Joao Hespanha

How to say my name?
Joe-wow Hesh-pahn-ya


Distinguished Professor
Electrical & Computer Engineering Dept. (ECE)
Mechanical Engineering Dept. (ME)
Center for Control, Dynamical-systems, and Computation (CCDC)

University of California, Santa Barbara (UCSB)

Email: hespanha @
Tel: +1 (805) 893-7042
Fax: +1 (805) 893-3262

Postal address:
Room 5157, Harold Frank Hall
Dept. of Electrical & Computer Eng.
University of California
Santa Barbara, CA 93106-9560 USA

quick links


  • The U.S. National Science Foundation (NSF) has announced a $140 million investment to establish seven new National Artificial Intelligence Research Institutes (AI Institutes). Among these, UCSB's AI Institute for Agent-based Cyber Threat Intelligence and Operation (ACTION) seeks to change the way mission-critical systems are protected against sophisticated, ever-changing security threats. In cooperation with (and learning from) security operations experts, intelligent agents will use complex knowledge representation, logic reasoning, and learning to identify flaws, detect attacks, perform attribution, and respond to breaches in a timely and scalable fashion. This vision is made possible by fundamental innovations in several AI domains, including integrated learning and reasoning, human-agent and agent-agent interaction, and strategic gaming and tactical planning.
  • book cover The second edition of my lecture notes on Linear Systems Theory are now available through Princeton Press. This new edition includes a large number of solved exercises.

  • book cover My lecture notes on Noncooperative game theory are now available through Princeton Press.

  • Since 2010, funding from the Inst. for Collaborative Biotechnologies (ICB) and the National Science Foundation (NSF) has been used to organize a Summer internship program for high-school students from Dos Pueblos, Goleta. Take a look at some of that in Jason's webpage.

  • book cover My lecture notes on Linear Systems Theory are now available through Princeton Press.

  • The full list of graduate control courses at UCSB is available here and you can find the courses scheduled for teaching in the current academic year here.

Brief Bio

João P. Hespanha was born in Coimbra, Portugal, in 1968. He received the Licenciatura in electrical and computer engineering from the Instituto Superior Técnico, Lisbon, Portugal in 1991 and the Ph.D. degree in electrical engineering and applied science from Yale University, New Haven, Connecticut in 1998. From 1999 to 2001, he was Assistant Professor at the University of Southern California, Los Angeles. He moved to the University of California, Santa Barbara in 2002, where he currently holds a Distinguished Professor position with the Department of Electrical and Computer Engineering.

Dr. Hespanha is the recipient of the Yale University’s Henry Prentiss Becton Graduate Prize for exceptional achievement in research in Engineering and Applied Science, a National Science Foundation CAREER Award, the 2005 best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing, the 2005 Automatica Theory/Methodology best paper prize, the 2006 George S. Axelby Outstanding Paper Award, and the 2009 Ruberti Young Researcher Prize. Dr. Hespanha is a Fellow of the International Federation of Automatic Control (IFAC) and of the IEEE. He was an IEEE distinguished lecturer from 2007 to 2013.

His current research interests include hybrid and switched systems; multi-agent control systems; game theory; optimization; distributed control over communication networks (also known as networked control systems); the use of vision in feedback control; stochastic modeling in biology; and network security.


Detailed curriculum vitae

Research Interests


As computers, digital networks, and embedded systems become ubiquitous and increasingly complex, one needs to understand the coupling between logic-based components and continuous physical systems. This prompted a shift in the standard control paradigm — in which dynamical systems were typically described by differential or difference equations — to allow the modeling, analysis, and design of systems that combine continuous dynamics with discrete logic. This new paradigm is often called hybrid, impulsive, or switched control.

hybrid systems example

The top diagram is a hybrid automaton model of a queuing system that receives jobs at a rate r and dispatches them at a rate B.

Below the hybrid automaton, we can see the corresponding modelica code.

This and other examples of hybrid systems can be found at the web site of the UCSB course ECE229 - Hybrid and Switched Systems .

Good starting points to learn about hybrid control systems include

  • the web site for the UCSB course ECE229 - Hybrid and Switched Systems
  • the following tutorial paper on hybrid control (mostly devoted to stability issues):
    J. Hespanha. Stabilization Through Hybrid Control. In Heinz Unbehauen, Encyclopedia of Life Support Systems (EOLSS), volume Control Systems, Robotics, and Automation, 2004. [bibtex] [pdf]

Our research covers several aspects of hybrid/switched systems:

  • construction of mathematical models to capture switching between discrete modes, delays, stochasticity, etc.
  • development of formal tools to analyze hybrid systems
  • development of methodologies to design control algorithms for hybrid systems.

Publications on this work can be found at the following URL:

While some of our work on hybrid systems is of a theoretical nature, it is motivated by several high-impact application areas, including networked control systems, cooperative control of autonomous systems, communication networks, and systems biology. Details on some of these application areas are included below.


Network Control Systems (NCSs) are spatially distributed systems in which the communication between sensors, actuators and controllers occurs through a shared band-limited digital communication network. The use of a multi-purpose shared network to connect spatially distributed elements results in flexible architectures and generally reduces installation and maintenance costs. Consequently, NCSs have been finding application in a broad range of areas such as the automotive and aerospace industries, mobile sensor networks, remote surgery, automated highway systems, and unmanned aerial vehicles.

