Human-System Interaction
My starting point is HCI and Visual Analytics: turning complex information, tools, and models into interfaces people can understand, inspect, and use.
<Kh.Kim / >
Summary
Frontend Engineer with 7 years of experience.
My work started in HCI and Visual Analytics, where I studied how people understand and work with complex systems. That interest has continued through AutoML and explainable AI (XAI), data visualization, content platforms, server-driven UI, and internal operations automation.
Recently, I have been interested in using AI to help people make better decisions with less effort. Through GitHub-centered collaboration, browser automation, and executable prototypes, I am exploring ways to make scattered information and evidence easier to work with.
https://kihwan.kimjuljin1875@gmail.comLinkedInGitHubThemes
Understandable AI Systems
In AutoML and explainable AI (XAI), I built product UI that helps non-expert users inspect predictions, evidence, and edge cases before they act.
Product Experimentation Infrastructure
I used server-driven UI and gradual migration to decouple operational UI changes and A/B tests from deployment cycles.
AI-Native Development Workflow
I am exploring how AI agents can turn ideas into executable prototypes that teams can review as product-like interfaces.
Collaboration and Operations Tooling
Through GitHub-centered workflows, internal-tool integration, and browser automation, I reduce the cost of moving between people, tools, and information.
Experience
Levvels
Frontend Engineer
Jul 2025–Present
Exploring AI-native product development and internal operations automation while building creator-commerce frontend systems. I use GitHub-centered collaboration, executable prototypes, and browser automation to reduce the cost of product changes and repeated investigation work.
React
Next.js
TypeScript
Browser Agent
Generative UI
Amplitude
ChannelTalk
PageAgent-based Internal Operations Automation
Mar 2026–Present
Turned repeated CS/QA investigation work into an automation target, connecting natural-language requests to browser actions and verifiable result UI.
- Connected account, payment, and mapping data from several internal tools so operators could compare the necessary context in one result view
- Extended Alibaba PageAgent for a Chrome Managed Profile environment to automate tab navigation, clicks, and inputs
- Separated LLM output from reproducible action sequences and result presentation
- Simplified repeated CS/QA investigation work that required manually checking multiple tables and services
Vuddy Commerce Frontend
Jul 2025–Present
Improved commerce flows such as product discovery, cart, and authentication while using analytics and design-system patterns to lower frontend change cost.
- Tracked and improved operational issues in cart and authentication flows
- Reflected user-flow and operations context from Amplitude and ChannelTalk into frontend improvements
- Organized reusable frontend patterns through the design system to reduce repeated implementation work
React
Next.js
TypeScript
Browser Agent
Generative UI
Amplitude
ChannelTalk
RIDI Corp.
Frontend Engineer
May 2022–Jul 2025
Focused on reducing the dependency between product experimentation, operational UI changes, and deployment cycles in a large content platform. Through server-driven UI and gradual migration, I helped create structures that let operators adjust and test experiences faster.
Next.js
React
TypeScript
Emotion
Jest
PHP
Twig
Sentry
Virtualization
RIDI Web Platform
May 2022–Jul 2025
Used server-declared UI and island architecture to gradually move legacy pages into React while lowering the execution cost of operational UI changes and A/B tests.
- Rendered server-declared UI on the client to decouple screen changes from deployment cycles
- Migrated PHP pages toward React incrementally to reduce migration risk
- Created a more flexible foundation for A/B tests and operational UI changes
- Maintained stability across mobile, desktop, and special-device environments
Large-scale List Rendering Optimization
May 2022–Jul 2025
Improved list rendering structure and loading experience so large-scale content exploration would not limit user experience or product experimentation.
- Analyzed frame drops from large DOM trees
- Rendered only the visible range with windowing
- Handled scroll position and dynamic heights
- Improved skeleton UI for perceived speed
User Environment Stabilization
May 2022–Jul 2025
Reduced instability caused by browser, device, and extension differences, improving both product experience and operational response.
- Tracked real-user errors with Sentry
- Handled translation-plugin conflicts
- Improved E-ink rendering and UX issues
Next.js
React
TypeScript
Emotion
Jest
PHP
Twig
Sentry
Virtualization
TmaxEnterprise
Research Engineer
Feb 2020–May 2022
Currently TmaxBizAI
Formerly TmaxData
Designed product flows, visualizations, and interactions that helped non-expert users work with complex AI and analytics systems. The focus was not technology first, but the usability, explainability, and workflow of AI systems.
React
TypeScript
Material-UI
Sass
Python
jQuery
Legacy System React Migration
Jan 2021–May 2022
Moved a tightly coupled internal UI library toward React to create a more consistent and extensible foundation for platform interfaces.
- Modularized legacy UI code and improved interface structure
- Developed reusable components to replace bespoke legacy widgets
AutoML Platform
Feb 2020–May 2022
Designed and implemented codeless studio flows and explainable AI visualizations so users without model-development experience could explore and operate AI capabilities.
- Implemented a codeless studio for non-expert model builders
- Developed explainable AI visualizations and dashboards
- Researched interfaces between AutoML engines and model developers
- Clarified requirements for an early-stage platform
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-driven UI and Recommender System Research
2019.01–2020.02
Studied how recommender systems should communicate algorithmic exploration so users can understand the system and provide higher-quality feedback.
- Measured the impact of transparency on data quality in the explore-exploit problem of recommendation systems
- Analyzed how disclosure patterns for exploratory items affect user perception and data quality
- Proposed a metric to evaluate the value of user logs from an AI perspective
- Implemented a web-based movie recommendation system for experimental environments
- Designed data-driven UI experiments to observe interaction between people and recommendation algorithms
Python
Flask
Surprise
jQuery
Modeling Web User Behavior
2019.02–2020.02
Modeled decision-making and behavior patterns in complex web environments to better understand interaction between people and systems.
- 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