About

João Hespanha is a Distinguished Professor in the Department of Electrical and Computer Engineering at UC Santa Barbara. His research interests include hybrid and switched systems, the modeling and control of communication networks, distributed control over communication networks (also known as networked control systems), the use of vision in feedback control, game theory, and stochastic modeling in biology. He is associated with the Networked Control Laboratory and is engaged in advancing the understanding and development of control systems, communication networks, and their applications in various fields.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Control engineering
• Sociology
• Political Science
• Electrical engineering
• Medicine
• Engineering ethics
• Management science
• Environmental health
• Engineering management
• Physics
• Mathematics
• Biomedical engineering
• Optics
• Pharmacology
• Virology
• Public relations
• Econometrics
• Anesthesia
• Telecommunications

Selected publications

• Seconds-Resolved Measurements of Vancomycin Transport from the Plasma to the Interstitial Fluid Highlight a Path Towards Real-Time Therapeutic Drug Monitoring

  Sensors · 2026-04-04

  articleOpen access

  Continuous, in vivo drug and biomarker measurements could transform healthcare, enabling both the high-precision personalization of drug dosing and the real-time monitoring of health status. A practical realization of this vision, however, requires an improved understanding of the relationship between concentrations measured in the easily accessible dermal interstitial fluid (ISF) that correlate with the plasma concentrations that guide clinical decision making. As a preliminary step towards this goal, here we have used electrochemical, aptamer-based (EAB) sensors to perform seconds-resolved vancomycin measurements in the plasma and subcutaneous ISF of live rats. Concentrations of the antibiotic in the ISF vary rather little between different subcutaneous sites and, after the very rapid initial distribution phase is complete, they are well correlated with the plasma concentrations (mean R2 = 0.88). Likewise, a simple, two-compartment, two-parameter model describes our six paired plasma and ISF drug time courses quantitatively. Together, these findings provide further evidence of the viability of the drug concentration measurements performed in the subcutaneous or dermal ISF as a less invasive approach to real-time drug monitoring in individual patients.

  PublisherDOI

• Scaling and Trade-offs in Multi-agent Autonomous Systems

  arXiv (Cornell University) · 2026-03-11

  preprintOpen access

  Designing autonomous drone swarms is hampered by a vast design space spanning platform, algorithmic, and numerical-strength choices. We perform large-scale agent-based simulations in three canonical scenarios: swarm-on-swarm battle, cooperative area search with attrition, and pursuit of scattering targets. We demonstrate that dimensional-analysis and data-scaling, established techniques in physical sciences, can be leveraged to collapse performance data onto scaling functions that are mathematically simple, yet counterintuitive and therefore difficult to predict a priori. These scaling laws reveal success-failure boundaries, including sharp break points. Additionally, we show how this technique can be used to quantify trade-offs between agent count and platform parameters such as velocity, sensing or weapon range, and attrition rate. Furthermore, we show the benefits of embedding an optimal path planning loop within this framework, which can qualitatively improve the scaling laws that govern the outcome. The methods we demonstrate are highly flexible and would enable rapid, budget-aware sizing and algorithm selection for large autonomous swarms.

  PublisherDOI

• Scaling and Trade-offs in Multi-agent Autonomous Systems

  arXiv (Cornell University) · 2026-03-11

  articleOpen access

  Designing autonomous drone swarms is hampered by a vast design space spanning platform, algorithmic, and numerical-strength choices. We perform large-scale agent-based simulations in three canonical scenarios: swarm-on-swarm battle, cooperative area search with attrition, and pursuit of scattering targets. We demonstrate that dimensional-analysis and data-scaling, established techniques in physical sciences, can be leveraged to collapse performance data onto scaling functions that are mathematically simple, yet counterintuitive and therefore difficult to predict a priori. These scaling laws reveal success-failure boundaries, including sharp break points. Additionally, we show how this technique can be used to quantify trade-offs between agent count and platform parameters such as velocity, sensing or weapon range, and attrition rate. Furthermore, we show the benefits of embedding an optimal path planning loop within this framework, which can qualitatively improve the scaling laws that govern the outcome. The methods we demonstrate are highly flexible and would enable rapid, budget-aware sizing and algorithm selection for large autonomous swarms.