NCS This diagram shows the general architecture of an NCS. Encoder blocks map measurements into streams of “symbols” that can be transmitted across the network. Encoders serve two purposes: they decide when to sample a continuous-time signal for transmission, and what to send through the network. Conversely, decoder blocks perform the task of mapping the streams of symbols received from the network into continuous actuation signals. One could also include in the diagram encoding/decoding blocks to mediate the controllers’ access to the network. We do not explicitly represent these blocks because the boundaries between a digital controller and encoder/decoder blocks are often blurry.

The interest in NCSs has been steadily rising due to several factors:

  • Low-cost, low-power, small embedded processors are widely available, which permits endowing sensors and actuators with local processing and sophisticated network protocols.
  • Low-power, high-bandwidth digital communication is widely available to interconnect a large number of sensors, actuators, and controller nodes. Wireless connections are especially attractive because they have minimal installation costs (although they can be severely constrained in terms of bandwidth).

Inexpensive computation and ubiquitous embedded sensing, actuation, and communication provide tremendous opportunities for societal impact, but also great challenges in the design of networked control systems, because the traditional unity feedback loop that operates in continuous time or at a fixed sampling rate is not adequate when sensor data arrives from multiple sources, asynchronously, delayed, and possibly corrupted. Moreover, the design of NCSs poses novel questions that lie at the intersection of control, communication, and signal processing:

  • How often/When should a sensor transmit a measurement to a control unit?
  • How often/When should a control unit send a control update to an actuator?
  • What forms of error correction/routing/data compression are most adequate for control applications?
  • Which wireless medium-access methods are most effective for control applications?
  • How can one implement a control/estimation algorithm within an embedded processor so as to minimize energy expenditure?

Our research on NCSs is motivated by the following observations:

  • At essentially every layer of the protocol stack, the protocols needed for networks of embedded systems are fundamentally different than those needed for bulk data transfer or even for other “real-time” applications such as voice-over-ip or live video streaming.
  • Fundamental research is needed to solve multiple open problems in the area of networked embedded systems. Adhoc approaches without a strong theoretical underpinning will fail to find appropriate solutions to these problems.
automotive CAN

The widely used Controller Area Network (CAN) bus standard manages the medium access for wireline connections of electronic control units (ECUs). Originally developed for automobiles, the CAN bus was specifically designed to be robust in electromagnetically noisy environments such as those arising in the Supervisory Control And Data Acquisition (SCADA) systems used to monitor or to control chemical or transport processes, in municipal water supply systems, to control electric power generation, transmission and distribution, gas and oil pipelines, and other distributed processes. The CAN standard uses a priority-based collision-free medium access protocol. Image from Robert Bosch GmbH, the developer of CAN.

A good starting point to learn about the design of controllers for NCSs is the following survey:

J. Hespanha, P. Naghshtabrizi, Y. Xu. A Survey of Recent Results in Networked Control Systems. Proc. of IEEE Special Issue on Technology of Networked Control Systems, 95(1):138—162, Jan. 2007. [bibtex] [pdf]

Publications on this work can be found at the following URL:


Robotic agents have the potential to free humans from unpleasant, dangerous, and/or repetitive tasks in which human performance would degrade over time due to fatigue. Currently, assembly lines for the automotive industry are highly automated using robots for welding, painting, machine loading, parts transfer and assembly, etc. However, these robotic systems have little autonomy and essentially continuously execute preprogrammed motions with little cognition of their surroundings.

The expression autonomous agents refers to the control of ground, aerial or aquatic robots so as to perform tasks that require a significant amount of information gathering, data processing, and decision making, without explicit human control. Especially promising (and challenging) is the use of groups of robots to perform complex tasks in a cooperative fashion. These tasks include:

  • environmental monitoring, e.g., for air/water/soil temperature, water pH and salinity, toxic compounds, or mapping endangered species
  • search and rescue operations for disaster response
  • law enforcement activities, such as surveillance or supporting a swat team
  • agricultural activities such as crop spraying

The interest in this area sparked in the last few years because of two main factors:

  • Cost: Advances in materials and fabrication processes have significantly reduced the cost of the hardware platforms (airframes, motors, etc.) and advances in MEMS and VLSI have resulted in sensors (inertial, magnetic, and GPS) and flight control systems that are lightweight, very energy efficient, and cheap.
  • Computation: Because microprocessors and hard drives are becoming fast, small, rugged, energy efficient, and cheap, it is now possible to place significant computational power onboard a flying or ground-moving robot. This allows for the deployment of algorithms that lead to sophisticated autonomous behavior.
Unicorn UAV Unicorn UAV - detail gimbal

The Unicorn UAV from Procerus Technologies is basically a foam wing powered by an electric motor. It has an onboard auto-pilot fed by a GPS unit, three-axis rate gyros and accelerometers, differential and absolute air pressure sensors, and a magnetometer. The autopilot communicates with a ground station through a radio link. We have used Unicorns to test our cooperative control algorithms.

Two key technical challenges in this area have driven our research:

  • Computational complexity: Optimal solutions to many (most!) of the problems that one would like to solve using teams of autonomous agents have large computational complexity. Sometimes this complexity is present even in single-agent versions of these problems (e.g., search), whereas other times is arises when one seeks decentralized solutions, i.e., solutions that do not require centralized decision making.
  • Limited communication: Wireless communication is typically used by a team of agents to coordinate their actions. However, wireless networks are severely constrained in terms of bandwidth and range. In addition, wireless communication is notoriously unreliable. In view of this, for an algorithm to be useful in practice it must require little inter-agent communication and it must be robust with respect to communication faults.

