MIT Reports to the President 1999–2000


The Laboratory for Information and Decision Systems (LIDS) is an interdepartmental laboratory for research and education in systems, communication, and control. It is staffed by faculty members, research scientists, postdoctoral fellows and graduate students drawn principally from the Department of Electrical Engineering and Computer Science, as well as the Department of Aeronautics and Astronautics, Mechanical Engineering and the Sloan School of Management. Undergraduate students participate in the research program of the Laboratory through the Undergraduate Research Opportunities Program (UROP). Every year several research scientists from various parts of the world visit the Laboratory to participate in its research programs.

The fundamental research goal of the Laboratory is to advance the field of systems, communication, control, and signal processing. In doing this, it explicitly recognizes the interdependence of these fields and the fundamental role that computers and computation play in this research. Specifically, the work conducted at LIDS falls into several areas. Research in the area of communications, networks and systems includes fundamental work on data networks, information theory and communication theory. Systems research includes satellite communications, wireless communication, optical communications and networks. Research in the area of estimation and signal processing includes work on multi-resolution statistical signal processing, robust estimation in the presence of non-normal noise, and the analysis of large scale systems. Research in the area of control ranges from theoretical issues such as robustness, aggregation, and adaptive control, to the construction of a computer-aided design environment for the control of unmanned air vehicles, the use of neural networks for approximating optimal controller designs and system identification and the study of natural neuro-control systems. Research in the area of algorithms includes analytical and computational methods for solving broad classes of optimization problems arising in engineering and operations research, and for applications in communication networks, control theory, power systems, computer-aided manufacturing, as well as such topics as resource allocation and scheduling under uncertainty and neuro-dynamic programming. Research on perceptual systems and machine learning includes the problems of speaker-independent speech recognition, on- and off-line handwritten character recognition.

As an interdepartmental laboratory, LIDS reports to the Dean of the School of Engineering, Thomas L. Magnanti and the director of the laboratory is Professor Vincent W. S. Chan.

The Center for Intelligent Control Systems, an inter-university, interdisciplinary research center operated by a consortium of Brown University, Harvard University, and MIT, resides administratively within LIDS.

Twenty faculty members, several research staff members, and approximately 90 graduate students are presently associated with the Laboratory and the Center. Currently, the Laboratory and the Center provide some 60 research assistantships to graduate students. Undergraduate students also participate in research and thesis activities. A number of postdoctoral and visiting appointments are made.

Financial support is provided by the Air Force Office of Scientific Research (AFOSR), the Army Research Office (ARO), the Defense Advanced Research Projects Agency (DARPA), C.S. Draper Laboratory, Intel, Motorola University Partnerships in Research, the National Science Foundation (NSF), the National Reconnaisance Office (NRO), the Office of Naval Research (ONR), Siemens AG, Tellabs, Inc., and the Multiple University Research Initiative Program (MURI).

The current research activities of the laboratory cover a wide range of theoretical and applied areas in systems, communications, control and signal processing. These areas include the following:


Data Communication Networks

The major objective of this work is to develop the scientific base needed to design data communication networks that are efficient, robust, and architecturally clean. Both wide-area and local-area networks, both high-speed and low-speed networks, and both point-to-point and broadcast communication channels are of concern. Some specific topics of current interest are power control, the capacity of wireless channels with parallel relays, splitting and successive decoding for wireless networks, routing in wireless networks, quality of service control, diverse traffic mixes, failure recovery, topological design, and the use of pricing as a mechanism for efficient resource allocation. Professors Dimitri P. Bertsekas, Vincent Chan, Robert G. Gallager, Muriel Medard, Eytan Modiano, Dr. Steven G. Finn, and their students are conducting this research.

Optical Networks

During the last year, Professors Vincent Chan, Bob Gallager, Eytan Modiano and Sunny Siu participated in a ‘Next Generation Internet’ program funded by DARPA. The focus of the program is to design and prototype the next generation local and metropolitan area access network with orders (up to 4) of magnitude increase in data rate, but at the same time decrease the cost of delivery per bit by approximately the same amount. The network will use multiple wavelengths (colors) to increase capacity and optical devices for routing and switching. One interesting architectural features of the network will be an option for the user of the network to set up direct end-to-end optical flows for future applications with very large transactions (Gigabytes and beyond). The architecture design will culminate in a test network deployed in eastern Massachusetts (LIDS as one of the nodes), with 10 Gbps access rate for users and well over a Tbps capacity. In the future DARPA will connect this test network and others around the country to form SUPERNET as a prototype for the Next Generation Internet. The highlight this year was the demonstration of a 1 Gbps flow from a workstation at MIT to one in Washington DC over a fiber without the need of an electronic repeater. Because of the interdisciplinary nature of the research, LIDS is able to partner with members of LCS (Dr. David Clark), Lincoln Laboratory, AT&T, Cabletron, JDS Fitel and Nortel.

