Assistant Professor of ECE at CMU

Guannan Qu
Assistant Professor
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 control, optimization, and machine learning. Particularly, I strive to develop theories that make machine learning applicable in 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 and applies these tools to cyber physical systems, power systems, transportation systems, robotics and beyond, with provable performance and resilience guarantee. For more details, please see the research page.

My CV can be found here (updated in Dec 2023).

Recent updates

  • 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)!
  • Recent paper highlights:
    • 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)!

Past updates (2022 and older)

  • 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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