Path planning for an Unmanned Air Vehicle (UAV) with a camera mounted on a gimbal system. By controlling the field-of-view (FOV) of the camera, one enlarges the field-of-regard (FOR) that can be imaged from each position of the UAV.

When computing an optimal path for the UAV, one should take into account that the FOV of the camera can be controlled through the gimbal mechanism. For maximum coverage, one thus needs to consider jointly the problems of path planning for the UAV with that of scheduling the motion of the camera. By solving these problems jointly, one can significantly increase coverage, at the expense of larger (but still manageable) computational complexity.

This picture refers to joint work between our research group (mostly former PhD student James Riehl) and the Toyon Research Corp. (Dr. Gaemus Collins). The algorithms developed were flight tested in Unicorn UAVs in November 2007. In these tests, a team of 4 UAVs (two real and two simulated) cooperated in searching for and tracking a moving ground target.

Publications on this work can be found at the following URLs:


The emergence of novel sensor and actuator suites are creating significant opportunities (and challenges!) for control engineers.

From the actuation side, advances in materials and fabrication techniques are resulting in high-torque/low-power/low-weight electric motors at a low cost. Micro-Electro-Mechanical Systems (MEMS, which result from the integration of mechanical elements and electronics on a common silicon substrate) and smart materials (i.e., materials whose properties can be significantly changed in a controlled fashion by external stimuli, such as stress, temperature, moisture, pH, electric or magnetic fields) are also starting to be used for very low-power and high-efficiency mechanical actuators.

Some of the sensors and actuators used in an autonomous helicopter
as part of a senior capstone project by Mike Hafner and Andrew Venneman
Helicopter (outside) Helicopter (inside)
Futaba servo

Futaba's S9206 high-torque servo for RC airplanes and helicopters based on a coreless motor. It delivers a torque of 9.5 kg/cm at 4.8V, measures 41 x 20 x 38 mm, and weights 53 grams.

MicroMag magnetometer

SparkFun's MicroMag3 integrated 3-axis magnetic field sensing module based on PNI Corporation’s Magneto-Inductive (MI) sensors. It provides a large field of measurement (±11 Gauss) with high resolution (0.00015 Gauss) for up to 2000 samples/second in a small package (25.4 x 25.4 x 19 mm).

SCP1000 barometric pressure sensor

The SCP1000 is an absolute pressure sensor that uses MEMS technology to achieve 17-bit resolution. Under ideal conditions, this sensor can detect the pressure difference within a 9cm column of air.

SparkFun 6DOF IMU

SparkFun's 6 degrees of freedom Inertial Measurement Unit (IMU). It uses a single IC triple axis accelerometer from Freescale combined with three iMEMs gyroscopes to provide Roll, Pitch, and Yaw. It fits in a 51 x 51 mm board and weights 29 grams.

MEMS technology is also revolutionizing sensing, especially for what regards small, low-power, low-cost sensors for navigation. Imaging is another sensing technology with growing importance. Imaging is especially attractive because it is passive, non-contact, very versatile, and low-cost. However, it introduces several technical challenges:

  • the mapping from the image plane to 3-dimensional coordinates can be significantly nonlinear
  • image processing is computationally intensive and introduces (typically variable) latencies from the time of capture to the time measurements are available
  • occlusions, poor lighting, or failure of the image processing algorithms can result in loss of measurements for long periods of time

The key driving force behind our research in this area is the need to develop control algorithms for systems with advanced sensors and actuators that overcome the following challenges:

  • nonlinearity
  • model uncertainty
  • large noise
  • low accuracy
  • loss of measurements
  • fusion of image-based sensing with other sensing modalities
Pioneer robots

ActivMedia Pioneer mobile robots with onboard computing (PC104 boards running Linux); wireless communication (802.11b cards); a sonar suit; and color cameras with zoom mounted on pan-and-tilt platforms.

In this laboratory we have installed a 10-camera Vicon motion capture system (hanging from the ceiling, not visible in the picture) that provides position and orientation of the robots and any other object of interest in the room, at rates in excess of 100 measurements per second. This system essentially provides "indoor GPS" for testing and validation of robot navigation algorithms.

Publications on the use of imaging sensors in control can be found at the following URL:


Systems biology seeks to understand living organisms by modeling and analyzing the complex interactions of genes, proteins, and other cell elements. These interactions occur through biochemical reactions that take place inside the cell or close to the cell membrane. Particularly crucial are the chemical reactions that participate in the complex regulatory mechanism that control cell functions such as the heat shock response, which protects a cell against environmental stresses (heat, cold, oxygen deprivation, etc.); apoptosis, which leads to a programmed cell death with minimal harm to nearby cells; chemotaxis, which permits a cell to move in search of food or to flee from poisons; or cell division, which results in two daughter cells from a single parent cell.

Ultimately, the goal of systems biology is to transform the methodology used for drug discovery, which is currently dominated by mass experimentation. By enlarge, when faced with a new disease or condition, drug developers expose compromised cell cultures to a large number of chemical compounds in the hope of finding a substance that "treats" the disease. Finding such a substance, triggers a second phase of experiments aimed at making sure that this substance does not harm individual cells or organs. In addition, a mechanism must be found to deliver the treatment to the right cells. The goal of systems biology is to guide this effort so that most effort is spent searching among the most promising types of substances and making sure that all cell functions that could be affected by the potential treatment are not negatively affected.

