Hee Suk Yoon

Ph.D. Candidate @ KAIST EE
Georgia Institute of Technology ECE
(Georgia Tech)


About Me

I graduated from the Georgia Institute of Technology with a B.S. in Computer Engineering in 2021. After completing my undergraduate studies, I decided to continue my education and pursue a Ph.D. in the field of Artificial Intelligence/Deep Learning at the Korea Advanced Institute of Science and Technology (KAIST). I am currently working under the guidance of Professor Chang D. Yoo, whose expertise and leadership have been invaluable to my research.

My research focuses on the reliability of Artificial Intelligence/Deep Learning technologies for various modalities, including images, videos, and natural language. I am passionate about exploring the potential of these technologies and finding ways to improve their performance and reliability in real-world applications.

In my free time, I enjoy staying up-to-date with the latest developments in the field of AI/DL, as well as reading and learning about other areas of computer science and engineering. I am excited to continue my studies at KAIST and to see where my research takes me in the future!

Education

    • Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
      Ph.D. candidate (integrated) in Electrical Engineering (Artificial Intelligence/Deep Learning)
      Aug. 2021 - Present
      Advisor: Chang D. Yoo


    • Georgia Institute of Technology, Atlanta, GA, United States
      B.S. in Computer Engineering
      graduated May 2021
      Cumulative GPA: 3.92/4.0 (highest honors)

Publications

(* denotes equal contribution)

    • Counterfactual Two-stage Debiasing for Video Corpus Moment Retrieval
      Sunjae Yoon, Ji Woo Hong, SooHwan Eom, Hee Suk Yoon, Eunseop Yoon, Daehyeok Kim, Junyeong Kim, Chanwoo Kim, Chang D. Yoo
      ICASSP 2023


    • ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
      Hee Suk Yoon*, Joshua Tian Jin Tee*, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
      ICLR 2023
      [openreview] [arxiv]


    • SMSMix: Sense Maintained Sentence Mixup for Word Sense Disambiguation
      Hee Suk Yoon*, Eunseop Yoon*, John Harvill, Sunjae Yoon, Mark Hasegawa-Johnson, Chang D Yoo
      EMNLP 2022 (findings)
      [arxiv] [pdf]


    • Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue
      Sunjae Yoon, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, Chang D Yoo
      EMNLP 2022
      [arxiv] [pdf]


    • Selective Query-Guided Debiasing for Video Corpus Moment Retrieval
      Sunjae Yoon, Ji Woo Hong, Eunseop Yoon, Dahyun Kim, Junyeong Kim, Hee Suk Yoon, Chang D Yoo
      ECCV 2022
      [arxiv] [pdf]

Reviewer Experience

    • EMNLP 2022, Language Modeling and Analysis of Language Models Track

    • ICASSP 2023, Machine Learning for Signal Processing Track

    • ACL 2023

    Projects

    • Prediction of Relative Object Sizes in Images using Deep Learning
      2021-06-01 ~ 2021-12-31
      U-AIM (Role: Operator)

    • Enhancing CD SEM Image Quality and Inter-Facility Image Transfer Technology using Deep Learning
      2021-06-01 ~ 2023-06-01
      Samsung (Role: Operator)

    • Development of Uncertainty-Aware Agents Learning by Asking Questions
      2022-03-01 ~ 2027-03-01
      Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (Role: Operator)

    • Visual Dialogue System: Developing Visual and Language Capabilities for AI-Based Dialogue Systems
      2023-01-01~
      Korea Telecom (KT) AI (Role: Operator)