• “Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behavior” by Minjae Lee and Minsuk Kahng. ACM CHI Conference on Human Factors in Computing Systems (CHI), 2026.
• “Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation” by Aeree Cho, Grace C. Kim, Alexander Karpekov, Seongmin Lee, Alec Helbling, Benjamin Hoover, Zijie J. Wang, Minsuk Kahng, and Duen Horng Chau. ACM CHI Conference on Human Factors in Computing Systems (CHI), 2026.
• “Who Defines “Best”? Towards Interactive, User-Defined Evaluation of LLM Leaderboards” by Minji Jung, Minjae Lee, Yejin Kim, Sarang Choi, and Minsuk Kahng, ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2026.
•  “COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs” by Dasol Choi, DongGeon Lee, Brigitta Jesica Kartono, Helena Berndt, Taeyoun Kwon, Joonwon Jang, Haon Park, Hwanjo Yu, and Minsuk Kahng. Annual Meeting of the Association for Computational Linguistics (ACL), 2026.
•  “LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models” by Minsuk Kahng, Ian Tenney, Mahima Pushkarna, Michael Xieyang Liu, James Wexler, Emily Reif, Krystal Kallarackal, Minsuk Chang, Michael Terry, and Lucas Dixon. IEEE Transactions on Visualization and Computer Graphics, 31(1) (VIS), 2025.
•  “Gemma 2: Improving Open Language Models at a Practical Size” by Google DeepMind Gemma Team. arXiv preprint, 2024.
•  “Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation” by Jessica Quaye, Alicia Parrish, Oana Inel, Charvi Rastogi, Hannah Rose Kirk, Minsuk Kahng, Erin van Liemt, Max Bartolo, Jess Tsang, Justin White, Nathan Clement, Rafael Mosquera, Juan Ciro, Vijay Janapa Reddi, and Lora Aroyo. ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2024.
• “DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps” by Donald Bertucci, Md Montaser Hamid, Yashwanthi Anand, Anita Ruangrotsakun, Delyar Tabatabai, Melissa Perez, and Minsuk Kahng. IEEE Transactions on Visualization and Computer Graphics, 29(1) (VIS), 2023.
•  “VLSlice: Interactive Vision-and-Language Slice Discovery” by Eric Slyman, Minsuk Kahng, and Stefan Lee. International Conference on Computer Vision (ICCV), 2023.
• “One Explanation is Not Enough: Structured Attention Graphs for Image Classification” by Vivswan Shitole, Li Fuxin, Minsuk Kahng, Prasad Tadepalli, and Alan Fern. Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.
• “FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning” by Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, and Duen Horng Chau. IEEE Conference on Visual Analytics Science and Technology (VIS), 2019.
• “ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models” by Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro, and Duen Horng Chau. IEEE Transactions on Visualization and Computer Graphics, 24(1) (VIS), 2018.
• “Interactive Browsing and Navigation in Relational Databases” by Minsuk Kahng, Shamkant B. Navathe, John T. Stasko, and Duen Horng Chau. Proceedings of the VLDB Endowment, 9(12) (VLDB), 2016.

   You can find the full list of publications on the website (https://minsuk.com).