What makes finding cures for diseases especially challenging is the fact that cells are exquisitely regulated mechanisms with multiple feedback loops. Suppose for example that it is discovered that a particular disease develops because a set of cells is lacking protein X. A naive cure would be to inject X into the blood stream in an attempt to increase its concentration. However, this can actually have a completely opposite effect if the body interprets the high concentration of X in the blood as a signal that this protein is being overproduced and shuts down the natural production of X. This is not unlike the apparent paradox that results from placing a heater next to the temperature feedback sensor of a central heating systems and suddenly realizing that the whole building got much colder.

Gene feedback

Simple gene regulatory network, where a gene G produces mRNA (transcription), which in turn produces a protein X (translation). The regulation of the production of X is achieved by a negative feedback loop that results from the fact that the protein X dimerizes to produce X2, which acts as a repressor transcription factor that inhibits transcription by preventing the RNA polymerase from binding to the gene G, thus effectively inactivating it by preventing the production of mRNA. To the right-hand side of the network, we see a set of chemical reactions that can embody this mechanism.

The goal of our research has been to develop tools to analyze complex networks of biochemical reactions. Motivated by the above observations, we are especially interested in constructing dynamical models that highlight the feedback mechanisms in cell regulation and that provide a qualitative and quantitative understanding of how the different genes, proteins, and other cell elements contribute to the observed behavior (phenotype).

Gene regulatory mechanisms typically involve a large number of distinct chemical species, but it is common for some of these species to be represented by just a few molecules, which can invalidate models based on the deterministic chemical rate equation. Our work has been using tools developed for Stochastic Hybrid Systems to construct differential equations that accurately model the stochastic effects present in biochemical networks.

Moment dynamics

The left plot shows the evolution of the mean and variance of the number of molecules of a particular chemical species involved in a bio-chemical network obtained from an ODE that approximates the moment dynamics. In the middle plot we see a typical sample path obtained through a Monte Carlo simulation and in the right plot an histogram obtained from a large number of such simulations. The moment dynamics ODE model was obtained using the package StochDynTools developed by our team and the Monte Carlo simulations were obtained with Petzold's StochKit.

Publications on this work can be found at the following URL:

Software to compute moment dynamics can be found at the following URL:

Selected Grants


  • NSF Project ECCS-2029985 — RAPID: Informed Decision Making for Pandemic Management
  • NIH 2019 Project — Feedback controlled, ultra-high-precision drug delivery
  • NSF Project EPCN-1608880— Online optimization for the control of small autonomous vehicles
  • ONR 2016 MURI — ADAPT: Analytical Framework for Actionable Defense against Advanced Persistent Threats


  • DARPA/DSO & BAE Systems — Stochastic Adaptive Game Analytic Multi-player Optimal Resilient Executive (SAGAMORE)
  • NSF Project CNS-1329650 — ROSELINE: Enabling Robust, Secure and Efficient Knowledge of Time Across the System Stack
  • ONR 2015-18 Project — Demonstration of a Local Carrier-Based Precision Approach and Landing System (LC-PALS)
  • NSF 2011-16 Project — The Evolution of Dynamic Response Strategies: Optimal Control and Evolutionary Dynamics
  • ICB/ARO 2013-16 Project — Neuro-inspired Architectures for Inference and Control in Massively Scalable Multi-Agent Systems
  • ARO 2009-12 MURI — A Cyber-awareness Framework for Attack Analysis, Prediction and Visualization
  • NSF Project ECCS-0835847 — Collaborative Research: CDI-Type II: Advanced Theory and Computational Methods for Modular Analysis and Design of Complex Gene Networks
  • NSF Project CNS-0834805 — Low-Power Digital MEMS Feedback Control
  • NSF Project ECCS-0725485 — Modeling and Analysis of Biological Systems using Stochastic Hybrid Systems
  • NSF Project CNS-0720842 — High-Confidence Algorithms and Protocols for Networked Embedded Systems
  • NSF Project CCR-0311084 — Infinite Dimensional Stochastic Hybrid Systems: A Unified Framework for Distributed Control with Limited and Disrupted Communication
  • NSF Project ANI-0322476 — Collaborative Research: A hybrid systems framework for scalable analysis and design of communications network
  • NSF Project ECS-0242798 — CAREER: Switching and Logic in Control



The full list of control courses at UCSB is available at:




  • ECE5 — Introduction to Electrical & Computer Engineering (Winter'16, Winter'17, Winter'18, Winter'19, Winter'20, Winter'21, Winter'22, Winter'23, Fall'23)
  • ECE147A — Feedback Control Systems: Theory and Design (Fall’03)
  • ECE147C/ME106A/ME155C — Control Systems Design Project/Laboratory (Spring’04, Spring'05, Spring’06, Spring'07, Spring'10, Spring'12, Spring'14, Spring'19, Spring'22, Spring'23)
  • ECE229 — Hybrid and Switched Systems (Winter’04, Fall’05), see
  • ECE230A/ME243A — Linear Systems I (Fall’02,Fall’04,Fall’06, Fall'08, Fall'10, Fall'12, Fall'14,Fall'16,Fall'17,Fall'18,Fall'20,ECE230A/ME243A,
  • ECE230B/ME243B — Linear Systems II (Winter’05, Winter’07, Spring'09, Spring'11, Spring'13)
  • ECE270 — Noncooperative Game Theory (Fall'09, Fall'11, Fall'13, Fall'15, Spring'18, Fall'19, Fall'21, Winter'24; This course used to be ECE594D in Spring'03, Winter'06, Fall'07)
  • ECE271B — Numerical Optimization Methods (Winter’03)
  • ECE594D — Modeling and Control of Large-Scale Distributed Systems (Winter'02)
  • ECE594D — Hybrid Control and Switched Systems (Spring'02)
  • EE364 @ USC — Introduction to Probability and Statistics for Electrical Engineering (Spring'00, Spring'01)
  • EE585 @ USC — Linear Systems Theory (Fall'99, Fall’01)
  • EE599 @ USC — Robot Control using Vision (Fall'00)

