Insu Jeon

graduated from UCLA with a major in computer engineering and a minor in statistics
currently working on the Ph.D. program at Seoul National University
Among artificial intelligence fields, working on NLP, computer vision, Bayesian deep learning, Generative models, and currently on Bayesian meta-learning.




Ph.D : Department of Computer Science and Engineering, Seoul National University, 2012 ~

B.S : Major in Computer Science, Minor in Statistics, University of California in Los Angeles, 2012

Published Papers

Insu Jeon, Youngjin Park and Gunhee Kim, Neural Variational Dropout Processes, ICLR, 2022.

Insu Jeon, Wonkwang Lee, Myeongjang Pyeon and Gunhee Kim, IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks, AAAI, 2021

Insu Jeon, D Kang, SI Yoo, Blind image deconvolution using Student’s-t prior with overlapping group sparsity, ICASSP, 2017.

Insu Jeon, SI Yoo, Spatial kernel bandwidth estimation in background modeling, ICMV, 2016.

Work Experiences

AI Researcher, Vision and Learning Laboratory, SNU, Mar 2017 – Present

  • Performing researches on Artificial Intelligence, NLP, and Bayesian meta-learning.

AI Researcher Intern, Ever-doubling LLC., Jun 2021 – Dec 2021

  • Participated in AI grand challenge and developed math problem solving AI engine.

Chief Technology Officer (CTO), RippleAI, Feb 2018 – Dec 2019

  • Developed and managed the company’s technical resources and led the development team.

AI Researcher, Artificial Intelligence Laboratory, SNU, Sep 2012 – Sep 2016

  • Participated in a variety of Machine Learning and Computer Vision related projects.


Unsupervised Learning-based Data Generation Research, Agency for Defense Development, Jun 2022 – Present

  • Improving object recognition performance via Generative model-based data augmentation in a data-scarce condition

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 the researchers.

Defect Detection, Super-Resolution, and Registration Project, Samsung Device Solutions Institute, Mar 2013 – Sep 2017

  • Designed and improved the defect monitoring system for the semiconductor display (SEM/OLED) production line.


1th Kbig-contest – National Information Society Agency, Dec 2013

  • Developed Twitter hot issue forecaster based on Machine Learning algorithm and placed an encouragement award.

Teaching Experience

Special Lectures on Bayesian Data Analysis and Statistical Inference – GSSHOP, May 2019

  • A lecture for Bayesian theory and statistical inference technique for commercial data analysis.

Practical Guide to Deep Learning – Korea Banking Institute, Mar 2019

  • A lecture for Deep Learning and Natural Language Processing.

Introduction to Generative Model with PyTorch – Fastcampus, Sep 2017 – Sep 2018

  • A lecture for Deep Generative model and Bayesian Deep Learning.

Special Issues in Machine Learning and Deep Learning – Seokyong University, Jun 2017

  • Recent developments in Deep Learning, Machine 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.

Technical Skills

Computer Skills:

  • Python, C/C++, Java, JavaScript, Objective C, OpenMP, CUDA, HTML, Bash, Windows, Mac OS, Linux


  • Korean (native), English (proficient).