Kwang-Sung Jun

Kwang-Sung Jun is an Assistant Professor at the University of Arizona, Department of Computer Science. His research spans interactive machine learning, reinforcement learning, and learning theory, with a focus on multi-armed bandits, Bayesian optimization, and generalized linear models.

Education: Ph.D. in Computer Science from the University of Wisconsin-Madison (2015).

Research Trends: Kwang-Sung's recent work (2023-2025) emphasizes bandit algorithms with second-order bounds, adaptive experimentation, and PAC-Bayes frameworks. He explores low-rank structures in regression, explainable reward shaping, and environmental risk modeling via probabilistic assessments of postfire debris-flows. His publications often bridge theoretical guarantees (e.g., regret bounds) with practical applications in machine learning and environmental hazards.

Expertise:

• Interactive machine learning
• Multi-armed bandits
• Confidence sequences
• Reinforcement learning
• Human-machine hybrid systems