Recent awards

  • 2019 ACM SIGBED Hybrid Systems Computation and Control (HSCC) Best Paper Award for the paper "On topological entropy and stability of switched linear systems".
  • 2016 Int. Federation of Automatic Control Fellow Award with citation “For contributions to the stability theory of switched and hybrid systems and its application to the analysis and design of networked control systems.”
  • 2016 Int. Conference on Cyber Physical Systems (ICCPS) Best Paper Award for the paper “SMT-based observer design for cyber-physical systems under sensor attacks”
  • 2009 Ruberti Young Researcher Prize with the citation "for fundamental contributions to adaptive control and to the theory of switched and hybrid systems."
    This award recognizes contributions by a researcher under the age of 41 in the broad field of systems and control. This award is funded by the Antonio Ruberti Foundation and the awardees are selected by the IEEE Control Systems Society.
  • Elevated to the grade of Fellow by the Institute for Electrical and Electronics Engineers (IEEE) with the citation "for contributions to stability techniques for switched and hybrid systems," Jan. 2008.
    Fellow is the highest grade of IEEE membership. The grade of Fellow recognizes unusual distinction in the profession and is conferred by the IEEE Board of Directors upon a person with an extraordinary record of accomplishments that must have contributed importantly to the advancement or application of engineering, science and technology.
  • 2006 George S. Axelby Outstanding Paper Award with the following paper
    João Hespanha. Uniform Stability of Switched Linear Systems: Extensions of LaSalle's Invariance Principle. IEEE Trans. on Automat. Contr., 49(4):470—482, Apr. 2004. [pdf]
    This prize is awarded annually by the IEEE Control Systems Society to recognize outstanding papers published in the IEEE Transactions on Automatic Control. NSF’s support is gratefully acknowledged.
  • 2005 Automatica Theory/Methodology best paper prize for the 2002-2004 period with the following paper
    João Hespanha, A. S. Morse. Switching Between Stabilizing Controllers. Automatica, 38(11), Nov. 2002. [pdf]
    This prize is awarded once every three years by the International Federation of Automatic Control to the best theory/methodology paper published in the previous three years in the journal Automatica. NSF’s support is gratefully acknowledged.
  • Best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing with the following paper:
    Prabir Barooah, João Hespanha. Estimation from Relative Measurements: Error Bounds From Electrical Analogy. In Proc. of the 2nd Int. Conf. on Intelligent Sensing and Information Processing, Jan. 2005. [pdf]
    This prize is awarded once every year to the best paper presented at the ICISIP.

Recent lectures & events


"Noncooperative Game Theory: An introduction for engineers, Minicourse at the University of Pavia, Italy, Oct. 2020. [Abstract, Slides/Notes]

"A modular perspective on the evolution of the p53 network" Invited talk at the Center for Bioengineering Seminar Series, University of California, Santa Barbara, Feb. 2017. [slides]

"Why Should I Care About Stochastic Hybrid Systems" Semiplenary talk at the 49th IEEE Conference on Decision and Control, Dec. 2010. [slides]

"Networked Control Systems: Protocols and Algorithms" Invited talk at the Symposium on Recent Trends in Networked Systems and Cooperative Control (NESCOC'09), Sep. 28, 2009. [slides]

"Networked Control System Protocols Modeling & Analysis using Stochastic Impulsive Systems" Inivited talk at the Workshop on Networked Induced Constraints in Control, Sep 29, 2009. [slides]

"Switched Systems: Mixing Logic with Differential Equations" Plenary talk at the IX Brazilian Symposium of Inteligent Automation (SBAI'09), Sep. 20, 2009. [slides]

"Stochastic Hybrid Systems: Modeling, analysis, and applications to networks and biology” Electrical Engineering and Computer Science Seminar, UC Berkeley, May 1, 2006. [slides]

"Internet Routing Games” Invited talk at the Workshop on Learning and Information in Games and Control, California Institute of Technology, Mar. 22, 2006. [slides]

"Stochastic Modeling of Chemical Reactions (and more…)” UC Santa Barbara Theoretical Ecology Seminar, Mar. 17, 2006. [slides]

"Game theoretical approaches to secure and robust routing” UC Berkeley Seminar, Apr. 22, 2005. [slides]

"Stochastic hybrid systems: Applications to communications networks” 43th CDC Workshop, Paradise Island, Bahamas, Dec. 13, 2004. [slides]

"Communication constraints and latency in Networked Control Systems” UC Riverside Seminar, Nov. 15, 2004. [slides]

"Switched Systems: Mixing Logic with Differential Equations. Plenary talk at Controlo 2002, 5th Portuguese Conference on Automatic Control, University of Aveiro, September 5-7, 2002 [slides].


Course on Modeling Analysis and Design of Hybrid Control Systems at the HYCON Graduate School on Control from the European Embedded Control Institute, February 12-16, 2007.

Trajectory-Tracking, Path-Following, and Formation Control of Autonomous Vehicles. Workshop for the 45th IEEE Conference on Decision and Control, San Diego, CA, December 12, 2006.

