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. I am a recipient of the Microsoft Research PhD Fellowship 2022.

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 was a research intern at NVIDIA Research in 2022, working with great mentors Weili Nie, De-An Huang, Arash Vahdat, and Anima Anandkumar.

Curriculum Vitae

Publications

  • 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 2022

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

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

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

  • 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

  • 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

  • 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)

  • 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

  • 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

Experience

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

  • 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 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