Kevin Jamieson

Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington. His academic journey includes a B.S. (2009), M.S. (2010), and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017.

• Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015)
• M.S., Electrical Engineering, Columbia University (2010)
• B.S., Electrical Engineering, University of Washington (2009)

Jamieson's research lies at the intersection of interactive machine learning, active learning, and sequential decision making. His work focuses on:

• Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL)
• Developing instance-dependent optimal algorithms that adapt to problem difficulty
• Applications in robotics, human perception studies, and hyperparameter optimization
• Representation learning for large models and experimental design frameworks

His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory, contextual RL, and game-theoretic learning. Notable trends include sample-efficient optimization, adaptive A/B testing, and sim-to-real transfer in robotics.

Jamieson has received:

• NSF CAREER award for foundational contributions
• Amazon Faculty Research award for innovation in learning systems

He actively recruits graduate students and postdocs, emphasizing collaboration in areas like:

• Multi-agent RL and strategic actor learning
• Empirical process suprema and adaptive sampling theory
• Applications in robotics, large language model finetuning, and biomedical data analysis

Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data.