Update: I recently left Google DeepMind and started a faculty job in South Korea in 2025.
Hi, I'm an Assistant Professor at Yonsei University in the Department of Computer Science and Engineering, where I co-lead the Human-Data Interaction Lab.
My research centers on addressing key challenges in Responsible AI, such as model failure, bias, and safety. To support this, I develop interactive data visualization tools that help practitioners explore data and understand model behavior. For example, one such tool, LLM Comparator, has been extensively used at Google for model evaluation and was featured at Google I/O as part of the Gemini models and the Responsible AI Toolkit.
Before joining Yonsei, I was a Senior Research Scientist at Google DeepMind in the People + AI Research (PAIR) team and an Assistant Professor at Oregon State University. I received my Ph.D. from Georgia Tech, advised by Polo Chau, along with a Dissertation Award.
Areas of expertise: Visual Analytics, Data Visualization, Responsible AI, Explainable AI, Human-Computer Interaction
Research Interests
AI systems are inherently imperfect. They fail unexpectedly, reflect biases, or raise safety concerns. My research group aims to help people uncover and address these issues by designing interactive web-based tools, drawing on methods from data visualization and responsible AI. We're especially interested in (but not limited to):
- Error Analysis and Evaluation of LLM Outputs: Identifying categories of prompts where models fail and analyzing why.
- Red Teaming and Safety: Generating adversarial prompts and assessing bias and safety risks.
- Bias Analysis and Dataset Curation: Iteratively curating datasets to ensure high-quality and diverse data for AI.
- Debugging AI Agents: Visualizing internal reasoning to explain unexpected agent behavior.
- Video Exploration and Analytics: Building tools for large-scale video search and analysis.
Note for Prospective Students
We're actively looking for graduate students and undergraduate interns (e.g., including opportunities for Summer 2025). If you're interested, please email me your CV, transcript, a short (1-page) statement explaining why you'd like to work with us and what relevant experience you have. If there's a potential fit, I'll get back to you within in a week regarding next steps.
Selected Publications (Latest & Greatest)
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LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models
VIS 2024
DOI (VIS) PDF (VIS) Blog Code
Deployed on Google's LLM Evaluation Platforms
Featured at Google I/O on Gemini Open Models and Responsible AI Toolkit -
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
FAccT 2024
arXiv PDF Blog -
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
CHI 2024 (LBW track)
DOI arXiv PDF Code -
VLSlice: Interactive Vision-and-Language Slice Discovery
ICCV 2023
arXiv PDF Talk Website -
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models
VIS 2023 (Short)
PDF Code -
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps
VIS 2022
DOI arXiv PDF Twitter Post Demo -
FitVid: Responsive and Flexible Video Content Adaptation
CHI 2022
DOI PDF -
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
NeurIPS 2021
arXiv PDF -
Contrastive Identification of Covariate Shift in Image Data
VIS 2021 (Short)
DOI PDF -
"Why did my AI agent lose?": Visual Analytics for Scaling Up After-Action Review
VIS 2021 (Short)
DOI PDF -
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
VIS 2020
DOI PDF Demo Video -
How Does Visualization Help People Learn Deep Learning? Evaluation of GAN Lab with Observational Study and Log Analysis
VIS 2020 (Short)
PDF -
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
VIS 2019
DOI PDF Blog Code -
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
VIS 2018
Open sourced with Google
DOI PDF Slides Code Website -
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
IEEE Transactions on Visualization and Computer Graphics, 25(8), 2018.
