Summary
Joonhyung Cho is a machine learning engineer with 9 years of experience applying research-grade ML to production recommendation and ranking systems across e-commerce and delivery platforms. He has an academic track record (one domestic and six international papers, multiple conference presentations, and 400+ citations) and practical impact building real-time banner recommendation, sequential recommenders, and lightweight inference pipelines at MUSINSA, Yogiyo, and KREAM. His work spans classical and deep methods—LightGBM, XGBoost, DeepFM, BERT4Rec and model compression techniques like progressive layer dropping—plus big-data engineering with PySpark. He has repeatedly turned research ideas into deployed features (e.g., dynamic take-rate scoring, review-ranking with multimodal signals, and 16 distinct recommender models), and was recognized internally as a Tech Star. A full-scholarship undergraduate and former research officer, he blends rigorous academic training from Yonsei with hands-on product delivery in Seoul.
9 years of coding experience
4 years of employment as a software developer
Exchange Student, Exchange Student at Northern Kentucky University
석박통합 박사수료 Information & Industrial Engineering, 석박통합 박사수료 Information & Industrial Engineering at Yonsei University
Bachelor's degree (Major) Information System (Duble Major) Industry Management Engineering, Bachelor's degree (Major) Information System (Duble Major) Industry Management Engineering at Hansung University