About

Charles Cao is an Assistant Professor in the Department of Physics at Virginia Tech, located in the Center for Quantum Information Science and Engineering. His research focuses on Theoretical Condensed Matter Physics and String Theory. He holds a Ph.D. from the California Institute of Technology. His work involves exploring fundamental aspects of condensed matter systems and string theoretical frameworks, contributing to the understanding of complex physical phenomena. He is based at the Virginia Tech campus in Blacksburg, VA, and can be contacted via email at cjcao@vt.edu.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Control engineering
• Mathematical optimization

Selected publications

• Mitigating error propagation in multi-hop fact verification with logic reasoning

  International Journal of Machine Learning and Cybernetics · 2025-05-26

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  PublisherDOI

• Bootstrapping LLM-based Fact-checking via Iterative Rationalization Finetuning

  2025-03-12 · 2 citations

  article

  Fact-checking, the task of reasoning about a claim’s truthfulness based on evidence, has become increasingly crucial with the rapid spread of misinformation. In real-world scenarios, fact-checking often involves checking complex claims necessitating multi-step reasoning, thus imposing a high requirement for a model’s autonomous ability. LLM-based fact-checking performs multi-step reasoning through generating natural language rationales. However, it is susceptible to error propagation problems, which means any error occurring inside rationales will result in an incorrect label. To this end, we propose an iterative rationalization finetuning approach to address this issue, enhancing the LLM’s ability by finetuning it with high-quality rationales. Specifically, we guide the generation of high-quality rationales using golden labels and, inversely, utilize these to finetune the LLM itself, thus constructing a self-improvement cycle. We demonstrate the effectiveness of the proposed method on HoVer and FEVEROUS-S benchmarks, where it achieves state-of-the-art performance, particularly in multi-step reasoning scenarios.

  PublisherDOI

• Verifying ambiguous claims with a reasoner-translator framework

  Neurocomputing · 2025-05-31

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  PublisherDOI

• 2D temperature field reconstruction using optimized Gaussian radial basis function networks

  Measurement · 2024-06-22 · 5 citations

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  PublisherDOI

• Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties

  Journal of Guidance Control and Dynamics · 2024-06-27 · 4 citations

  articleSenior author

  In control of aerospace systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions. This paper presents a robust adaptive control architecture for linear parameter-varying (LPV) systems that allows for the desired dynamics to be systematically scheduled, while being able to handle a broad class of uncertainties, both matched and unmatched, which can depend on both time and states. The proposed controller adopts an [Formula: see text] adaptive control architecture for designing the adaptive control law and peak-to-peak gain (PPG) minimization for designing the robust control law to mitigate the effect of unmatched uncertainties. Leveraging the PPG bound of a LPV system, we derive transient and steady-state performance bounds in terms of the input and output signals of the actual closed-loop system with respect to a nominal system. The proposed control architecture is applied to control the longitudinal motion of an F-16 aircraft operating within a large envelope. Simulation results using both LPV and fully nonlinear models validate the efficacy of the proposed method.

  PublisherDOI

• On novel trajectory tracking control of quadrotor UAV: A finite-time guaranteed performance approach

  Journal of the Franklin Institute · 2022-09-16 · 18 citations

  article1st author
  PublisherDOI

• ESO-based Trajectory Tracking Control for Quadrotor UAV with Prescribed Performance

  2020-07-01 · 4 citations

  article1st authorCorresponding

  The trajectory tracking control of the quadrotor unmanned aerial vehicle (QUAV) is investigated. The aim of this paper is to develop a novel trajectory tracking control method considering model uncertainties and external disturbances with finite-time stability and prescribed performance of the attitude. Firstly, the trajectory tracking control system of the QUAV is decoupled into two subsystems, i.e., position and attitude subsystems, wherein, backstepping technique is used to design the position tracking controller. Then, to guarantee the tracking performance of the attitude subsystem, a finite-time convergent performance function is developed, and a prescribed performance attitude controller is devised via using integral barrier Lyapunov function thereafter. In the design process of the above two controllers, ESO is used to estimate and compensate multiple uncertainties and disturbances simultaneously. Moreover, the fractional state feedback and discontinuous phenomenon of the control law are directly avoided to achieve the finite-time convergence rate. Finally, an illustrative example is organized to verify the effectiveness of the proposed approach.

  PublisherDOI

• Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties

  arXiv (Cornell University) · 2020-10-09 · 5 citations

  preprintOpen accessSenior author

  In controlling systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions. This paper presents a robust adaptive control architecture for linear parameter-varying (LPV) systems that allows for the desired dynamics to be systematically scheduled, while being able to handle a broad class of uncertainties, both matched and unmatched, which can depend on both time and states. The proposed controller adopts an L1 adaptive control architecture for designing the adaptive control law and peak-to-peak gain (PPG) minimization for designing the robust control law to mitigate the effect of unmatched uncertainties. Leveraging the PPG bound of an LPV system, we derive transient and steady-state performance bounds in terms of the input and output signals of the actual closed-loop system as compared to the same signals of a nominal system. The efficacy of the proposed method is validated by extensive simulations using the short-period dynamics of an F-16 aircraft operating in a large envelope.

  PublisherOA PDFDOI

• ℒ₁-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

  2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2020 · 46 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> adaptive controller robustifies the architecture, allowing the overall system to behave similar to the nominal system simulated with MPPI. The architecture is validated in a simulated multirotor racing environment.

  PublisherDOI

• Energy Efficient Resource Allocation for Time Switching Wireless Powered NOMA Networks

  2020-12-11 · 2 citations

  article1st authorCorresponding

  Non-orthogonal multiple access (NOMA) has attracted much attention from not only industry but also academia and has the potential to meet the demand of increasing data rate. In this paper, the problem of joint resource allocation and time switching (TS) control in wireless-powered NOMA internet of things (IoT) network is considered. We aim to maximize the sum of user-centric energy efficiency (EE) in the system. The optimization problem is divided into subcarrier allocation, power control and TS control. Firstly, the subcarrier allocation problem is a mixed-integer non-linear programming problem which is non-convex. Thus two-sided matching algorithm is proposed to optimize subcarrier allocation with low complexity. Then because the sum of user-centric EE is the sum of fractional programming problem, successive convex approximation (SCA) is applied to solve the problem. Finally, the simulation results show convergence of the proposed optimization scheme and we achieve better performance than orthogonal frequency division multiple access (OFDMA) scheme.

  PublisherDOI

Recent grants

• NRI-Small: Cooperative Underwater Robotic Networks for Discovery & Rescue

  NSF · $568k · 2012–2015

Frequent coauthors

• Naira Hovakimyan

  119 shared

• Enric Xargay

  University of Michigan–Ann Arbor

  28 shared

• Isaac Kaminer

  Naval Postgraduate School

  24 shared

• Vladimir Dobrokhodov

  Naval Postgraduate School

  24 shared

• Irene M. Gregory

  Langley Research Center

  24 shared

• Eugene Lavretsky

  23 shared

• Vijay Patel

  Aeronautical Development Agency

  14 shared

• Jie Luo

  Chinese Academy of Sciences

  13 shared

Labs

• Center for Quantum Information Science and EngineeringPI