Geoffrey J. Gordon

Geoffrey J. Gordon is a Professor in the Machine Learning Department at Carnegie Mellon University and affiliated with the Robotics Institute. His research spans multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of complex data, computational learning theory, and game theory.

He leads the SELECT lab (SEnse, LEarn, and aCT), focusing on predictive state representations, spectral learning, and applications in robotics. His recent work integrates deep learning with controlled dynamical systems and optimization, as seen in publications at AAAI and AISTATS.

• Research Interests:
  • Multi-agent systems and game theory
  • Reinforcement learning and dynamical systems
  • Statistical models for high-dimensional data
  • Spectral learning and quantum Markov models
• Scientific Awards:
  • Best paper award at ICML 2010
• Teaching:
  • 10-405/605: Machine Learning with Large Datasets (2023)
  • 10-606/607: Mathematical/Computational Background for ML (2022, 2017)
  • 10-701: Intro to Machine Learning (2021, 2014)
• Labs & Teams:
  • SELECT Lab (SEnse, LEarn, and aCT)
  • Collaborations with Stanford Robotics Lab, AUTON Lab, and others