JoonHo JANG > Alumni | 카이스트 응용인공지능 연구실

Research Interest

Domain Adaptation (e.g. Open-Set Domain Adaptation, Test-Time Adaptation)
Main research topic for my doctoral dissertation)
Active Learning, Learning with Noisy Label, Multi-label Classification)
Debiasing, Fairness, Representation Learning, Robustness)

Education

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea (Mar. 2019 - Feb. 2024)

Doctoral Degree Program in Industrial and Systems Engineering, AAILab
Academic Advisor: Professor Il-Chul Moon

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea (Mar. 2017 - Feb. 2019)

Master's Degree Program in Industrial and Systems Engineering, AAILab
Academic Advisor: Professor Il-Chul Moon

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea (Feb. 2012 ~ Feb. 2017)

Bachelor of Science in Industrial and Systems Engineering

Publication

International Conference

Suhyeon Jo, Donghyeok Shin, Byeonghu Na, JoonHo Jang, and Il-Chul Moon, Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy, ACM International Conference on Information and Knowledge Management (CIKM 2023), Birmingham, United Kingdom, Oct. 21-25, 2023
Yoon-Yeong Kim*, Youngjae Cho*, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, SAAL: Sharpness-Aware Active Learning, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023.
JoonHo Jang, Byeonghu Na, Dong Hyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon, Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022
HeeSun Bae*, Seungjae Shin*, Byeonghu Na, JoonHo Jang, Kyungwoo Song, and Il-Chul Moon. 2022. From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model. In The 39th International Conference on Machine Learning (ICML 2022), July 17-23, 2022, Baltimore, Maryland USA.
Seungjae Shin, Byeonghu Na, HeeSun Bae, JoonHo Jang, Hyemi Kim, Kyungwoo Song, Youngjae Cho, and Il-Chul Moon. 2022. Improving Group-based Robustness and Calibration via Ordered Risk and Confidence Regularization. In The Workshop on Spurious Correlations, Invariance, and Stability, International Conference on Machine Learning (SCIS at ICML 2022), July 22, 2022, Baltimore, Maryland USA.
Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang, and Il-Chul Moon, "LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning", Neural Information Processing Systems (NeurIPS), 2021. [acceptance rate: 26%]
Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song, Weonyoung Joo, Wanmo Kang, Il-Chul Moon "Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder" AAAI Conference on Artificial Intelligence (AAAI) 2021.
Seungjae Shin, Kyungwoo Song, Joonho Jang, Hyemi Kim, Weonyoung Joo, and Il-Chul Moon “Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation” Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP) 2020.
Kyungwoo Song, JoonHo Jang, Seungjae Shin, and Il-Chul Moon “Bivariate Beta-LSTM” AAAI Conference on Artificial Intelligence (AAAI) 2020.

International Journal

Joonho Jang, Seungjae Shin, Hyunjin Lee, Il-Chul Moon "Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model" Sensors 2020

Domestic Conference

신수진, 박준건, 김윤영, 장준호, 문일철, "Faster R-CNN을 이용한 위성 이미지에서의 교통 수단 객체 탐지'', 한국군사과학기술학회 추계학술대회, 2017.
신수진, 박준건, 김윤영, 장준호, 문일철, "Faster R-CNN의 중심 Convolution Neural Network 모델이 훈련 및 추론에 미치는 영향에 대한 분석", 대한산업공학회 추계학술대회, 2017.
장준호, 문일철, "Movie Recommendation System based on Matrix Factorization with Trust data & Side information", 대한산업공학회 춘계학술대회, 2017.

Award

Qualcomm Innovation Fellowship South Korea 2023 Finalist
한국인공지능학회 2021 하계학술대회 우수논문상 수상

Teaching Experience

Korea Advanced Institute of Science and Technology (KAIST),Daejeon, Republic of Korea

Data Structure and Analysis
Teaching Assistant, (2017 Fall, 2018 Spring, 2018 Fall, 2019 Spring, 2019 Fall, 2023 Spring)
Applications of AI and DM Technology
Teaching Assistant, (2021 Fall, 2022 Fall)

Members

Alumni