  PublisherOA PDF

• Feedback control over plasma drug concentrations achieves rapid and accurate control over solid-tissue drug concentrations  

  Research Square · 2025-01-28 · 1 citations

  preprintOpen access
  PublisherOA PDFDOI

• Disturbance Attenuation for Linear Systems: Optimal Solutions and Nonlinear Control

  IEEE Transactions on Automatic Control · 2025-06-30

  article

  This study presents the optimal solutions to the state feedback finite horizon disturbance attenuation problem using a game theory approach. The proposed solution method is valid for linear dynamical systems and quadratic objective functions, under bounded disturbances. Two solution regions in the space of initial states define the optimal value of the controller. The first region, which contains the zero initial state, features the linear optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> controller, whereas the second region is characterized by a nonlinear optimal control, which converges to the linear quadratic regulator (LQR) for large initial states. The transition between the two regions provides a unified framework that spans from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> control to LQR control, depending on the relative sizes of the initial state and the bound on the disturbance. A novel solution algorithm is introduced, reducing the optimization of the disturbance attenuation problem to a linear algebra problem for the first region and a single, scalar nonlinear program for the second region. The performance of the algorithm is tested on a representative set of numerical examples. This paper enhances the versatility of disturbance attenuation state feedback controllers by introducing an efficient optimal solution strategy applicable to both zero and nonzero initial states. One consequence of these results is that optimal disturbance attenuation of even linear systems requires nonlinear control.

  PublisherDOI

• Methods of Estimating Plasma Pharmacokinetics From Minimally Invasive, High Temporal Resolution Measurements of Interstitial Fluid Drug Concentrations

  Clinical and Translational Science · 2025-07-01 · 2 citations

  articleOpen access

  In therapeutic drug monitoring, plasma drug concentrations are used to guide dosing decisions, significantly improving outcomes for many therapeutic interventions. Due to the cumbersome, laboratory-based approaches used to measure such drug concentrations, however, such monitoring is slow to return actionable information to the clinician and is performed far less frequently than would be optimal. In response, approaches are being developed by which in vivo drug concentrations can be monitored in real time and at high frequency in the subcutaneous or intradermal interstitial fluid-measurements that are safe, convenient, and minimally invasive. In the furtherance of this approach, here, we explore theoretically the ability to use such high-frequency sub- or intradermal measurements to estimate the corresponding plasma concentration-time courses, as the latter remain the basis of effectively all clinical decision making. Doing so, we find that, given various physiologically and technologically plausible assumptions, it is possible to accurately estimate plasma concentration-time courses from measurements of interstitial fluid taken at two nonredundant sites in the interstitial fluid. This ability to derive clinically important plasma pharmacokinetics using minimally invasive subcutaneous or intradermal sensor placements has the potential to significantly improve the precision and reach of therapeutic drug monitoring and, with that, the safety and efficacy of drug delivery.

  PublisherOA PDFDOI

• Data-Driven Robust Control Under Input–Output Stealthy Attacks

  IEEE Control Systems Letters · 2025-01-01

  articleOpen accessSenior author

  We consider the problem of robust control of an unknown but minimal linear time-invariant system under input and output disturbances and adversarial manipulation. An attacker can (i) corrupt sensor measurements (deception attacks) and (ii) perturb the control channel (actuation attacks). To address the lack of model knowledge, we adapt the Data-enabled Predictive Control (DeePC) framework, which constructs predictors directly from input–output data with bounded disturbance. We formulate a finite-horizon open-loop control problem as a two-player zero-sum game with asymmetric information: the defender selects control inputs based on measured data and only knows an upper bound on disturbances, whereas the attacker has access to the true disturbance realization and can remain stealthy by hiding within this uncertainty set. The main contributions are (i) sufficient conditions for the existence of a Nash equilibrium corresponding to saddle-point policies for this game, and (ii) an analysis of the defender’s security strategy against deception and actuation attacks. Simulation studies on finite-horizon control demonstrate the effectiveness of the proposed approach.

