Researcher on Responsible & Safe AI.
Love to learn new things.
Love to play the violin.
Biography
I am a Ph.D. candidate at the Data Intelligence Lab of KAIST EE. I am extremely fortunate to have been advised by Prof. Steven Euijong Whang during my PhD journey. I am also a recipient of the Microsoft Research PhD Fellowship 2022.
My main research area is Responsible & Safe AI, which includes fairness, robustness, interpretability, and transparency. I believe that these aspects will be indispensable in future machine learning systems. Among these aspects, I am especially interested in building fair and robust AI frameworks that do not have demographic disparities and generalize well in new data distributions. To this end, I currently focus on 1) lowering the barrier to fair AI development, 2) ensuring fairness amid data distribution shifts, and 3) improving model fairness and robustness in large-scale scenarios (e.g., using large foundation models).
I was a research intern at Google DeepMind in 2023 and NVIDIA Research in 2022, working with wonderful mentors.
Curriculum Vitae / Google Scholar
I am in the industrial job market! Please feel free to contact me for any potential opportunities.
Publications
Responsible & Safe AI
Improving Fair Training under Correlation Shifts [Paper / Slides / Code]
Y. Roh, K. Lee, S. E. Whang, and C. Suh
ICML 2023Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data [Paper / Code]
Y. Roh, W. Nie, D. Huang, S. E. Whang, A. Vahdat, and A. Anandkumar
TMLR 2023Sample Selection for Fair and Robust Training [Paper / Talk / Slides / Code]
Y. Roh, K. Lee, S. E. Whang, and C. Suh
NeurIPS 2021Machine Learning Robustness, Fairness, and their Convergence (Tutorial) [Paper / Talk / Slides]
J. Lee, Y. Roh, H. Song, and S. E. Whang
ACM SIGKDD 2021FairBatch: Batch Selection for Model Fairness [Paper / Talk / Slides / Code]
Y. Roh, K. Lee, S. E. Whang, and C. Suh
ICLR 2021Responsible AI Challenges in End-to-end Machine Learning [Paper]
S. E. Whang, K. Tae, Y. Roh, and G. Heo
IEEE Data Engineering Bulletin 2021FR-Train: A Mutual Information-Based Approach to Fair and Robust Training [Paper / Talk / Slides / Code / KAIST Breakthroughs]
Y. Roh, K. Lee, S. E. Whang, and C. Suh
ICML 2020Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach [Paper]
K. Tae, Y. Roh, Y. Oh, H. Kim, and S. E. Whang
DEEM @ ACM SIGMOD 2019
Data-CEntric AI
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective [Paper]
S. E. Whang, Y. Roh, H. Song, and J. Lee
VLDB Journal 2023 (Early Access from 2022)Inspector Gadget: A Data Programming-Based Labeling System for Industrial Images [Paper]
G. Heo, Y. Roh, S. Hwang, D. Lee, and S. E. Whang
VLDB 2021A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective [Paper]
Y. Roh, G. Heo, and S. E. Whang
IEEE TKDE 2021 (Early Access from 2019)
Experience
Google DeepMind, Mountain View, CA | Jun. 2023 - Dec. 2023
Research Intern
Mentors: Zhe Zhao, Sunny Liu, Huan Gui, Jeremy Yuan, Lichan Hong, and Ed ChiNVIDIA, Santa Clara, CA (Remote) | Jun. 2022 - Dec. 2022
Research Intern
Mentors: Weili Nie, De-An Huang, Arash Vahdat, and Anima Anandkumar
Education
Ph.D. (Integrated with Master) in Electrical Engineering, KAIST | Sept. 2019 - Feb. 2024 (Expected)
Master in Electrical Engineering, KAIST | Sept. 2018 - Aug. 2019
B.S. in Electrical Engineering & Minor in Intellectual Property, KAIST | Feb. 2014 - Aug. 2018 (Summa Cum Laude )
Korea Science Academy | Feb. 2011 - Feb. 2014
Honors
Microsoft Research PhD Fellowship | 2022
Best Research Achievement Award, KAIST EE | Spring 2022
Research Breakthroughs, KAIST | Spring 2021
The Qualcomm Innovation Fellowship | Dec. 2020
Best TA Award, KAIST | Jun. 2020
Department Honors (Top-3) Scholarship, KAIST | Spring 2017
Dean's List, KAIST | Spring 2016
National Science & Engineering Scholarship | Spring 2016 - Spring 2018
Talks
Machine Learning Fairness and its Convergence with Robustness
Tutorial @ IEEE BigComp Conference | Feb. 2023
Machine Learning Robustness, Fairness, and their Convergence
Tutorial @ ACM SIGKDD Conference | Aug. 2021
Responsible AI Techniques for Model Training
TechTalk to the TensorFlow team @ Google Korea | Feb. 2020
CONtact
yuji(dot)roh(at)kaist(dot)ac(dot)kr