Professor Eytan Modiano was awarded an NSF grant to study mechanisms for providing optical bypass in the Next Generation Internet (NGI). The goal of the research is to use Wavelength Division Multiplexing (WDM) technology together with novel algorithms to reduce the size, cost and complexity of electronic switches and routers in the network leading to a dramatic increase in the traffic capacity that can be supported by the NGI.

Professor Muriel Medard, in collaboration with Professor Steven S. Lumetta of the University of Illinois Urbana-Champaign, has worked in an NGI project funded by DARPA in the area of survivability and reliability in direct access networks. The goal of this project is to provide robust access to optical networks in a way that ensures fault-tolerant communications. Results obtained in this area include capacity efficient restoration, as well as robust routing and protocols for local direct access. More details may be found on the World Wide Web at

Satellite Communications And Networking

Professors Vincent Chan, Eytan Modiano and John Tsitsiklis and Steven Finn have been working on a new research project on satellite data communications and networking. A DARPA funded Next Generation Internet Program is started with Motorola and Teledesic as research partners.

In the past, commercial satellite communication systems have always been used for trunking purposes, whereas military satellite systems have been providing direct individual user access for decades. With the launching of several new satellite communications systems such as the Iridium system by Motorola and the Globalstar system by Loral, the commercial sector has started a major new trend of providing economic and ubiquitous communication services to mobile users and users with small earth terminals. There are several proposed systems that will focus on supporting data communications. Inevitably, in the future, these satellite systems will be interconnected among themselves and with other terrestrially based networks to form a multi-purpose integrated heterogeneous network of global extent. With the goal towards a commercially based network, the integration of disparate communication modalities of satellite systems, fiber and wireless networks presents the usual challenges of internetworking such as the creation of gateway functions, routing and QoS negotiations across different network domains and network management and control. Typically, satellite open-air links are different from wired terrestrial links in a number of ways: they have higher bit-error rates, larger propagation delays, and fading due to weather conditions.

Furthermore, space networks typically are limited in resources such as onboard buffers and transmission power. As a result, existing protocols often fail to operate efficiently over air or space links. To efficiently connect space and ground networks the wide disparity in transmission capacity and channel quality that exists between these two segments must be addressed. This gives rise to a range of issues including: space/ground network architectures, the design of efficient end-to-end protocols, quality of service assurance and the design of efficient interfaces between the ground and space portions of the network. With this space/terrestrial network multiple connected, new and interesting dimensions open up for the consideration of efficient routing between users and new and more effective congestion control algorithms. The added property of rich path diversity also permits applications with more robust requirements when exploited properly. The proposed research addresses architecture designs for efficient data communications over an interconnected heterogeneous LEOS/terrestrial-wired-wireless network. We have been concentrating on three main themes that we have identified as areas where significant impact to network performance can be made when efficient designs are applied:

Codes On Graphs And Iterative Decoding

Professor G. David Forney, Jr. and his student Sae-Young Chung have been studying codes defined on graphs and iterative decoding algorithms, particularly low-density parity-check (LDPC) codes. In joint work with Richardson and Urbanke of Lucent Bell Laboratories, they have devised optimization algorithms with which they have designed LDPC codes that are able to approach the Shannon limit within 0.006 dB and that have implementable decoding algorithms. A number of useful approximate analysis methods have been found. Finally, a new general structure for codes on graphs has been found, called "normal graphs." This structure yields a clean separation of functions in decoding, and some powerful and general duality theorems.

