Associate Professor of ECE at CMU

Guannan Qu
Associate Professor

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

I am an associate professor at the Department of Electrical and Computer Engineering at Carnegie Mellon University.

I am broadly interested in machine learning, decision making, and control. My recent interest focuses on developing fundamental principles and scientific understandings of GenAI to make them interpretable, safe, and scalable. For a taste of this line of work, see here. I have also been interested in the interplay between learning and control, developing theories that make machine learning applicable in control of real-world large scale engineering systems. See here for example projects.

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

Recent updates

Jun 2026Paper on transformers for networked control with long-range interactions has been selected for Best Student Paper Award for ECC 2026! Congratulations to our student authors, Vidur and Muhammed!
Jun 2026Invited tutorial and Gen AI workshop talks at SIGMETRICS 2026.
May 2026Released new preprint on Effective Theory of LLMs; see the project website for details and demos.
May 2026New preprints on Multi-Step Policy Gradient and Emergent and Subliminal Misalignment.
Apr 2026New ICML paper on emergent misalignment.
Apr 2026Received a new grant from MFI.
Feb 2026Promoted to Associate Professor of ECE (effective July 1, 2026).
Jan 2026New ICLR 2026 papers on internal planning in LLMs, generative control, and Riemannian diffusion.
Dec 2025Gave a tutorial at CDC on sampling-based control.
Dec 2025New NeurIPS 2025 papers on mean-field MARL (Spotlight) and stabilizing linear systems.
Aug 2025Received a new NSF grant on sampling-based control.
Aug 2025Received a new PITA grant.
Jul 2025New ICML 2025 paper on theoretical study of (hyper) self-attention.
May 2025Dial-MPC selected as a Best Paper Finalist at ICRA 2025.
Feb 2025Best Paper Award at the AAAI 2025 Workshop on Multi-Agent AI in the Real World.
Feb 2025Group members received multiple fellowships: Zeji Yi (CMU Wei Shen and Xuehong Zhang Presidential Fellowship), Alex DeWeese (David H. Barakat and LaVerne Owen-Barakat College of Engineering Dean's Fellowship), and Chaoyi Pan (Hsu Chang Memorial Fellowship in Electrical & Computer Engineering).
Jan 2025Co-organized the NSF Workshop on Reinforcement Learning.
Past updates (2024 and earlier)
2024Student Alex DeWeese received the Leo Finzi Memorial Fellowship in Electrical & Computer Engineering.
2023Our paper was selected among the top 5 papers (ranked 3rd) out of more than 1,000 articles published in IEEE Transactions on Smart Grid in the prior three years.
2023Received the NSF CAREER Award.
2023 Paper highlights:
  • Proposed CoVariance Optimal MPC (CoVO-MPC), exploiting dynamics structure to improve sampling-based MPC efficiency with strong theoretical and empirical results.
  • Applied our scalable RL framework from this paper to microgrid inverter control, demonstrating superior scalability; preprint here.
  • Proposed a distributed networked MPC framework with provable dynamic regret guarantees; preprint here.
  • Proposed an ISS-Lyapunov neural certificate framework to stabilize networked dynamical systems; accepted to L4DC 2023 (oral). See paper.
2023Three new members joined the group (Fall 2023): Zeji, Chaoyi, and Muhammed.
2023Received CMU CyLab seed funding (Spring 2023).
2023Paper "Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning" (link) accepted to SIGMETRICS 2023.
2023Paper "Near-optimal distributed linear-quadratic regulator for networked systems" (link) accepted to SIAM Journal on Control and Optimization.
2022Two new members joined the group (Fall 2022): Alex and Ziyi.
2022Two papers accepted to NeurIPS 2022: On the sample complexity of stabilizing LTI systems on a single trajectory and Bounded-regret MPC via perturbation analysis: prediction error, constraints, and nonlinearity.
2022One paper in ICML 2022: Decentralized Online Convex Optimization in Networked Systems.
2022Received a new research grant from NSF EPCN (Spring 2022).
2022Received a new research award from C3 AI Institute (Spring 2022).
2021Paper on scalable multi-agent RL for networked systems (link) accepted to Operations Research.
2021Started at CMU as an assistant professor (Fall 2021).

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