Hybrid Systems Biology. Workshop for the 45th IEEE Conference on Decision and Control, San Diego, CA, December 12, 2006.

12th Southern California Non-linear Control Workshop. Santa Barbara, California, June 2, 2006.

9th International Workshop on Hybrid Systems: Computation and Control (HSCC 2006), Santa Barbara, California, from March 29--31, 2006.

Summer Study in Brazil: US undergraduate and graduate students at UCSB may apply for summer study in Brazil. The program consists of 6 weeks of study in language and applied mathematics in Rio de Janeiro, followed by a 8-10 week project in Campinas (near Sao Paolo). Funding for travel and local expenses is provided through the FIPSE program. More details at

Stochastic Hybrid Systems: Theory and Applications. Workshop for the 43rd IEEE Conference on Decision and Control, December 13, 2004.

SensorNets@UCSB Spring’04 Mini-Symposium, Bldg 406 conference room, 9-12:30noon, May 17, 2004.

8th Southern California Non-linear Control Workshop. Santa Barbara, California, May 7-8, 2004.

4th Southern California Non-linear Control Workshop. Santa Barbara, California, May 31—June 1, 2002.

Modeling and control of Large-scale distributed systems. Winter 2002 seminar series.

Logic-based Control. Tutorial session for the 10th Mediterranean Conference on Control and Automation, Lisbon, Portugal, July 9-12, 2002.

Control Using Logic and Switching. Tutorial workshop for the 40th IEEE Conference on Decision and Control in Orlando, Florida, December 4-7, 2001.

Touch in Virtual Environments. One-day conference on Haptics sponsored by the Integrated Media Systems Center and the Annenberg School for Communication, University of Southern California, Los Angeles, February 23, 2001.

Unmanned Air Vehicles: Coordination, Sensing, and Control. Tutorial workshop for the 38th Conference on Decision and Control in Phoenix, Arizona, December 7-9, 1999 and also for the IEEE International Conference on Control Applications/IEEE International Symposium on Computer-Aided Control Systems Design, Anchorage Hilton, Anchorage, Alaska, September 25-27, 2000.

System Theory on the Eve of the 21st Century. Mini-course on state-of-the-art topics on system theory by top experts in the field, Arrábida Courses Summer University, Monastery of Arrábida, Arrábida, Portugal, June 28th - July 3rd, 1999.

Students, Postdocs, and Visitors


Look at this page.


Sean Anderson, BS from Berkeley University, California, started PhD in Fall 2020,

Madeline Blischke, BS from University of Michigan, started PhD in Fall 2021.

Zeki Duman, BS from Bogazici University, Istanbul, Turkey, started PhD in Fall 2018.


Matthew Kirchner, PhD 2023, BS from Washington State University, MS from University of Colorado, currently Assistant Professor at Auburn University (as of Nov. 2023).

Murat Erdal, PhD 2023, BS from Bogazici University, Turkey, currently Data Scientist at Nutromics (as of Nov. 2023).

Raphael Chinchilla, PhD 2023, BE from University of São Paulo, Brazil and ME degree from École Nationale Supérieure des Télécommunications, France, currently Research Scientist at BlackRock AI Labs (as of Nov. 2023).

Sharad Shankar, PhD 2022, BS from the University of California, Los Angeles, currently Navigation Systems Analyst at The Aerospace Corporation, El Segundo, California (as of Nov. 2023).

Henrique Ferraz, PhD 2019, BS from Federal University of Rio de Janeiro, Brazil, currently Senior Battery Management System Controls Engineer at John Deere, Los Angeles, California (as of Nov. 2023).

Justin Pearson, PhD 2018, BS from the University of California, Santa Barbara and MS degree from Stanford University, currently Software Engineer Manager at AppFolio, Inc., Santa Barbara, California (as of Nov. 2023).

David Copp, PhD 2017, BS from University of Arizona, Tucson, currently an Assistant Professor of Teaching at the University of California, Irvine (as of Nov. 2023).

Hari Sivakumar, PhD 2016, BS and MS from Univ. Michigan, Ann Arbor, currently Staff Data Scientist, Product Analytics at Meta, Menlo Park, California (as of Nov. 2023).

Steven Quintero, PhD 2014, BS from Embry-Riddle Aeronautical University, currently Senior Autonomy Robotics Engineer at AeroVironment, Simi Valley, California (as of Dec. 2019) .

Michael Nip, PhD 2014 (co-advised with Prof. Mustafa Khammash), BS from University of California, Berkeley, currently Research Scientist in Quantitative Investing at BlackRock, San Francisco, California (as of Jan. 2022).

Farshad R.Pour Safaei, PhD 2013, BS from University of Tehran, Iran, currently a Battery Management System Architect at Apple, Cupertino, California (as of Nov. 2023).

William (Josh) Russell, PhD 2012, BS from University of Washington, Seattle, currently a Quantitative Analyst at Sabrient Systems, Santa Barbara, California (as of Mar. 2013).

Jason Isaacs, PhD 2012, BS from Eastern Kentucky University, Richmond, currently AssiciateP rofessor at the Computer Science Department, California State University, Channel Islands, Camarillo, California (as of Nov. 2023).