Communication Under Channel Uncertainty

In many applications, e.g., mobile wireless communication and military communication in the presence of jamming, the channel characteristic and the nature of the noise are unknown in the design stage of the communication link. For such applications it is imperative to design robust receivers and codes that allow reliable communication over each of a wide family of channels. To this end Professor Amos Lapidoth is studying universal receivers that do not require precise knowledge of the channel law, and yet perform asymptotically as well as the best receivers that could have been designed had the channel been known in advance. Professor Lapidoth is also studying the ultimate bounds on the rates at which reliable communication can be guaranteed over a channel that is only known to belong to some given family of channels.

Professor Medard has been investigating several issues in the area of wireless communications over uncertain channels. In collaboration with Professor R. Srikant at the University of Illinois Urbana-Champaign, she has investigated the effect of unequal channel knowledge at the sender and receiver. In particular, they have developed bounds to assess the effectiveness of applying techniques designed for certain idealized channel models to more channels with more detailed models. In collaboration with Professor Andrea J. Goldsmith of Stanford, she has investigated the capacity of time-varying channel with sender and receiver side information, in particular for channels with perfect side information but significant inter-symbol interference, for which no capacity formulas existed.

Wireless Communications

Professor Robert G. Gallager together with several students, have ongoing projects in mobile communication aimed at developing a cohesive theory and set of insights for wireless multiple access. Specific research includes the capacity of fading channels, the transmission of bursty sources over a shared time-varying channel, transmitter power allocation across many cells, and capacity improvements through joint decoding.

Professor Muriel Medard has worked in the area of developing capacity models for wireless channels. With Professors Sean Meyn of the University of Illinois Urbana-Champaign and Professor Andrea Goldmisth, she has worked on defining capacity regions for time-slotted packetized access for uncoordinated users sharing a single channel.

Collaboration With Tellabs

The Laboratory for Information and Decision Systems and Tellabs Operations, Inc., a telecommunications equipment manufacturer, are developing a novel approach to collaborative research. In this approach, LIDS and Tellabs integrate industrial research interests within MIT's research and educational environment. The key difference between this new model of collaboration and traditional approaches is the focus on human resources as the primary enabler. Toward this end, LIDS provides Tellabs with access to faculty, students, visitors, facilities, and infrastructure support, while Tellabs dedicates resident corporate research positions to the effort, assuming responsibility both for co-advising students research and for technology transfer as an internal corporate process. LIDS benefits from the persistent presence of industrial researchers, and Tellabs benefits from the leveraging of LIDS's staff. LIDS and Tellabs have been jointly working on this new research model for three years and look forward to its growth and refinement.


Stochastic Systems Group

The Stochastic Systems Group (SSG) is led by Professor Alan S. Willsky, with the assistance of Research Scientist, Dr. John Fisher. In addition, the group includes 10-12 graduate students, several postdoctoral researchers (currently 2), visitors, and participants from other groups within LIDS and from other MIT laboratories and departments. The general focus of research within SSG is on the development of statistically-based algorithms and methodologies for complex problems of information extraction from signals, images, and other sources of data. The work in the group extends from basic mathematical theory to specific areas of application. Current applications include SAR-based automatic target recognition (ATR), biomedical image analysis, oceanographic and hydrological data assimilation, and situation modeling for complex phenomena (such as military situations). Funding for this research comes from a variety of sources, including ONR, AFOSR, ARO, DARPA, ODDR&E (through AFOSR, ARO, and ONR), NIH, and NSF.

In addition to directing these research activities, Professor Willsky is very active in supporting government and, in particular, DoD organizations in assessing and planning technology investments. In particular, he is a member of the Air Force Scientific Advisory Board. Each of the following research areas being pursued within SSG involves both theoretical development as well as applied studies to the application areas mentioned previously.

Multi-Resolution Statistical Signal Processing

For some time now (and in part sparked by the flurry of activity associated with the wavelet transform) there has been considerable interest in algorithms for processing signals or images at multiple resolutions. SSG has played a leadership role in developing a statistical basis for such multiresolution processing that has had a significant impact as evidenced not only by the applications pursued within the group (in SAR-based ATR, oceanography and hydrology, and computer vision, for example) but also by the increasing use of our methodology by others in fields ranging from biomedical imaging to chemical engineering to helioseismology.

The key to this research area is the direct statistical modeling of phenomena at multiple resolutions using graphical models on trees, in which each level on a tree corresponds to a particular resolution. We have developed very efficient algorithms for estimation, data fusion, and other image analysis tasks using these models and have also demonstrated, primarily through example and application, that a wide variety of real phenomena and applications can be captured within this framework. Because of this success, our current and planned efforts involve expanding the domain of applicability of our methodology, both by pursuing additional applications and by developing tools for constructing multiresolution models needed as the basis for applying our results. In particular, we have recently had several important new results that allow us much more easily to construct multiresolution models for complex phenomena.