  PublisherDOI

• Sensitivity analysis for uncertain linear systems: a set-membership approach

  International Journal of Control · 2025-04-23 · 1 citations

  articleOpen access
  PublisherDOI

• Two-dimensional parallel tempering for constrained optimization

  Physical review. E · 2025-08-26 · 2 citations

  articleOpen access

  Sampling Boltzmann probability distributions plays a key role in machine learning and optimization, motivating the design of hardware accelerators such as Ising machines. While the Ising model can, in principle, encode arbitrary optimization problems, practical implementations are often hindered by soft constraints that either slow down mixing when too strong or fail to enforce feasibility when too weak. We introduce a two-dimensional extension of the powerful parallel tempering algorithm (PT) that addresses this challenge by adding a second dimension of replicas interpolating the penalty strengths. This scheme ensures constraint satisfaction in the final replicas, analogous to low-energy states at low temperature. The resulting two-dimensional parallel tempering algorithm (2D-PT) improves mixing in heavily constrained replicas and eliminates the need to explicitly tune the penalty strength. In a representative example of graph sparsification with copy constraints, 2D-PT achieves near-ideal mixing, with Kullback-Leibler divergence decaying as O(1/t). When applied to sparsified Wishart instances, 2D-PT yields orders-of-magnitude speedup over conventional PT with the same number of replicas. The method applies broadly to constrained Ising problems and can be deployed on existing Ising machines.

  PublisherOA PDFDOI

• Zero-sum turn games using Q-learning: finite computation with security guarantees

  ArXiv.org · 2025-09-16

  preprintOpen accessSenior author

  This paper addresses zero-sum ``turn'' games, in which only one player can make decisions at each state. We show that pure saddle-point state-feedback policies for turn games can be constructed from dynamic programming fixed-point equations for a single value function or Q-function. These fixed-points can be constructed using a suitable form of Q-learning. For discounted costs, convergence of this form of Q-learning can be established using classical techniques. For undiscounted costs, we provide a convergence result that applies to finite-time deterministic games, which we use to illustrate our results. For complex games, the Q-learning iteration must be terminated before exploring the full-state, which can lead to policies that cannot guarantee the security levels implied by the final Q-function. To mitigate this, we propose an ``opponent-informed'' exploration policy for selecting the Q-learning samples. This form of exploration can guarantee that the final Q-function provides security levels that hold, at least, against a given set of policies. A numerical demonstration for a multi-agent game, Atlatl, indicates the effectiveness of these methods.

  PublisherOA PDFDOI

Recent grants

• COVID 19: RAPID: Informed Decision Making for Pandemic Management

  NSF · $146k · 2020–2022

• Collaborative Research: A Hybrid Systems Framework for Scalable Analysis and Design of Communication Networks

  NSF · $51k · 2005–2007

• EPCN - Online Optimization for the Control of Small Autonomous Vehicles

  NSF · $360k · 2016–2021

• PCAN -- Modeling and Analysis of Biological Systems Using Stochastic Hybrid Systems

  NSF · $300k · 2007–2012

• Collaborative Research: A Hybrid Systems Framework for Scalable Analysis and Design of Communications Networks

  NSF · $180k · 2003–2007

Frequent coauthors

• Thomas Parisini

  527 shared

• B. Pasik-Duncan

  486 shared

• Francesco Bullo

  Dynamic Systems (United States)

  478 shared

• Robert R. Bitmead

  University of California, San Diego

  477 shared

• Jorge Cortés

  University of California, San Diego

  476 shared

• Andrew G. Alleyne

  University of Minnesota

  472 shared

• Luca Zaccarian

  467 shared

• M.L. Corradini

  Università di Camerino

  466 shared

Education

• PhD, Engineering and Applied Sciences

  Yale University

  1998

• Licenciatura, Electrical Engineering

  Instituto Superior Técnico

  1991