Duarte Antunes, PhD 2011 from Inst. Superior Técnico, Lisbon, Portugal (co-advised with Prof. Carlos Silvestre), Licenciature (5 year degree) and MS degrees in Electrical and Computer Engineering, currently Associate Professor at Eindhoven University of Technology, The Netherlands (as of Nov. 2023)

Alexandre Mesquita, PhD 2010, Undergraduate Degree in Electrical Engineering 2006 (Divisão de Engenharia Eletrónica, Instituto Tecnológico de Aeronáutica - ITA), currently Assistant Professor at the Federal University of Minas Gerais, Brazil (as of Mar. 2013).

Shaunak Bopardikar, PhD 2010 (co-advised with Prof. Francesco Bullo), BT/MT in Mechanical Engineering 2004 (Indian Institute of Technology, Bombay), currently Assistant Professor at the Dept. of Electrical and Computer Eng., Michigan State Univ., East Lansing (as of Aug. 2019).

Abhyudai Singh, PhD 2008, BT in Mechanical Engineering (Indian Institute of Technology, Kaput), currently Assistant Professor at the Electrical and Computer Engineering Dept., University of Delaware (as of Jan. 2015).

James Riehl, PhD 2007, BS in Engineering 2002 (Harvey Mudd College), currently Researcher at the Ecole Polytechnique de Louvain, Belgium (as of Jan. 2019).

Payam Naghshtabrizi, PhD 2007, BS in Electrical Engineering 1997 (Sharif University of Technology, Tehran, Iran), currently a Senior Technical Specialist at the Ethan Corporation (as of Nov. 2023).

Prabir Barooah, PhD 2007, BT 1996 (Indian Institute of Technology, Kanpur), currently Associate Professor at the Department of Mechanical Engineering, University of Florida, Gainsville (as of Jan. 2015).

Chansook Lim, PhD 2006, BS (Seoul National University, Korea), currently Assistant Professor at the Department of Computer Science Hongik University, Korea (as of Feb. 2007)

Yonggang Xu, PhD 2006, BS 1998 (Tsinghua University, Beijing, China), currently Director of Research at 4INFO, Inc., San Mateo, California, USA (as of Mar. 2013)

Junsoo Lee, PhD 2004, BS 1990 (Seoul National University, Korea), currently Assistant Professor at the Department of Computer Science Sookmyung Women's University, Korea (as of Apr. 2006)

Hakan Kizilocak, PhD 2005, IntelRanD, Santa Monica, California, USA (as of 2006)


Guosong Yang, PhD 2017 (PhD from University of Illinois, Urbana-Champagne).


Kyriakos Vamvoudakis, 2012-2016, PhD 2011 (PhD from University of Texas at Arlington, currently Assistant Professor at the Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta (as of Aug. 2019).

Masashi Wakaiki, 2014—2016, PhD 2014 (PhD from University of Kyoto, Japan), currently Assistant Professor at Kobe University, Japan (as of Jan. 2019).

Kunihisa Okano, 2013—2016, PhD 2013 (PhD from Tokyo Institute of Technology, Japan), currently Assistant Professor at the Okayama University, Japan (as of Jan. 2019).

Jason Isaacs, 2012—2015, PhD 2012 (PhD from Univ. of California, Santa Barbara), currently Assistant Professor at the Computer Science Department, California State University, Channel Islands, Camarillo (as of Apr. 2016).

Farshad R.Pour Safaei, 2013—2014, PhD 2013 (PhD from Univ. of California, Santa Barbara), currently Senior Software Engineer, Algorithms & Controls at SolarCity, San Mateo, CA (as of Jan. 2015).

Michelle Chong, 2014—2015, PhD 2013 (PhD from University of Melbourne, Australia).

Steven Quintero, 2014—2015, PhD 2014 (PhD from Univ. of California, Santa Barbara), currently Controls Engineer at AeroVironment, Simi Valley, CA (as of Oct. 2015).

Luis Rodolfo Garcia Carrillo, 2012—2013, PhD 2011 (PhD from Univ. of Technology of Compiegne, France), currently Assistant Professor at Texas A&M (as of Aug. 2019).

Shaunak Bopardikar, 2010—2013, PhD 2010 (PhD from Univ. of California, Santa Barbara), currently Assistant Professor at the Dept. of Electrical and Computer Eng., Michigan State Univ., East Lansing (as of Aug. 2019).

Daniel Klein, PhD 2007 (Intellectual Ventures, Seattle, WA, as of Aug. 2013).

Shengxian Jian, PhD 2007 (PhD from University of Illinois, Urbana Champaign).

Pedro Aguiar, 2002—2005, currently Associate Professor at the Dept. of Electrical and Computer Engineering, University of Porto, Portugal (as of Aug. 2019).

Vladimir Dobrokhodov, 2004—2005, currently Research Associate Professor at the Dept. of Mechanical and Astronautical Engineering, Naval Postgraduate School, Monterey, California, USA (as of Dec. 2005).

Jongrae Kim, 2002—2004, currently Associate Professor in Institute of Design, Robotics & Optimisation, School of Mechanical Engineering, University of Leeds, UK (as of Sep. 2014).


This list only contains visitors that will stay at UCSB for 2 weeks or longer (list sorted by date of last arrival)

Cui Lin, Department of Control Science and Control Engineering, Dalian University of Technology, China, 10/1/2018-9/31/2020

Pedro Sequeira, Dept. of Electrical and Computer Engineering, University of Porto, Portugal, 10/1/2018-9/31/2019


This list only contains visitors that stayed at UCSB for 2 weeks or longer (list sorted by date of last departure)

A. Pedro Aguiar, Dept. of Electrical and Computer Engineering, University of Porto, Portugal, 5/6/2019-5/20/2019

Hikaru Akutsu, PhD student (advisor: Prof. Kenji Hirata), Dept. of Information Science and Control Engineering, Nagaoka University of Technology, Japan, 6/20/2018-1/1/2019.

