Hee Suk Yoon
(윤희석)
Ph.D. Candidate @ KAIST EE
Georgia Institute of Technology ECE
(Georgia Tech)
Ph.D. Candidate @ KAIST EE
Georgia Institute of Technology ECE
(Georgia Tech)
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!
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)
* denotes equal contribution
denotes selected publications
[C]: Conference Paper [W]: Workshop Paper [J]: Journal Paper
[C14] BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation
Hee Suk Yoon*, Eunseop Yoon*, Joshua Tian Jin Tee*, Kang Zhang, Yu-Jung Heo, Du-Seong Chang, Chang D. Yoo
ECCV 2024
[arxiv]
[ J1] Physics Informed Distillation for Diffusion Models
Joshua Tian Jin Tee*, Kang Zhang*, Hee Suk Yoon, Dhananjaya Nagaraja Gowda, Chanwoo Kim, Chang D. Yoo
TMLR 2024
[paper] [code]
[C13] LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognition
Eunseop Yoon*, Hee Suk Yoon*, John Harvill, Mark Hasegawa-Johnson, Chang D. Yoo
INTERSPEECH 2024 (oral)
[paper] [arxiv]
[C12] TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback
Eunseop Yoon*, Hee Suk Yoon*, SooHwan Eom*, Gunsoo Han, Daniel Wontae Nam, Daejin Jo, Kyoung-Woon On, Mark Hasegawa-Johnson, Sungwoong Kim, Chang D. Yoo
ACL 2024 (findings)
[paper] [arxiv] [code]
[C11] C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
Hee Suk Yoon*, Eunseop Yoon*, Joshua Tian Jin Tee, Mark Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
ICLR 2024
[paper] [arxiv] [code] [poster] [slides]
[C10] ADAMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition
SooHwan Eom, Eunseop Yoon, Hee Suk Yoon, Chanwoo Kim, Mark Hasegawa-Johnson, Chang D. Yoo
ICASSP 2024
[paper] [arxiv] [poster]
[C9] SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
Hyun Ryu, Sunjae Yoon, Hee Suk Yoon, Eunseop Yoon, Chang D. Yoo
AAAI 2024
[paper] [arxiv] [code]
[C8] HEAR: Hearing Enhanced Audio Response for Video-grounded Dialogue
Sunjae Yoon, Dahyun Kim, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, Chang D. Yoo
EMNLP 2023 (findings)
[paper] [arxiv] [code]
[W2] One-Shot and Few-Shot Exemplification Modeling
John Harvill, Hee Suk Yoon, Eunseop Yoon, Mark Hasegawa-Johnson and Chang D. Yoo
EMNLP 2023 Workshop on Generation, Evaluation & Metrics (GEM 2023)
[paper]
[C7] Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Eunseop Yoon*, Hee Suk Yoon*, Dhananjaya Gowda, SooHwan Eom, Daehyeok Kim, John Harvill, Heting Gao, Mark Hasegawa-Johnson, Chanwoo Kim, Chang D. Yoo
INTERSPEECH 2023
[paper] [arxiv] [poster]
[W1] One-Shot Exemplification Modeling via Latent Sense Representations
John Harvill, Hee Suk Yoon, Eunseop Yoon, Mark Hasegawa-Johnson and Chang D. Yoo
ACL 2023 Workshop on Representation Learning for NLP (RepL4NLP 2023)
[paper] [poster]
[C6] INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognition
Eunseop Yoon*, Hee Suk Yoon*, John Harvill, Mark Hasegawa-Johnson, Chang D. Yoo
ACL 2023 (findings)
[paper] [arxiv]
[C5] 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
[paper] [arxiv] [poster] [code]
[C4] 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 (oral)
[paper]
[C3] 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)
[paper] [arxiv]
[C2] Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue
Sunjae Yoon, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, Chang D. Yoo
EMNLP 2022
[paper] [arxiv] [poster]
[C1] 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
[paper] [arxiv] [code]
Excellent Paper Award, 2024 Summer Conference of the Korean Artificial Intelligence Association: 2024.08.15
CAU-AI Core Technology Seminar (Navigating Uncertainty Challenges in Classification and Language Generation): 2024.03.22
Transactions on Machine Learning Research (TMLR)
International Conference on Learning Representations (ICLR): 2025
Conference on Neural Information Processing Systems (Neurips): 2024
IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2024
9th Workshop on Representation Learning for NLP (RepL4NLP) 2024
European Conference on Computer Vision (ECCV): 2024
Conference on Computer Vision and Pattern Recognition (CVPR): 2024
Association for Computational Linguistics Rolling Review (ARR): 2024
Association for Computational Linguistics (ACL): 2023
International Conference on Acoustics, Speech & Signal Processing (ICASSP): 2023, 2024
Empirical Methods on Natural Language Processing (EMNLP): 2022, 2023
International Conference on Learning Representations Tiny Papers Track (ICLR TinyPapers): 2024
Statistical Learning Theory 2024 Spring
Introduction to Machine Learning 2023 Fall, 2024 Fall
Introduction to Reinforcement Learning 2023 Spring
Signals and Systems 2022 Spring, 2022 Fall
Hwaseong City-KAIST Semiconductor Specialized Curriculum - Large Language Models 2023
Seongnam-KAIST Next Generation ICT Research Center EE Co-op+ Joint Research Program 2023 Fall
Seongnam-KAIST Next Generation ICT Research Center Machine Learning and Big Data Course: 2021, 2022, 2023
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)
Multi-modal Generative AI for Summarization
2023-09-01 ~ 2024-09-01
Samsung Speech Recognition & Natural Language Processing Lab (Role: Supporter)
Visual Dialogue System: Developing Visual and Language Capabilities for AI-Based Dialogue Systems
2023-01-01~
Korea Telecom (KT) AI2XL Laboratory (Role: Operator)
Enhancing CD SEM Image Quality and Inter-Facility Image Transfer Technology using Deep Learning
2021-06-01 ~ 2023-06-01
Samsung Metrology & Inspection (Role: Operator)
Prediction of Relative Object Sizes in Images using Deep Learning
2021-06-01 ~ 2021-12-31
U-AIM (Role: Operator)