Assistant Professor of ECE at CMU

Guannan Qu
Assistant Professor -> Associate Professor (starting from July 1, 2026)

Department of Electrical and Computer Engineering
Carnegie Mellon University
Contact: gqu [at] andrew.cmu.edu
Office: Porter B22

I am an assistant professor at the Department of Electrical and Computer Engineering at Carnegie Mellon University. From 2019 to 2021, I was a CMI and Resnick postdoc in the CMS Department of California Institute of Technology, working with Prof. Steven Low and Prof. Adam Wierman. I obtained my Ph.D. degree from Harvard SEAS working with Prof. Na Li in 2019. I obtained my B.S. degree from Tsinghua University in Beijing, China in 2014.

I am broadly interested in machine learning and decision making/control. My recent interest focuses on developing fundamental principles of machine learning and GenAI to make them interpretable, safe, and scalable. Coming from a traditional control background, I have also been interested in developing theories that make machine learning applicable in control of real-world large scale engineering systems. My research is interdisciplinary in nature that develops new mathematical tools in machine/reinforcement learning, control theory, optimization, network science.

My CV can be found here (updated in Feb 2026).

Recent updates

Past updates (2024 and older)

  • Student Alex DeWeese received Leo Finzi Memorial Fellowship in Electrical & Computer Engineering (2024)! Congrats!
  • Our paper has been selected as the 3rd paper out of the top 5 papers chosen among over a thousand articles published in the IEEE Transactions on Smart Grid (TSG) in the past 3 years.
  • I received NSF CAREER Award (2023)!
  • Paper highlights (2023):
    • We proposed CoVariance Optimal MPC (CoVO-MPC), which exploits the dynamics structure to improve the efficiency of sampling based MPC. We showed significant improvements both in theory and in simulations/real-world experiments!
    • We applied the Scalable Reinforcement Learning framework (which we proposed a few years ago in this paper) to a microgrid inverter control problem and showed superior scalability of the proposed framework! See preprint here.
    • We proposed a distributed networked MPC framework with provable dynamic regret guarantee for networked control problems! See preprint here.
    • We proposed an ISS-Lyapunov based neural certificate framework to stabilize networked dynamical systems! Accepted to L4DC 2023 as oral presentation. See paper here.
  • Three new members joined my group (Fall 2023). Welcome, Zeji, Chaoyi, and Muhammed!
  • We received CMU CyLab seed funding (Spring 2023)!
  • Our paper ``Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning’’ (link) has been accepted to SIGMETRICS 2023!
  • Our paper ``Near-optimal distributed linear-quadratic regulator for networked systems (link)’’ has been accepted to SIAM Journal on Control and Optimization.
  • Two new members joined my group (Fall 2022). Welcome, Alex and Ziyi!
  • Two new papers accepted to NeurIPS 2022: On the sample complexity of stabilizing LTI systems on a single trajectory (link), Bounded-regret MPC via perturbation analysis: prediction error, constraints, and nonlinearity (link)
  • One new paper in ICML 2022: Decentralized Online Convex Optimization in Networked Systems (link)
  • We received a new research grant from NSF EPCN (Spring 2022)!
  • We received a new research award from C3 AI Institute (Spring 2022)!
  • Our paper on scalable multi-agent RL for networked systems (link) has been accepted to Operations Research!
  • I am starting at CMU as an assistant professor (Fall 2021)!

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