Most recently we have increased our investigation on how we can exploit our methodology for problems involving much more complex graphical models as arise in military command and control or in problems of monitoring complex systems, a problem of great national concern because of its relevance to making critical national infrastructure secure. In particular our approach to modeling takes a very different perspective from most work on graphical models–in particular, we focus explicitly on developing accurate but approximate models that have structure that leads to very fast optimal algorithms, rather than most work on graphical models that involves trying to find tractable suboptimal solutions to exact graphical models that do not have structure that allows fast optimal inference. Recently we have had a significant breakthrough in this area, using our tree-based algorithm as the control component in algorithms for graphical models on graphs other than trees.

Nonlinear and Geometric Image Analysis

During this past year, we have continued our efforts in the area of nonlinear/non-Gaussian image analysis, including the explicit estimation/extraction of geometric information such as object boundaries and segmentation. The first part of our work in this area involves the development of statistically-based curve evolution algorithms. Such algorithms involve explicitly defining and dynamically evolving curves in ways that lead to accurate and efficient segmentation of images. Methods of this type that had been developed by others had a number of very attractive features, including the fact that they provided seamless ways in which a curve could separate into multiple curves or merge from several disjoint curves to a single curve, allowing automatic and easy segmentation of multiple regions of interest (e.g., multiple blood cells in a microscopic image). However, previously developed methods either were very sensitive to noise or required ad hoc preprocessing to remove noise but that also reduced the resolution of the resulting segmentation. In our work, we have developed a first principles statistical approach to curve evolution that deals with noise and variability in a statistically optimal way without sacrificing resolution. The resulting suite of algorithms that we have developed have shown great promise for the analysis of a wide variety of imagery of varying quality and contrast. In particular, in addition to continue our work on extending our methodology we are also actively involved with clinicians and researchers at Brigham and Woman's Hospital who are pursuing the development of image guided therapy procedures for prostate cancer.

Finally, we have continued our work on developing non-Gaussian multiresolution models for images that capture more faithfully the scale-to-scale variability observed in real images. In particular, real images have what are commonly referred to has "heavy-tailed" statistical behavior which is not captured by Gaussian distributions. We have now constructed a rich class of models that both captures the behavior of real imagery and also leads to very efficient and robust image processing algorithms.

Information-theoretic Methods in Image Analysis and Fusion

During this past year, we have continued to increase our efforts in developing and using methods of non-parametric statistics for a variety of very complex image analysis and fusion problems. In particular, in our work on SAR-based ATR we have built non-parametric probabilistic models that capture statistically significant differences in the scattering response of different types of scatterers and then using these models to exploit these differences for feature extraction, enhancement, and recognition. We have also used non-parametric statistics together with the concept of mutual information to develop new approaches for functional MRI studies in which we wish to correlate particular experimental protocols (e.g., a patient squeezes and then releases a ball) with brain activity so that mapping of brain activity to function can be performed. This work is interesting in that it involves fusing information of very different modalities (force applied to a ball and Magnetic Resonance Imagery). We have also used similar tools for the fusion of video and acoustic data and, in particular, for the localization of the sources of sound (e.g. human voices) in the video field of view.

Radar-Based ATR

Professor Shapiro and his students have been working on several physics-based approaches to ATR problems, focusing on the important area of performance assessment. Their work has included analysis of the benefits of adaptive-resolution processing in synthetic-aperture radar (SAR) systems used for target detection and recognition. They have also established an analytical framework for determining the accuracy with which the orientation of a ground-based target may be estimated from forward-looking infrared (FLIR) and laser radar (LADAR) imagery. This study is being extended to a predict the detection and recognition performance limits of these two sensors. In both the orientation, estimation and detection/recognition contexts the performance gains that accrue from sensor fusion are being quantified. In all of this work, the principal objective is to replace simulation and/or train-and-test performance assessments with more analytical techniques which allow the interrelated effects of sensor parameters, target signatures, and atmospheric propagation on system performance to be determined and understood.

