I'm a research scientist and tech leader at NAVER AI Lab, working on machine learning and its applications. In particular, my research interests focus on bridging the gap between two gigantic topics: reliable machine learning tasks (e.g., robustness [C3, C9, C10, W1, W3], de-biasing or domain generalization [C6, A6], uncertainty estimation [C11, A3], explainability [C5, C11, A2, A4, W2], and fair evaluation [C5, C11]) and learning with limited annotations (e.g., multi-modal learning [C11], weakly-supervised learning [C2, C3, C4, C5, C7, C8, C12, W2, W4, W5, W6, A2, A4], and self-supervised learning). I have contributed large-scale machine learning algorithms [C3, C9, C10, C13] in NAVER AI Lab as well. Prior to working at NAVER, I worked as a research engineer at the advanced recommendation team (ART) in Kakao from 2016 to 2018.
NAVER AI Lab is looking for motivated research internship students / regular research scientists (topic: real-world biases, uncertainty estimation, robustness, causality, explainability, large-scale learning, self-supervised learning, multi-modal learning, ...). Our mission is to perform impactful long-term AI research to make AI more beneficial and contribute to the AI community. We, therefore, expect very strong publication records for the applicants. See a more detailed job description here. If you are interested in joining our group, please send an email to me (or naverai at navercorp.com) with your academic CV and desired topics.
If you are interested in the internship position, you have to aware that we expect at least 6-month internship period and we are not hiring undergratuate students (or students who don't have enough publication records). The location will be Seoul, Korea [Google map], but a remote internship program can be considered depending on the situation. Lastly, if you are not a Korean citizen, the whole hiring process could be delayed due to the VISA process.
_7/2021 : Co-organizing the NeurIPS Workshop on ImageNet: Past, Present, and Future! [webpage]
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 (named 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 full generated font list can be found in [this link]. Details for the generation technique used for the service was presented in Deview 2019 [Link].
This work was also extended to the few-shot generation based on the compositionality. See the papers in AI for Content Creation Workshop (AICCW) at CVPR 2020 (short paper) [Link], ECCV 2020 (full paper) [Link], AAAI 2021 [Link], and ArXiv preprint [Link].
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
The mobile push service (or alert system) is widely-used in mobile applications to attain a high user retention rate. However, a freqeunt push notification makes a user feel fatigue, resulting on the application removal. Usually, the push notification system is a rule-based system, and managed by human labor. In this project, we researched and developed a personalized push notification system based on user activity and interests. The system has been applied to Daum an Kakao Page mobile applications. More details are in our paper.
Large-Scale Item Categorization in e-Commerce (Daum Shopping)
Deployed in 2017
An accurate categorization helps users to search desired items in e-Commerce based on the category, e.g., clothes / shoes / sneakers. However, the categorization is usually performed based on rule-based systems or human labor, which leads to low coverage of categorized items. Even the automatic item categorization is difficult due to its web-scale data size, the highly unbalanced annotation distribution, and noisy labels. I developed a large-scale item categorization system for Daum Shopping based on a deep network, from the operation tool to the categorization API.
Research internship (Naver Labs)
Aug. 2015 - Dec. 2015
During the internship, I implemented batch normalization (BN) to AlexNet, Inception v2 and VGG on ImageNet using Caffe. I also researched batch normalization for sequential models, e.g., RNN using Lua Torch.
Software engineer (IUM-SOCIUS)
Jun. 2012 - Jan. 2013
I worked as web developer at IUM-SOCIUS. During the internship, I developed and maintained internal batch services (JAVA spring batch), internal statistics service (Python Flask, MongoDB), internal admin tools (Python Django, MySQL), and main service systems (JAVA spring, Ruby on Rails, MariaDB).
Education and Career
M.S. (2014.03 - 2016.02), School of Electrical Engineering, KAIST
B.S. (2009.03 - 2014.02), School of Electrical Engineering and School of Management Science (double major), KAIST
Tech leader / research scientist at NAVER AI Lab (Feb. 2018 - Now)