Lucas Egidio, PhD student (advisor: Prof. Grace Deaecto), School of Mechanical Engineering, Univ. of Campinas, Brazil, 9/1/2018-2/28/2019.

Victor Campus, Federal Univ. of Minas Gerais, Brazil, 8/1/2014-2/1/2015.

Prof. Adolfo Bauchspiess, Dept. of Electrical Eng., Brasilia University, Brazil, 2/10/2014-8/10/2014.

Prof. Márcio Fantini Miranda, Federal University of Minas Gerais, Brazil, 8/31/2012-08/31/2013.

Prof. Kenji Hirata, Dept. of Mechanical Engineering, Nagaoka University of Technology, Japan, 9/1/08-8/31/09, 9/13/2012-3/31/2013.

Prof. Leonardo Torres, Federal University of Minas Gerais, Brazil, 10/1/2010-04/15/2011

Francesco Papi, PhD student (advisor: Prof. Luigi Chisci), University of Florence, Italy, 09/01/09-08/31/10

Prof.Ole Morten Aamo, Norwegian University of Science and Technology, 8/1/09-7/31/10

Alessandro Borri, PhD student (advisor: Prof. Marika Di Benedetto), L'Aquila University, Italy, 9/15/09-6/15/10

Vahid Hassani, PhD student (advisor: Prof. Antonio Pascoal), Institute for Systems and Robotics, Instituto Superior Tecnico, Lisbon, Portugal, 1/15/01 - 5/15/10

Andrea Alessandretti, MS student, University of Perugia, Italy, 10/22/09-04/28/10

Duarte Antunes, PhD student, Inst. Superior Técnico, Lisbon, Portugal, 8/20/07-12/20/07, 4/4/08-6/28/08, 10/1/08-12/12/08, 8/20/09-11/5/09.

Pietro Tessi, PhD student, University of Florence, Italy, 9/3/08-2/25/09

Prof. Carlos Silvestre, Institute for Systems and Robotics, Instituto Superior Tecnico, Lisbon, Portugal, 5/20/08-6/6/08

Prof. Ti-Chung Lee, EE dept., Minghsin Univ. of Science & Tech, Taiwan, 8/3/06-8/24/06 and 7/13/07-8/3/07

Hege Sande, PhD student, Norwegian University of Science and Technology, 1/1/07-6/1/07

Paolo Santesso, PhD student, Padova Univ., Italy, 9/1/06-5/31/07

Prof. Nathan Van de Wouw, Eindhoven Univ. of Technology, Netherlands, 10/01/06-2/1/07

Prof. David Angeli, University of Firenze, Italy, 11/18/06-12/3/06

Aksel Andreas Transeth, PhD student, NTNU in Trondheim, Norway, 10/1/06-12/1/06

José Pedro Gaivão, PhD student, Porto Univ., Portugal, 8/4/06-11/1/06

Information for visitors


Lodging information can be found in this page.


From Los Angeles take the 101N and take the UCSB/Highway 217 exit a few miles after Santa Barbara. Drive up to the end of 217 and go under UCSB's Henley Gate (also known as the East gate). At the rotunda, take the first right exit and then turn left on the first lights. This will get into a large parking structure called P10. All parking in campus is paid, so you need to get a visitor's permit at an automated machine inside the parking structure. Monday-Friday visitor permits are valid in "C", (Commuter) spaces and parking lots.

My office is in the fifth floor of Harold Frank Hall (also known as HFH), room 5157. This building is quite close from the parking structure P10. You can find P10 and HFH at the right-edge of this map. Henley Gate is at D6, P10 at D5, and HFH at E5.

More information can be found at UCSB's visitor center page.

Use to find directions to UCSB. Use the following address for UCSB:

Harold Frank Hall UCSB, Santa Barbara, CA 93106 @34.414,-119.841

Google maps has a pretty good resolution in the Santa Barbara area. Click on the link above to see a satellite image of my building.


I compiled a list of Santa Barbara restaurants and bars that I like. You can get it from this page.

Other stuff

My Erdös number is 4 (one of the paths is given in the table below). If you do not know what this means you may look it up at However, it is not that interesting anyway.

4 Anderson, B. D. O.; Brinsmead, T. S.; De Bruyne, F.; Hespanha, J.; Liberzon, D.; Morse, A. S. Multiple model adaptive control. I. Finite controller coverings. George Zames commemorative issue. Internat. J. Robust Nonlinear Control 10 (2000), no. 11-12, 909--929.
3 Vidyasagar, M.; Anderson, B. D. O. Approximation and stabilization of distributed systems by lumped systems. Systems Control Lett. 12 (1989), no. 2, 95--101.
2 Subbarao, M. V.; Vidyasagar, M. On Watson's quintuple product identity. Proc. Amer. Math. Soc. 26 1970 23--27.
1 Erdös, Paul; Subbarao, M. V. On the iterates of some arithmetic functions. The theory of arithmetic functions (Proc. Conf., Western Michigan Univ., Kalamazoo, Mich., 1971), pp. 119--125. Lecture Notes in Math., Vol. 251, Springer, Berlin, 1972.

Useful links (too many and outdated, I know...)

My underwater photos.

ECE's webmail-roundcube,gmail.

My public RSA key.

My free/busy time :-<