Daeyoung Kim

Daeyoung Kim

Research Engineer

I am a Research Engineer at KT (Korea Telecom), developing an omni-modality model. I received my M.S. in AI from KAIST, advised by Prof. Edward Choi, and my B.S. in Computer Science and Information Security Convergence from Korea University.

My research interest is building reliable and robust multimodal AI. I am passionate about developing systems that can understand and process information from diverse sources—such as images, text, and speech—while ensuring their trustworthiness and reliability.

Download CV

Research Interests

  • Multimodal AI
  • Reliable Machine Learning
  • Natural Language Processing

Education

  • M.S. in Artificial Intelligence
    KAIST, 2021 - 2023
  • B.S. in Computer Science
    Korea University, 2015 - 2021

Publications

* denotes equal contribution.

VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models

Jeongho Ju*, Daeyoung Kim*, SunYoung Park*, and Youngjune Kim

Technical Report, 2024

PDF Model
OffsetBias: Leveraging Debiased Data for Tuning Evaluators

Junsoo Park*, Seungyeon Jwa*, Meiying Ren, Daeyoung Kim, and Sanghyuk Choi

In Findings in Empirical Methods in Natural Language Processing (EMNLP), 2024

PDF GitHub Dataset Model
Towards the Practical Utility of Federated Learning in the Medical Domain

Seongjun Yang*, Hyeonji Hwang*, Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, and Edward Choi

In Proc. of Conference on Health, Inference, and Learning (CHIL), 2023

PDF GitHub
Revisiting the Importance of Amplifying Bias for Debiasing

Jungsoo Lee*, Jeonghoon Park*, Daeyoung Kim*, Juyoung Lee, Edward Choi, and Jaegul Choo

In Proc. of Association for the Advancement of Artificial Intelligence (AAAI), 2023 (Oral Presentation)

PDF GitHub
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records

Daeyoung Kim, Seongsu Bae, Seungho Kim, and Edward Choi

In Proc. of Conference on Health, Inference, and Learning (CHIL), 2022

PDF GitHub
Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture

Seongsu Bae, Daeyoung Kim, Jiho Kim, and Edward Choi

In Proc. of Machine Learning for Health (ML4H), 2021 (Oral Presentation)

PDF
Empowering Sentence Encoders with Prompting and Label Retrieval for Zero-shot Text Classification

Jimin Hong*, Jungsoo Park*, Daeyoung Kim*, Seongjae Choi, Bokyung Son, and Jaewook Kang

Preprint, 2022

PDF