Sangmin Lee

Machine Learning Engineer

"저는 Machine Learning Engineer을 목표로 경기대학교에서 공학사, 이학석사를 취득하였고, 해당 과정을 통해 연구 경력은 4년을 쌓았으며 다양한 분야의 역량 향상을 위해 노력해왔습니다.
경험을 통해 쌓은 문제해결역량을 통해 산업 발전에 기여하는 것이 목표입니다."

Technical Writer @Slashpage

Education

I am pleased to introduce my educational background.

Kyonggi University / M.S. of Computer Science (2022.03 ~ 2024.02)

G. P. A: 4.25 / 4.5

Transcript: https://drive.google.com/file/d/1M9FXnkug1mlgOMzFjaqaePkw-dTLapX-
Graduate Certificate: https://drive.google.com/file/d/1MAzl9yFkpjk7_8OnHp7P3RMP70OTpuuW
Research Field : Network, Deep Learning (Recommendation System, Computer Vision, Graph Neural Network)
Advisor : Prof. Namgi Kim (Kyonggi university)

Kyonggi University / B.S. of Computer Engineering (2015.03 ~ 2022.02)

G. P. A: 3.62 / 4.5

Transcript: https://drive.google.com/file/d/1M787Fnq8NTA9b-NVVdzjAuyuUKxQutPD
Graduate Certificate: https://drive.google.com/file/d/1M98UlQGH_CCHMAJs8Lhc2LCZxLdBqfc7
Research Field : Deep Learning(Computer Vision)

Honors & Awards

The honors I have had and the awards I have received are as follows.

Top 0.04%

DACON Competition Rank

11th / 104,243th

(2024.03.26)

Top 4.41%

DACON Code Sharing Rank

110th / 2,495th

(2024.02.27)

212 EA

Programmers Ranking

18,378th, 1,421 point

(2024.01.27)

87,863th

BAEKJOON Rank: Silver 5

(2024.01.27)

Participation

Kyonggi University Department of AI and Computer Science

No. 2021-0005

Best Paper

Paper: Health Trainer Web Services with PostNet

KIIT, No. 21-222

Publication

The results of my efforts to build artificial intelligence capabilities are as follows.

Master’s thesis

Optimization methods of Graph Convolution Networks for Recommendation Systems

International journal

Deep Learning Model Ensemble for the Accuracy of Classification Degenerative Arthritis

Weighted Forwarding in Graph Convolution Networks for Recommendation Information System

Embedding Enhancement method for LightGCN in Recommendation Information Systems

Learning optimization method for Graph Neural Network-Based Graph Convolutional Network Optimization

International conference

Egress Initialization for Graph Convolution Network in Recommendation Systems

Thin Graph Convolution Network in Recommendation Systems

Over-smoothing in LightGCN doesn’t happen much

Leverage time-based data to improve recommendation system accuracy

Using Deep Learning for Medical Automation in Diagnosis of Degenerative Arthritis

Classification of degenerative arthritis using Xception model in radiographic images

Domestic journal

Analyzing the use of time-based user behavior data to improve the performance of graph neural network-based recommendation systems

Improved Pancreas Segmentation using Multiple Concatenated U-Net Model for Medical Image Systems

Domestic conference

Analysis of performance of unsupervised anomaly detection methods according to anomaly score threshold selection criteria

Analysis of Traffic Flow Prediction Performance According to Spatial-Temporal Attention Module Structures in GNN-based Deep Learning Model

Time-based data Analysis for improving recommendation system performance

A Study on the Flow Classification Method Using Auto-Encoder Model in SDN Environment

Optimizing caching servers using a recommender system

Implementation of attendance system through RFID tags

Accuracy analysis of convolutional neural network models for overlapped character image classification

Health Trainer Web Services with PostNet

Project

The projects to develop my software skills are as follows.

💬

Optimizing GCN for Recsys (23.01 ~ 23.12)

WF Technique, Egress Technique, Non-combination architecture

Role: Project leads and researchers

Research Field : Graph Convolutional Network, Recommendation Systems, Optimization techniques.

Project result: https://github.com/d9249/WFGR, https://github.com/d9249/OptGCN

Description: During the second year of his master's program, he proposed WF technique, Egress technique and Non-combination architecture for optimizing graph convolutional networks in recommendation systems, and improved learning speed and accuracy through this project.

🗃️

Smart I.o.T Lab Homepage (21.12 ~ 22.04)

Role: Front Developer

Skill : Vue.js, Node.js, JavaScript

Project Result: https://netlab.kyonggi.ac.kr/

Description: Creation of the Smart I.o.T Lab website and addition of features commissioned by Prof. Nam-Ki Kim

🧞‍♂️

IA^2S (Image Analysis Automation System)
(21.10 ~ 22.02)

Role: Team Leader, Front Developer

Skill : JavaScript, Django, OpenCV, Python 3.7.

Project Result: https://github.com/HHFEHH/crack-automation

Description: Web services for crack flattening, crack length measurement, and data automation to improve existing methods for physical exterior wall repair and building maintenance companies.

📔

Fit-sibang (21.03 ~ 21.06)

Role: Team Leader, Front Developer

Skill : React.js, Node.js, JavaScript

Project Result: https://github.com/KGU-Code-15/fit-sibangI

Description: Made as a web app, it can be accessed on any device and helps you exercise through posture estimation using AI.

External Activities

Activities to develop my communication and research skills are as follows.

💬

LG Aimers (24.01 ~ 24.02)

Role: 4th semester.

Link: https://github.com/d9249/LG_Aimers

Research Field: Deep Learning

Description: LG Aimers is LG Group's youth education program that provides AI education and the opportunity to experience participating in AI hackathons that deal with LG's actual data.

🗃️

Pseudo-lab (22.09 ~ 22.11)

Team: 그래프로 설득하기.

Link:https://www.notion.so/chanrankim/ac12010e21af4431a5f3e8ae2bdc4ee3?pvs=4

Research Field : Graph Neural Network

Description: Conduct projects in their respective domains that utilize Graph to solve real-world needs, from defining the problem to why the need should be met with Graph.

🧞‍♂️

DIYA (21.03 ~ 23.02)

4th Team: Computer Vision

Role: Team members

Research Field : Deep Learning, Computer Vision(paper review, participate in competition).

5th Team: Computer Vision, Graph Neural Network, Pytorch

Role: Team members

Research Field : Computer Vision, Graph Neural Network, Deep Learning.

Certificate: https://drive.google.com/file/d/1NxSxsWUAXOWbk2WgKtwxHrU4dsBsbY8Y

💡

CS224W-KOR (22.07 ~ 22.09)

Role: Team members

Research Field : Graph Neural Network.

Project Result: https://cs224w-kor.github.io/blog/

Description: CS224W, presenting and organizing the parts they studied and translating them into Korean.

📔

7th college student coding camp
(21.06 ~ 21.07)

Certificate: https://drive.google.com/file/d/1NzwgCVIDXgRZPjK3mbp-L94mf2fR9vRY

Competition

The list of artificial intelligence competitions I participated in to improve my problem-solving skills is as follows, and I have organized the competitions that ranked in the top 10%.