I'm a research scientist at NAVER AI LAB and NAVER CLOVA, 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.
I received a master's degree in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2016. During the master's degree, I researched on a scalable algorithm for robust subspace clustering (the algorithm is based on robust PCA and k-means clustering). Before my master's study, I worked at IUM-SOCIUS in 2012 as a software engineering internship. I also did a research internship at Networked and Distributed Computing System Lab in KAIST and NAVER Labs during summer 2013 and fall 2015, respectively.
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). If you are interested in joining our group, please send an email to me with your academic CV and desired topics.
[For the internship] We recommend a 6-month internship (especially if you are not a Korean citizen, at least 6-month working is required for the VISA process). The location will be Seoul, Korea [Google map], but a remote internship program can be considered depending on the situation. We are not hiring an undergraduate student for the internship. Lastly, if you are not a Korean citizen, the whole hiring process could be delayed due to the VISA process.
Reliable machine learning with limited supervision. Real-world machine learning models often suffer from unreliability issues; (1) the lack of generalizability to unseen biases or corruptions, (2) improper uncertainty estimation, (3) their decisions are not explainable to humans. To achieve reliable machine decisions, we need a large number of annotations in every possible situation, e.g., traffic signs with every possible weather condition, which is highly impractical and unachievable in most cases. Instead of collecting or generating all possible situations, my research interests focus on developing reliable machine learning models with only limited human supervision. In particular, I am interested in the following types of supervision: (1) human inductive bias without additional labeling, (2) extra multi-modal information related to the original task, (3) weak supervision, or semi-supervision which requires a reasonable number of additional annotations.
(C: peer-reviewed conference, W: peer-reviewed workshop, A: arxiv preprint, O: others)
(❋authors contributed equally)
See also at my Google Scholar.
Distributed at 2019 Hangul's day (한글날), [Full font list]
Deployed in Jan. 2019
Feb. 2016 - Feb. 2018
Deployed in 2017
Deployed in 2017
Aug. 2015 - Dec. 2015
Jun. 2012 - Jan. 2013