Software Engineering Internship at IUM-SOCIUS (Jun. 2012 - Jan. 2013)
I'm a research scientist at NAVER Clova AI Research (CLAIR), working on machine learning and its applications. Prior to working at NAVER, I worked as a research engineer at advanced recommendation team (ART) in Kakao from 2016 to 2018.
My research goal is to understand unexpected and less interpretable behaviors of machine learning (ML) models and to solve the problems in both practical and provable manners. In particular, I believe that it is important to solve four main research topics: (i) Understanding machine learning models by explainable ML, (ii) Building generalizable models to different biases, corruptions, or domains, (iii) Developing probabilistic machines with well-calibrated uncertainty measurement, (iv) Overcoming insufficient labeled data points by learning with minimal human supervision.
This section is written by generated font using my handwriting. You can play with my handwriting here. (이 섹션은 실제 제 손글씨로 생성한 폰트로 작성되었습니다. 제 손글씨 폰트는 이 곳에서 테스트해보실 수 있습니다.)
Hangul (Korean alphabet, 한글) originally consists of only 24 sub-letters (ㄱ, ㅋ, ㄴ, ㄷ, ㅌ, ㅁ, ㅂ, ㅍ, ㄹ, ㅅ, ㅈ, ㅊ, ㅇ, ㅎ, ㅡ, ㅣ, ㅗ, ㅏ, ㅜ, ㅓ, ㅛ, ㅑ, ㅠ, ㅕ), but by combining them, there exist 11,172 valid characters in Hangul. For example, "한" is a combination of ㅎ, ㅏ, and ㄴ, and "쐰" is a combination of ㅅ, ㅅ, ㅗ, ㅣ, and ㄴ. It makes generating a new Hangul font be very expensive and time-consuming. Meanwhile, since 2008, Naver has distributed Korean fonts for free (Nanum fonts, 나눔 글꼴).
In 2019, we developed a technology for fully-personalized Hangul generation only with 152 characters. We opened an event page where users can submit their own handwriting. The generated fonts will be distributed at this Hangul's day.
LINE is a major messenger player in east asia (Japan, Taiwan, Thailand, Indonesia, and Korea.) In the application, users can buy and use numerous emoijs a.k.a. LINE Sticker.
In this project, we recommended emojis to users based on their profile picture (cross-domain recommendation).
I developed and researched the entire pipeline of the cross-domain recommendation system and operation tools.
Kakao Advanced Recommendation Technology (ART) team (2016 ~ 2018)
Recommender Systems (Kakao services)
Feb. 2016 - Feb. 2018
I developed and maintained a large-scale real-time recommender system (Toros [PyCon Talk][AI Report]) for various services in Daum and Kakao. I mainly worked with content-based representation modeling (for textual, visual, and musical data), collaborative filtering modeling, user embedding, user clustering, and ranking system based on Multi-armed Bandit.
Textual domain:Daum News similar article recommendation, Brunch (blog service) similar post recommendation, Daum Cafe (community service) hit item recommendation.
Visual domain:Daum Webtoon and Kakao Page similar item recommendation, video recommendation for a news article (cross-domain recommendation).
Personalized Push Notification with User History (Daum, Kakao Page)
Deployed in 2017
I researched and developed personalized item application push system. Our personalized recommender system finds user's interest based on their activity in services. The system has been applied to Daum an Kakao Page mobile applications.
Large-Scale Item Categorization in e-Commerce (Daum Shopping)
Deployed in 2017
I developed a large-scale item categorization system for Daum Shopping. The problem is challenging because of its web-scale data size, unbalanced label distribution, and noisy label. We served operation tools and the categorization API using deep learning based item categorization model.
Research on deep learning normalization (Naver Labs)
Aug. 2015 - Dec. 2015
During my research internship in Naver Labs, I mainly worked with Batch Normalization (BN) techniques using C++ Caffe framework [code] and Lua Torch framework. I tested the implemented BN to AlexNet, Inceptionv2 and VGG architectures at ImageNet datasets
I researched on normalization techniques for sequential dataset, namely RNN.
Web developer (IUM-SOCIUS)
Jun. 2012 - Jan. 2013
I worked as web developer at IUM-SOCIUS. I developed and maintained internal admin tools and main service systems based on JAVA spring and Ruby on Rails.
I also developed and maintained internal tools including batch jobs (JAVA spring batch), internal statistic service (Python Django, MongoDB).
"Learning generalizable representations with CutMix and REBIS", NAVER Labs Europe (2019).