Bum Chul Kwon

Bum Chul Kwon

Research Scientist

IBM Research

Hi, I’m BC, a research scientist at IBM Research passionate about making AI and data work for people. I build and evaluate AI models, visual analytics systems, and agentic workflows, applying them to unlock deeper insights, address healthcare and life sciences challenges, tell compelling data-driven stories, and advance data visualization literacy.

Experience

  • Research Scientist — IBM Research, Cambridge, MA 2016–present
  • Adjunct Professor — Columbia University · UPenn · UC Berkeley 2017–present
  • Postdoctoral Researcher — Universität Konstanz, Germany 2013–2015

Education

  • PhD, Industrial Engineering — Purdue University, West Lafayette, IN 2008–2013
  • MS, Industrial Engineering — Purdue University, West Lafayette, IN 2008–2010
  • BS, Systems Engineering — University of Virginia, Charlottesville, VA 2004–2008
  • Civil Engineering — Seoul National University, Seoul, Republic of Korea 2002–2004

Research Keywords

Data Visualization Visual Analytics Human-AI Interaction Agentic Workflows AI for Biology, Healthcare, Medicine, and Science
Publications
(2023). Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models. The 61st Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations.
(2023). Causalvis: Visualizations for Causal Inference. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).
(2020). Geono-Cluster: Interactive Visual Cluster Analysis for Biologists. IEEE Transactions on Visualization and Computer Graphics (TVCG).
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(2019). Thumbnails for Data Stories: A Survey of Current Practices. Proceedings of the IEEE Conference on Visualization (IEEE VIS) Short Papers.
(2016). Off-Screen Visualization Perspectives: Tasks and Challenges. Symposium on Visualization in Data Science (VDS) at IEEE VIS.
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(2016). ReVACNN: Real-Time Visual Analytics for Convolutional Neural Network. Future of Interactive Learning Machines Workshop at Neural Information Processing Systems (NIPS).
(2016). Towards a Taxonomy for Evaluating User Engagement in Information Visualization. Personal Visualization: Exploring Data in Everyday Life at IEEE VIS.
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(2016). Peekquence: Visual Analytics for Event Sequence Data. KDD Workshop on Interactive Data Exploration and Analytics (IDEA).
(2014). Knowledge Generation Model for Visual Analytics. IEEE Transactions on Visualization and Computer Graphics (TVCG).
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(2011). Investigating the Efficacy of Crowdsourcing on Evaluating Visual Decision Supporting System. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
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(2011). Direct Manipulation Through Surrogate Objects. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI).
(2011). Visual Analytic Roadblocks for Novice Investigators. Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST).
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