Cited more than 700 times
DOI PDF Website Medium -
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
VIS 2017
Deployed on Facebook's ML Platform
DOI PDF Video Slides Website -
Interactive Browsing and Navigation in Relational Databases
VLDB 2016
DOI PDF Slides
Employment
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Yonsei University, Seoul, South Korea
2025 - present
Assistant Professor, Department of Computer Science and Engineering, College of Computing -
Google, Atlanta, GA
2022-2025
Senior Research Scientist, People+AI Research (PAIR) Team, Google DeepMind -
Oregon State University, Corvallis, OR
2019-2022
Assistant Professor of Computer Science, School of Electrical Engineering and Computer Science -
Google, Cambridge, MA
Summer 2017
Software Engineering Intern, People+AI Research (PAIR) Team, Google Brain -
Facebook, Menlo Park, CA
Summer 2016
Research Intern, Applied ML Research Group -
Facebook, Menlo Park, CA
Summer 2015
Research Intern, Applied ML Research Group
Education
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Ph.D. in Computer Science,
Georgia Institute of Technology, Atlanta, GA
2013-2019
Thesis: Human-Centered AI through Scalable Visual Data Analytics
Committee: Polo Chau (Advisor), Sham Navathe, Alex Endert, Martin Wattenberg, and Fernanda Viégas -
M.S. in Computer Science and Engineering,
Seoul National University, South Korea
2009-2011
Thesis: Context-Aware Recommendation using Learning-to-Rank (Advisor: Sang-goo Lee) - B.S. in Electrical and Computer Engineering, Seoul National University, South Korea 2005-2009
Awards
- College of Computing Dissertation Award, Georgia Tech 2021
- Finalist, Facebook Research Award 2021
- ACM Trans. Interactive Intelligent Systems (TiiS) 2018 Best Paper, Honorable Mention 2020
- Google PhD Fellowship, Google AI 2018-2019
- Graduate TA of the Year in School of Computer Science, Georgia Tech 2018
- NSF Graduate Research Fellowship, National Science Foundation 2014-2017
- Best Paper Award, PhD Workshop at CIKM 2011
- National Scholarship for Science and Engineering, Korea Student Aid Foundation 2005-2009
Teaching
- Yonsei University CSI 7110. Topics in Responsible AI, 2025
- CAS 4150. Introduction to Data Visualization, 2025
- Oregon State Univ. CS 499/549. Visual Analytics, 2022
- CS 565. Human-Computer Interaction, 2020-2022
- CS 539. Data Visualization for Machine Learning, 2020
Professional Service
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Conf. Organization
VIS 2024-25 (Publication Chairs)
IDEA@KDD 2018 (Workshop Organiziers)
WSDM 2016 (Web) -
Conf. PC
VIS (2020-present)
IUI (2019-present)
AAAI (2021-22)
SDM (2020)
WSDM (2022 Demo)
CIKM (2019 Demo) -
Journal Reviewers
IEEE Transactions on Visualization and Computer Graphics (TVCG)
ACM Transactions on Interactive Intelligent Systems (TiiS)
ACM Transactions on Computer-Human Interaction (TOCHI)
ACM Transactions on Intelligent Systems and Technology (TIST)
Distill - Conf. Reviewers CHI (2014, 17-18, 21-22, 24-25), UIST (2023), CSCW (2020), VIS (2018-20), EuroVis (2018, 25), KDD (2014-16), SDM (2014, 16-17), IUI (2016), RecSys (2016), SIGMOD (2013), DASFAA (2011)
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Grant Reviewers
NSF Review Panelists
(CISE III)
Google Academic Research Awards
Bio
Minsuk Kahng is an Assistant Professor in the Department of Computer Science and Engineering
at Yonsei University in South Korea.
His research aims to empower researchers and practitioners to gain insights through
interactive data visualization, enabling the responsible development of AI systems.
To achieve this, he builds novel visual analytics tools that help these people
interpret model behavior and explore large datasets.
Kahng publishes papers at the top venue in the field of data visualization (IEEE VIS),
as well as at premier conferences in the field of AI, Human-Computer Interaction, and Responsible Computing.
His research has led to deployed technologies (e.g., LLM Comparator for Google, ActiVis for Facebook)
and been recognized by prestigious awards,
including a Google PhD Fellowship and an NSF Graduate Research Fellowship,
and supported by NSF, DARPA, Google, and NAVER.
Before joining Yonsei, Minsuk was a Senior Research Scientist at Google DeepMind
in the People + AI Research (PAIR) team and an Assistant Professor at Oregon State University.
He received his Ph.D. from Georgia Tech with a Dissertation Award.
Website: https://minsuk.com
CV: https://minsuk.com/minsuk-kahng-cv.pdf