Researcher on Trustworthy/Responsible AI.
Love to learn new things.
Love to play the violin.

Biography

I am a Ph.D. student at the Data Intelligence Lab of KAIST EE and advised by Professor Steven Euijong Whang. I received B.S. in electrical engineering from KAIST in 2018.

My main research area is Trustworthy/Responsible AI, which includes fairness, robustness, interpretability, and transparency. I believe that these aspects will be indispensable in future machine learning systems. Among the trustworthiness aspects, I am especially interested in building fair AI systems that do not have demographic or individual disparities. To this end, I currently focus on lowering the barrier of fair AI development, achieving multiple aspects of trustworthiness, and building human-centered fair AI systems.

I am currently a research intern at NVIDIA Research, working with great mentors Weili Nie, De-An Huang, Arash Vahdat, and Anima Anandkumar.

Curriculum Vitae

Publications

Conference Papers

[C4] Sample Selection for Fair and Robust Training [Paper / Talk / Slides / Code]
Y. Roh, K. Lee, S. E. Whang, and C. Suh
NeurIPS 2021

[T1] Machine Learning Robustness, Fairness, and their Convergence (Tutorial) [​Paper / Talk / Slides]
J. Lee, Y. Roh, H. Song, and S. E. Whang
ACM SIGKDD 2021

[C3] FairBatch: Batch Selection for Model Fairness [Paper / Talk / Slides / Code]
Y. Roh, K. Lee, S. E. Whang, and C. Suh
ICLR 2021

[C2] 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 2021

[C1] FR-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 2020

Journal Papers

[J3] Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective [Paper]
S. E. Whang, Y. Roh, H. Song, and J. Lee
ArXiv 2021

[J2] Responsible AI Challenges in End-to-end Machine Learning [Paper]
S. E. Whang, K. Tae, Y. Roh, and G. Heo
IEEE Data Engineering Bulletin 2021

[J1] A 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)

Workshop Papers

[W1] Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach [Paper]
K. Tae, Y. Roh, Y. Oh, H. Kim, S. E. Whang
DEEM @ ACM SIGMOD 2019

Education

  • Ph.D. (Integrated with Master) in Electrical Engineering, KAIST, Sept. 2019 - Present

  • 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

  • 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 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