Minsuk Kahng
Assistant Professor at Yonsei University
Seoul, South Korea
Summary
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Rockstar🎓
Top SchoolMinsuk Kahng is an Assistant Professor at Yonsei University who designs interactive visualization tools that make large AI models and datasets interpretable, blending data visualization with Responsible AI. With 12 years of experience spanning academic and industry labs, he previously led research at Google DeepMind PAIR and held faculty positions at Oregon State, and his work has been deployed at scale (e.g., LLM Comparator for Google, ActiVis for Facebook). He holds a PhD from Georgia Tech with a distinguished dissertation and has secured support from NSF and Google. A hands-on developer as well as a researcher, his open-source contributions include front-end work on influential projects like GAN Lab and CSrankings, reflecting a knack for turning visualization research into polished interactive tools. Based in Seoul, he combines rigorous empirical methods with production-minded design to make complex ML systems more transparent and usable.
12 years of coding experience
13 years of employment as a software developer
Master of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at Seoul National UniversityDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Georgia Institute of Technology
Stackoverflow
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Github Skills (15)
html10
polymer10
javascript10
web-development10
front-end-development10
webapp10
web-application10
vega-lite10
typescript-types9
typescript9
typescripts9
css9
computer-science6
tensorflowjs5
generative-adversarial-network4
Programming languages (6)
Github contributions (5)
poloclub/ganlab
Mar 2018 - Sep 2020
GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks
Role in this project:
Front-end Developer
Front-end DeveloperContributions:1 release, 94 commits, 14 PRs in 2 years 6 months
Contributions summary:Minsuk primarily focused on developing the user interface for the GAN Lab tool. Their contributions involved importing various web components, including Polymer and Material Design Lite, and modifying the HTML structure. They also implemented the layout, added UI elements like the top bar with descriptions and controls, and integrated the ability to load and display the distribution data for a GAN.
pytorchexperimentationdeep-learningadversarialgenerative-adversarial-network
emeryberger/CSrankings
Nov 2019 - Nov 2021
A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:19 commits, 8 PRs, 5 comments in 2 years
Contributions summary:Minsuk primarily contributed to the frontend of the csrankings web application. They refactored the chart implementations, replacing the older d3pie charts with vega-lite bar charts. They also integrated the vega-embed library to render the charts. Additionally, the user made minor improvements to the HTML, JavaScript and TypeScript files, including fixing the appearance of chart icons and updating the URL parsing.
dblpreactscienceresearch-outputselective
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