Nonlinear System Analysis And Designs

Professor Alexandre Megretski and his students are working on the development of new methods of nonlinear system analysis, and application of these techniques in various control systems, (flight control, firm control, animation control, hybrid systems, etc.). The work involves a broad spectrum of system-theoretic topics including modeling, identification, stability analysis, and optimization. One important objective is to learn how simplifications necessarily made in nonlinear system modeling affect the validity of nonlinear control design.

Neurobiological Modeling

Professors Dahleh and Massaquoi are interested in two problems. The first is the development of a hierarchical model of the interaction between the cerebrum and cerebellum that is anatomically justified that can explain two-dimensional arm motions. The second problem is deriving a multi-scale, multi-resolution model that explains EEG data, with specific interests in Anesthesia. These projects are in collaboration with various laboratories/departments at MIT as well as the Massachusetts General Hospital and the Brockton V.A. Medical Center.


This project focuses on analytical and computational methods for solving broad classes of optimization problems arising in engineering and operations research, as well as for applications in communication networks, control theory, power systems, computer-aided manufacturing, and other areas. Currently, in addition to traditional subjects in nonlinear and dynamic programming, there is an emphasis on the solution of large-scale problems involving network flows, as well as in the application of decomposition methods. Professors Dimitri P. Bertsekas and John N. Tsitsiklis and their students perform this work.

Neurodynamic Programming

Problems of sequential decision making under uncertainty are all pervasive; for example, they arise in the contexts of communication networks, manufacturing systems, logistics, and in the control of nonlinear dynamical systems. In theory, such problems can be addressed using dynamic programming techniques; in practice, however, only problems with a moderately-sized state space can be handled. This research effort deals with the application of neural networks and other approximation and interpolation methodologies to overcome the curse of dimensionality of real-world stochastic control problems. The objectives driving this research are twofold. First, to develop the theoretical foundations and improve the understanding of such methods, using a combination of tools from approximation theory, dynamic programming, and stochastic algorithms. Second, to use these methods for solving some large-scale problems of practical interest. Application areas being currently investigated include problems in logistics (resource scheduling and assignment), finance (pricing of high-dimensional derivative instruments, dynamic portfolio management in the presence of risk constraints), supply chain management, and communications (dynamic channel allocation). Professors Dimitri P. Bertsekas and John N. Tsitsiklis and their students perform this work.

Perceptual Systems

Sanjoy Mitter, Stefano Casadei and their collaborators have been working on various aspects of Perception and Recognition. Perception and recognition consist in recovering useful information about the environment from sensed data and prior knowledge about the real world and the sensors. Artificial systems designed to carry out this task are yet much inferior to biological systems, largely due to the size and intricacy of the knowledge required to carry out reliable inference in unrestricted and uncertain domains. For instance, in visual perception, several factors contribute to render the problem difficult: clutter, occlusion, and variability of the objects in the scene. The basic engineering principle of decomposing a complex task into simpler and independent tasks is difficult to apply to perception and recognition due to the extremely complicated and yet unknown pattern of interdependency among the many "acts of perception" involved. For instance, the recognition of an occluded chair in a cluttered office environment is highly dependent on the interpretation of its subparts, the other objects near to it and the overall scene of which it is part.

What are the components, which are involved in perception and recognition? What architecture should these components to be organized into? How does one minimize the interdependence of these components? How should uncertainty be represented? How does one acquire and represent the knowledge about the real-world and the sensors? Several projects are being undertaken to find answers to these questions.

Electric Power Systems

Dr Marija Ilic, together with her 10-12 graduate students and several international visitors, is currently working on new concepts for planning and operating electric power systems under restructuring. These range from: modeling and simulation of the electricity market dynamics; new control paradigms for distributed monitoring, estimation and decision making over time horizons relevant for reliable operations as well as for dynamic investments under uncertainties; and through modeling, simulation and control of large transmission grids required to provide open access to all market participants. This work is closely coupled to industry through the "Consortium on New Concepts and Software for the Electric Power Industry under Restructuring," which is administered through the Energy Laboratory and currently has five members. The problems of immediate solution needs concern congestion control using technical feedback as well as incentive pricing. These are challenging examples to the state-of-the-art in the hybrid control of large dynamic systems, decision making under uncertainties and approximate dynamic programming.

