒ† Daytime    38 commits  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘  27.0%
    3
    πŸŒƒ Evening    50 commits  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘  35.5%
    4
    πŸŒ™ Night       8 commits  β–ˆβ–β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘   5.7%
  2. Autonomous-driving_2d_object_detection Autonomous-driving_2d_object_detection Public

    경기도 μžμœ¨μ£Όν–‰μ„Όν„°μ—μ„œ μ§„ν–‰ν•œ 2d 객체 탐지 곡λͺ¨μ „

    Jupyter Notebook 1

  3. CJ-Logistics-Distribution-Strategy-and-Optimal-HUB-Determination CJ-Logistics-Distribution-Strategy-and-Optimal-HUB-Determination Public

    2023-1 SCM ν…€ ν”„λ‘œμ νŠΈ CJλŒ€ν•œν†΅μš΄ μœ ν†΅μ „λž΅ 및 졜적의 HUB κ²°μ •

    1

  4. RL_Practice RL_Practice Public

    Jupyter Notebook 1

  5. NCO-solvers-for-VRP NCO-solvers-for-VRP Public

    Forked from wuuu110/NCO-solvers-for-VRP

  6. rl4co rl4co Public

    Forked from ai4co/rl4co

    A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)

    Python