Mijoo Kim

About me

In the rapidly evolving world of AI, I am deeply interested in the next challenge we face: "How can we deploy AI safely?" To address this, I am dedicated to researching reliable and safe AI technologies. Specifically, my research focuses on uncertainty calibration and out-of-distribution detection.

News

[2023.10] One paper accepted in NeurIPS 2023 Workshop on Distshift
[2023.09] Joined as a visiting scholar Carnegie Mellon University IITP AI Intensive Program (Aug. 2023 ~ Feb. 2024)
[2022.09] Joined CVML @CAU

Education

M.S. Department of Artificial Intelligence, CAU, 2024 (expected)
- Advisor : prof. Junseok Kwon
B.S. Department of Computer Science, JBNU, 2022
- Advisor : prof. Hyungki Kim

Research Interest

Safety & trustworthy AI
Uncertainty calibration
Out-of-distribution detection
Differential privacy

Publication (International)

Changdae Oh, Mijoo Kim, Hyesu Lim, Junhyeok Park, Euiseog Jeong, , Zhi-Qi Cheng, Kyungwoo Song, “Towards Calibrated Robust Fine-Tuning of Vision-Language Models”, NeurIPS Workshop on Distribution Shifts, (2023) [pdf]

Hyungki Kim, Wonyong Lee, Mijoo Kim, Yoochan Moon, Taekyong Lee, Mincheol Cho, and Duhwan Mun, "Deep learning-based recognition of symbols and texts at an industrially applicable level from high-density piping and instrumentation diagram images", ESWA, (2021) [pdf]

Publication (Domestic)

Mijoo Kim, Junseok Kwon, “A Survey of Post-hoc Uncertainty Calibration Methods”, Summer Annual Conference of IEIE, (2023)

Wonyong Lee, Mijoo Kim, Duhwan Mun, Hyungi Kim, "Image format P&ID recognition technique using synthetic data and text-symbol Integrated detection", Korean Journal of Computational Design and Engineering, (2021)

Preprint

Mijoo Kim, Junseok Kwon, “Uncertainty Calibration With Energy Based Instance-wise Scaling In the Wild Dataset” (2024), under review

Changdae Oh*, Hyesu Lim*, Mijoo Kim, Jaegul Choo, Alexander Hauptmann, Zhi-Qi Cheng, Kyungwoo Song, "Towards Calibrated Robust Fine-Tuning of Vision-Language Models", (2024) [pdf] under review

Experience

[2023.09 ~ 2024.02] Visiting scholar with IITP AI Intensive Program, Carnegie Mellon University
[2021.09 ~ 2021. 12] Vision engineer Internship at prior development team, NextLab
[2019.04 ~ 2021.08] Undergraduate Research Student at Visual Computing Lab, JBNU

Awards

[2022] Top prize at Korean Society for Computational Design and Engineering
[2021] Excellence Prize at Korean Society for Computational Design and Engineering Summer Conference
[2020] Excellence Prize at School Idea Hackathon

Patent

Chang Geun Lee, Jeong Gi Park, Mijoo Kim, Hyoung Seok Kim, Hak Jin Kim, Min Seo Park, and Seyoung Park. AI-Based Road Control System and Method Using Drones. KOR Patent 10-2022-0143599. filed November 1, (2022)

Projects more

JUMO (Development of a Beverage Serving Robot) @CMU

Robotics Jetson orin nano Jetbot serving robot SLAM speech recognition

Team Project in AI on Edge with Robotics

Oct. 2023 ~ Feb. 2024

Assembly of Jetbot (an open-source robot based on NVIDIA Jetson Nano)
Real-time slam navigation & mapping
Build speech recognition pipeline
Robot motion control

Towards Calibrated Robust Fine-Tuning of Vision-Language Models @CMU

Team research project in Large-Scale Multimedia Analysis 23f

Sep. 2023 ~ Dec. 2023

Vision-Language-model CLIP Uncertainty calibration Robust fine-tuning

reviewing research overview & literature search
draft writing & code writing & experiment
publication : [pdf]

Implementation of Diffusion Attentive Attribution Maps @CMU

Implementation project in Natural Language Processing 23f

Sep. 2023 ~ Dec. 2023

NLP Explainable AI Stable Diffusion Model Attribution map

Theoretical study and pipeline structure analysis of stable diffusion
Implementation of Diffusion Attentive Attribution Map (DAAM)
Designing additional experiments and analysis for meaningful interpretation of attribution map
Technical report writing