Insu Jeon
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Insu Jeon

Hello, I am a person who is interested in innovating the world with Artificial Intelligence (AI) technology for human well-being and happiness.
I graduated from UCLA with a major in Computer Science and a minor in Statistics.
I graduated from Seoul National University with a Ph.D. in Computer Science and Engineering under the supervision of Prof. Gunhee Kim in the Vision & Learning Lab. I was under Prof. SI Yoo in the Artificial Intelligence Laboratory during my master's period.
I have participated in various Artificial Intelligence (AI) projects, including Computer Vision, Natural Language Processing, Bayesian deep learning, Generative model, Meta-Learning, Federated Learning, and Large Language Model.
I have an entrepreneurship experience where I learned valuable lessons to be a tech leader.
Here is my CV.
If you have a question about me, ask to Chat-GPT using the search box of this web page (press ctrl + k and select the Lens option) or email me.



Ph.D.: Department of Computer Science and Engineering, Seoul National University, 2013 - 2023
B.S.: Major in Computer Science, Minor in Statistics, University of California in Los Angeles, 2009 - 2012

Work Experiences

Lead, AI Tech Lab & Product, QRAFT. Oct 2023 – present
  • Developing a Large Language Model (LLM)-based chatbot application for financial services: LLM-based summary of overseas disclosures and financial counseling chatbot for customers.
AI Researcher, Vision & Learning Laboratory, SNU. Mar 2017 – Sep 2023
  • Conducted advanced research in Generative models, Natural Language Processing (NLP), and Bayesian meta-learning.
AI Researcher, Everdoubling. Jun 2021 – Dec 2021
  • Participated in AI Grand Challenge; developed a math problem-solving AI engine using a General Language Model.
Chief Technology Officer (CTO), RippleAI. Feb 2018 – Dec 2019
  • Managed a team of 9 developers and engineers; developed an Instagram comment-generating bot.
Machine Learning Researcher, Artificial Intelligence Laboratory, SNU. Sep 2012 – Sep 2016
  • Developed ML algorithms for computer vision tasks such as defect detection, super-resolution, and registration.

Published Paper

Minui Hong, Junhyeog Yun, Insu Jeon, Gunhee Kim. "FedAutoAug: Augment Local Data via Shared Policy in Federated Learning." CVPR, 2024. (under review)
Insu Jeon, Minui Hong, Junhyeog Yun, Gunhee Kim. "Federated Learning via Meta-Variational Dropout." NeurIPS, 2023. [paper][code]
Insu Jeon, Junhyeog Yun, Minui Hong, and Gunhee Kim. "Data Augmentation via Generation Model in Military Aircraft Classification." KIMST, 2023.
Insu Jeon, Youngjin Park and Gunhee Kim. "Neural Variational Dropout Processes." ICLR, 2022. [paper] [code] [project]
Insu Jeon, Wonkwang Lee, Myeongjang Pyeon and Gunhee Kim. "IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks." AAAI, 2021. [paper] [code] [project]
Insu Jeon, D Kang, SI Yoo. "Blind image deconvolution using Student's-t prior with overlapping group sparsity." ICASSP, 2017. [paper] [pdf] [project]
Insu Jeon, SI Yoo. "Spatial kernel bandwidth estimation in background modeling." ICMV, 2016. [paper]


Unsupervised Learning-based Data Generation Research, Agency for Defense Development (ADD). Jun 2022 – Present
  • Improved military object recognition performance by 10% via Generative model-based data augmentation.
Neural Processing System Research, Samsung Advanced Institute of Technology. Mar 2018 – Sep 2019
  • Contributed to Samsung’s core AI vision technology and organized group activities for researchers.
Computer Vision Projects, Samsung Device Solutions Institute. Mar 2013 – Sep 2017
  • Optimized defect-monitoring systems in semiconductor display (SEM/OLED) production lines.


1th Kbig-contest – National Information Society Agency. Dec 2013
  • Developed Twitter hot issue forecaster using NLP algorithm and placed an encouragement award.

Teaching Experience

Special Lectures on Bayesian Data Analysis and Statistical Inference – GSSHOP. May 2019
  • Delivered lectures on Bayesian theory and statistical inference techniques for commercial data analysis. [part1] [part2] [part3]
Practical Guide to Deep Learning – Korea Banking Institute. Mar 2019
  • Conducted lectures on Deep Learning and Natural Language Processing.
Introduction to Generative Model with PyTorch – Fastcampus. Sep 2017 – Sep 2018
  • Taught courses on Deep Generative model and Bayesian Deep Learning. [code]
Special Issues in Machine Learning and Deep Learning – Seokyong University. Jun 2017
  • Performed lectures on Modern developments in Machine Learning, Deep Learning, and Artificial Intelligence.
Prerequisite Courses for Artificial Intelligence – SNU 4th Industrial Revolution Academy. May 2017
  • Prerequisite courses for understanding Artificial Intelligence - Linear Algebra, Probability, and Statistics. [part1]

Technical Skills

Computer Skills
  • Python, C/C++, Java, JavaScript, Objective C, OpenMP, CUDA, HTML, Bash, Windows, Mac OS, Linux, Huggingface, Langchain
  • Korean (native), English (proficient).
Last modified 2mo ago