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<Kh.Kim / >

Summary

I’m Kihwan Kim, a Frontend Engineer. At RIDI, I worked on large-scale content platforms, focusing on structural improvements and user experience optimization. Currently, at Levvels, a subsidiary of Dunamu, I continue to enhance frontend architecture and design interaction layers for commerce-oriented services. During my master’s studies, I researched visual analytics and AI-driven interfaces, and I’ve since integrated that analytical mindset into practical frontend development to design flexible and scalable user experiences. My experience across both industry and academia has given me a balanced perspective — not only on technical craftsmanship but also on problem framing and cross-functional collaboration.

juljin1875@gmail.comLinkedInGitHub

Experience

Levvels
Frontend Engineer
Jul 2025–Present

Owned the frontend for a new commerce service, refining structure and UX through cart and auth refactors, analytics and communication tooling, and standardized interactions.

Commerce Frontend Development
Jul 2025–Present
Designed and developed the frontend architecture for Levvels’ new commerce platform, including creator-facing channel experiences.
  • Implemented shopping cart and product detail pages alongside creator channel and post experiences with responsive layouts, accessibility improvements, and live end-user views
  • Designed API integration and client-side caching logic
  • Refactored authentication flow to simplify login and sign-up experience
  • Integrated Amplitude and ChannelTalk for unified analytics and communication
  • Built an iframe-based real-time preview system covering both editor and published views
Design System & Interaction Standardization
Sep 2025–Present
Standardized motion and MDC rules within the design system to ensure interaction consistency.
  • Defined animation presets and transition rules within the design system
  • Established MDC conventions for Cursor environment and component integration
  • Collaborated with design team to document interaction guidelines
React
Next.js
TypeScript
Emotion
Framer Motion
RIDI Corp.
Frontend Developer
May 2022–Jul 2025

Developed the frontend of RIDI’s multi-format content platform serving 2.13M monthly active users across eBooks, webtoons, and web novels.

RIDI Web Platform
May 2022–Jul 2025
Maintained and evolved the frontend for RIDI’s 2.13M MAU digital content marketplace.
  • Co-designed and implemented the server-driven UI system ‘RiGrid’ for layout flexibility and experimentation
  • Enabled UI changes without app releases, speeding content operations by over 50%
  • Established GraphQL-based BFF and async rendering components for cross-platform consistency
  • Integrated Sentry and Amplitude to monitor errors and browser-specific issues
Next.js
React
TypeScript
Emotion
Jest
PHP
Twig
TmaxEnterprise
Research Engineer
Feb 2020–Apr 2022

Currently TmaxBizAI

Formerly TmaxData

Migrated enterprise products from a jQuery-based stack to React and built interfaces for the company’s AutoML platform.

Legacy System React Migration
Jan 2021–Apr 2022
Ported enterprise platform products from a jQuery-based internal library to React.
  • Modularized legacy UI code and improved interface structure
  • Developed reusable components to replace bespoke legacy widgets
AutoML Platform
Feb 2020–Apr 2022
Built frontend and interaction patterns for the company’s AutoML platform.
  • Implemented a codeless studio for non-expert model builders
  • Developed explainable AI (XAI) visualizations and dashboards
  • Researched interfaces to bridge AutoML engines and model developers
React
TypeScript
Material-UI
Sass
Python
jQuery

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

A Survey of Visualization Techniques for Interpreting Deep Learning

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