ML Engineer- Korean classification (BERT, KoBERT, KoGPT, GPT3 etc.)- Sensor data classification (S-ARIMA DNN based etc.)- Image classification (ResNET, CNN based etc.)- Object detection (Faster-R-CNN, YOLO etc)- Object Tracking (DeepSORT, Faster-R-CNN, YOLOv4 etc.)- Recommender System (Collaborative Filtering, NCF, Graph Embedding, GNN based etc.)- Image Generative (GAN, DCGAN etc.)- Image Segmentation (Mask R-CNN, YOLACT etc.)Developer1. Automatic Crawling Service- Java, Quarkus, PostgreSQL2. Web Assembler based page- C#, blazor, PostgreSQL

Refactoring- The structure of the complex code was changed to a simple architecture due to the existing mix-in structure and legacy code.- Delete the legacy code and apply API communication between modules through the interface.- Created code convention and reflected ml source code.Architecture- Created Functional Requirement, Nonfunctional Requirement, Deployment View, Use Case Diagram, Class Diagram of ABACUS product.- Created Deployment View, pipeline of new product.Automated Feature Engineering- Investigation and implementation of technology for automatic derived variable generation function in csv file uploaded by user.Credit Connect- Designed AWS architecture and developed data pipeline.- Created prototype API documents about this service.

Course : Develop Project : Software Distribution Risk management

I was doing several data preprocessing, malware analysis related projects in KMU InfoSec lab.role : data collect, preprocessing, Learning modelworked projects : - Detection PE32 malware based on Deep learning - Detection malicious document based on Deep learning and machine learning - Detection ransomware based on Deep learning - Detection malicious android app based on Deep learning - Detection malware using Elastic Search - Code Diffing using Machine learning