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<Kh.Kim / >

Summary

Hello, I am Kihwan Kim. Currently, I am a Frontend Engineer at unicorn startup RIDI, boasting over 6 years of industry and academic experience. For the last 2 years, my focus has been exclusively on front-end development. Before that, I dedicated 4 years to researching two key topics: visual analytics for explaining machine learning models and AI-driven user interfaces.

juljin1875@gmail.comLinkedInGitHub

Experience

RIDI Corporation
Frontend Engineer
2022.05-Present
RIDI website
2022.05-Present
I develop the frontend of a content platform that provides webtoons, web novels, comics, and e-books.
  • Implementing new features to meet various business requirements
  • Maintaining existing systems: refactoring, writing test codes
Next.js
TypeScript
React
Emotion
Jest
PHP
Twig
Tmax Enterprise
Researcher
2020.02–2022.04

Currently TmaxBizAI

Formerly TmaxData

React Porting of Legacy System
2021.01–2022.04
Converted products implemented with a jQuery-based internal frontend library — TOP (Tmax One Platform) to React.
  • Divided inadequately modularized existing code into appropriate modules and improved interfaces
  • Developed various new components
TypeScript
React
Sass
Material-UI
jQuery
AutoML Platform
2020.02–2022.04
Developed a platform that automates repetitive and tedious machine learning model development tasks.
  • Implemented a codeless development studio environment for non-experts
  • Researched and developed explainable AI — XAI (eXplainable Artificial Intelligence) technologies
  • Studied interfaces for effective interaction between AutoML engines and model developers
  • Developed a dashboard for the development, management, and operation of machine learning models
TypeScript
React
Sass
Material-UI
Python

Education

Ulsan National Institute of Science and Technology (UNIST)
Master of Science in Computer Science
2018.03–2020.02
Bachelor of Science in Technology Management (Multi-disciplinary major in Computer Science)
2013.03–2018.02
AI-centered UI/UX Design
2019.01–2020.02
Conducted research to answer the question, 'How should the UI be designed to better collect user preference data?'
  • Measured the impact of transparency on data quality in the explore-exploit problem of recommendation systems
  • Proposed a metric to evaluate the value of user logs from an AI perspective
  • Implemented a web-based movie recommendation system for experimental environments
Python
Flask
Surprise
jQuery
Modeling Web User Behavior
2019.02–2020.02
Conducted research to understand and model user behavior patterns in web environments.
  • Estimated user reward functions from behavior logs through Inverse Reinforcement Learning
  • Dealt with various web environments such as data analysis tools like Tableau, history education tools based on Wikipedia documents, and card games
  • Utilized data analysis tools such as Tableau and implemented a history education tool based on Wikipedia documents
Python
TensorFlow
Keras
scikit-learn

Publications

An Empirical Analysis on Transparent Algorithmic Exploration in Recommender Systems

Kihwan Kim

A Computing Research Repository (CoRR), 2108.00151, 2021

  • Investigated how to deliver random items to capture user preferences in recommendation systems
  • Implemented a web-based movie recommendation system used as an experimental environment
  • Proposed a metric to evaluate the value of user logs from an AI perspective
  • Measured the impact of transparency on data quality in the explore-exploit problem of recommendation systems
  • Recruited 94 participants from Amazon MTurk
  • Collected real usage log data and survey responses by having participants use a Netflix-like experimental environment

ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed

Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Kihwan Kim, Seungmin Jin, Sungahn Ko, Jaegul Choo

ACM International Conference on Information and Knowledge Management (CIKM), 2020

  • Preprocessed vehicle detection sensor data installed on the road surface by Korea Expressway Corporation
  • Categorized cases where attention worked effectively by patterns

Modeling Exploration/Exploitation Decisions through Mobile Sensing for Understanding Mechanisms of Addiction

Kihwan Kim, Sanghoon Kim, Chunggi Lee, Sungahn Ko

ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), 2019

  • Proposed a system to detect addiction disorders from smartphone usage logs through Inverse Reinforcement Learning

An Empirical Study on the Relationship Between the Number of Coordinated Views and Visual Analysis

Juyoung Oh, Chunggi Lee, Hwiyeon Kim, Kihwan Kim, Osang Kwon, Eric D. Ragan, Bum Chul Kwon, Sungahn Ko

A Computing Research Repository (CoRR), 2204.09524, 2018

  • Conducted experiments to see how the number of visualization charts affects data visual analysis
  • Had 44 participants use a visual analysis tool and solve data analysis tasks
  • Categorized users' analysis patterns through think-aloud protocol, recorded screens, and log data
  • Observed a positive correlation between the number of charts and task scores

시각화 기반 딥러닝 분석 기술

Jaesung Lee, Kihwan Kim, Chunggi Lee, Sungahn Ko

Noise & Vibration, Vol.27 No.6, 2017.11

  • Summarized and investigated visualization techniques for interpreting deep learning models