Professor
Department Vice Chair
Department Graduate Advisor
Electrical & Computer Engineering Dept. (ECE)
University of California, Santa Barbara (UCSB)

Associate director
Center for Control, Dynamical-systems, and Computation (CCDC)

Executive committee member
Institute for Collaborative Biotechnologies (ICB)

Email: hespanha @ ece.ucsb.edu
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

• The full list of control courses at UCSB is now available at: http://www.ece.ucsb.edu/~hespanha/ccdc/
• If you arrived recently at UCSB and you are a graduate student in the ECE department, you may find this presentation useful.

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 Professor position with the Department of Electrical and Computer Engineering. Prof. Hespanha is Associate Director for the Center for Control, Dynamical-systems, and Computation (CCDC), Vice-Chair of the Department of Electrical and Computer Engineering, and a member of the Executive Committee for the Institute for Collaborative Biotechnologies (ICB). From 2004—2007 he was an associate editor for the IEEE Transactions on Automatic Control.

His current 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; and stochastic modeling in biology.

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, and the 2006 George S. Axelby Outstanding Paper Award. Dr. Hespanha is a Fellow of the IEEE and an IEEE distinguished lecturer since 2007.

Detailed curriculum vitae

HYBRID SYSTEMS

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.

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:
http://www.ece.ucsb.edu/~hespanha/published.html#1SwitchedandHybridSystems

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.

NETWORKED CONTROL SYSTEMS (NCSs)

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.

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.

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:
http://www.ece.ucsb.edu/~hespanha/published.html#4NetworkedControlSystems

COOPERATIVE CONTROL OF AUTONOMOUS AGENTS

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.

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:
http://www.ece.ucsb.edu/~hespanha/published.html#7CooperativeControlandMulti-agentSystems
http://www.ece.ucsb.edu/~hespanha/published.html#8SearchPursuit-EvasionandPathPlanning

ADVANCED SENSING AND ACTUATION

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

	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.
	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).
	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'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

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:
http://www.ece.ucsb.edu/~hespanha/published.html#10Vision-basedControl

SYSTEMS BIOLOGY

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.

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.

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:
http://www.ece.ucsb.edu/~hespanha/published.html#13Biology

Software to compute moment dynamics can be found at the following URL:
http://www.ece.ucsb.edu/~hespanha/software/stochdyntool.html

CURRENTLY

none

IN THE NEAR FUTURE

• ECE230A — Linear Systems I (Fall’08)
• ECE230B — Linear Systems II (Spring’09)

PAST

• ECE147A — Feedback Control Systems: Theory and Design (Fall’03)
• ECE147C — Control Systems Design Project & ME106A — Advanced Mechanical Engineering Laboratory (Spring’04, Spring'05, Spring’06, Spring'07)
• ECE229 — Hybrid and Switched Systems (Winter’04, Fall’05), see http://www.ece.ucsb.edu/~hespanha/ece229/
• ECE230A — Linear Systems I (Fall’02,Fall’04,Fall’06)
• ECE230B — Linear Systems II (Winter’05, Winter’07), see http://www.ece.ucsb.edu/~hespanha/ece230b
• 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)
• ECE594D — Noncooperative Game Theory (Spring’03, Winter’06, Fall'07), see http://www.ece.ucsb.edu/~hespanha/ece594d/
• 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)

• 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.

TALKS

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 for Controlo 2002, 5th Portuguese Conference on Automatic Control, University of Aveiro, September 5-7, 2002 [slides].

CONFERENCES AND WORKSHOPS

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 http://www.cds.caltech.edu/~murray/cdsa/.

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.

PROSPECTIVE STUDENTS

Look at this page.

CURRENT STUDENTS

Abhyudai Singh, BT in Mechanical Engineering (Indian Institute of Technology, Kaput), started PhD in Fall 2004.

Shaunak Bopardikar, BT/MT in Mechanical Engineering 2004 (Indian Institute of Technology, Bombay), started PhD in Fall 2005 (co-advised with Prof. Francesco Bullo).

Alexandre Mesquita, Undergraduate Degree in Electrical Engineering 2006 (Divisão de Engenharia Eletrônica, Instituto Tecnológico de Aeronáutica - ITA), started PhD in Fall 2006.

William (Josh) Russell, started PhD in Fall 2007.

Jason Isaacs, started PhD in Fall 2007.

FORMER STUDENTS

James Riehl, PhD 2007, BS in Engineering 2002 (Harvey Mudd College), currently Systems Design Engineer Specialist, AT&T Government Solutions, Inc. (as of Oct.2007).

Payam Naghshtabrizi, PhD 2007, BS in Electrical Engineering 1997 (Sharif University of Technology, Tehran, Iran), currently at Ford Motor Company (as of Oct.~2007).

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

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 Senior Control Engineer at Advertising.com, Mountain View, CA, USA (as of Apr. 2006)

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 2004, currently Associate Scientist at Intelligent Optical Systems, Inc., Torrance, CA, USA (as of Oct. 2005)

CURRENT POSTDOCS

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

FORMER POSTDOCS

Pedro Aguiar, 2002—2005 (Adjunct Professor at the Instituto Superior Técnico, Portugal, as of July 2007).

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

Jongrae Kim, 2002—2004 (Lecturer/Assistant Professor at the Aerospace Department of the University of Glasgow, UK, as of July 2007).

CURRENT AND FUTURE VISITORS

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

Duarte Antunes, PhD student, Inst. Superior Técnico, Lisbon, Portugal, 8/20/07-12/20/07 and 4/4/08-6/28/08

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

PAST VISITORS

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

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

LODGING

Lodging information can be found in this page.

DIRECTIONS TO UCSB

From Los Angeles take the 101N and take the UCSB/Highway 217 exit a few miles after Santa Barbara. At the main gate get a parking permit and directions to the nearest parking lot. My office is in Harold Frank Hall (former Engineering I), 5th floor.

You can get the campus map from this page (the main gate is at D6 and Harold Frank Hall/Engineering I is at E5) and a map of the Goleta area (showing the 101 and the 217) from this page .

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

Use http://maps.google.com/ 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.

FOOD & FUN in SANTA BARBARA

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

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 http://www.oakland.edu/enp/. However, it is not that interesting anyway.

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

My underwater photos.

ECE's webmail, mail server, gmail.

UCSB's corporate time server

My public DSA key.