Dr. Ilic recently published jointly with Professor John Zaborszky a textbook entitled "Dynamics and Control of Large Electric Power Systems," which could be used by people interested in applications of systems and control theories to the electric power systems. Dr. Ilic also participates in an EPRI/DoD sponsored multi-university project on complex interactive networks as part of the Harvard led team. She has spent 1999—2000 school year as a Control, Networks and Computational Intelligence (CNCI) Program Director at the National Science Foundation on half time basis.


Multivariable And Robust Control

The systematic design of multiple-input, multiple-output systems, using a unified time-domain and frequency-domain framework to meet accurate performance in the presence of plant and input uncertainty is an extremely active research area in the Laboratory. Various theoretical and applied studies are being carried out by Professors, Munther A. Dahleh, Gunter Stein, Steve Massaquoi and their students. Theoretical research deals with issues of robustness, aggregation, and adaptive control. The aim of the research is to derive a computer-aided design environment for the design of control systems which can address general performance objectives for various classes of uncertainty. Furthermore, new results on the robustness of nonlinear feedback systems, using feedback linearization, have been obtained for unstructured uncertainty model errors. Recent application-oriented studies include the control of large space structures, helicopters, submarine control systems, issues of integrated flight control, control of chemical processes and distillation columns, automotive control systems, and the modeling and analysis of biological control systems.

New areas of application of robust control theory are now emerging at LIDS, including the real-time, agile guidance of single and multiple Unmanned Aerial Vehicles (UAV) as well as vehicle anticollision problems arising in Air Traffic Control. Some of these concepts are implemented and tested on small helicopter systems. Professors Feron and Massaquoi are beginning a collarboration regarding the brain's internal representation of external world dynamics.

Feedback Control Using Approximate Dynamic Programming

Feedback controllers for nonlinear systems are often driven by potential (Lyapunov) functions, whereby the controller at each step steers the system in a direction of decrease of the potential function. The optimal cost-to-go function that results from dynamic programming formulations of control problems is a suitable such Lyapunov function, except that it may be difficult to compute. This research investigates whether recent approximate dynamic programming methods, that rely extensively on simulation and neural network training, can lead to a viable methodology for designing Lyapunov functions and controllers for nonlinear feedback systems. This research is carried out by Professors Munther A. Dahleh and John N. Tsitsiklis, and their students.

Identification And Adaptive Control

Determining the fundamental limitations and capabilities of identification and adaptive control is an active area of research, carried out by Professors Munther A. Dahleh, John N. Tsitsiklis, and their students. This research program draws upon areas such as information-based complexity theory and computational learning theory, as well as upon the theory of robust control. One aim of this research is to develop a theory that combines both system identification and robust control within the same framework, in which a controller that meets given performance specifications can be designed based on finite noisy data. Issues studied include the representation of uncertainty in both noise and model, design of experiments, sample and computational complexity, as well as implementation of optimal algorithms.

Computational Complexity

Problems in systems and control theory are of varying degrees of difficulty, ranging from polynomial-time solvable to undecidable. Professor Tsitsiklis and coworkers have been using tools from theoretical computer science (theory of computation) to characterize the intrinsic difficulty of problems in stochastic optimal control, and various stability problems for hybrid systems, saturated linear systems, and linear time-varying systems.

Control In Presence Of Communications Constraints

Sanjoy Mitter in collaboration with Vivek Borkar (Tata Inst. of Fundamental Research, India), Nicola Elia and several graduate students have been working on fundamental issues of control in the presence of communication constraints. The goal of this research is to understand the interaction between information and control in the presence of uncertainty. Development of Real-time Information Theory forms an essential part of this research topic.

Unmanned Air Vehicles

Professors Dahleh and Feron with their students have been working on developing control architectures for unmanned vehicles. This research entails the development of a hierarchical control system that replaces the human pilot in order perform agile maneuvers. The group is also involved in building and demonstrating these concepts on a small helicopter.

Control Of Hybrid Systems

Hybrid systems are compositions of continuous systems (described by ordinary differential equations) and discrete systems that are event-driven. A theory of optimal control of such systems, based on the theory of impulse control and piecewise-deterministic processes, has been developed by Professor Sanjoy K. Mitter in collaboration with Professor Michael Branicky, currently at Case Western Reserve University, Professor Vivek Borkar, visiting from the Indian Institute of Science, and Dr. Nicola Elia, a post-doctoral scientist. Numerical methods for the dynamic programming inequalities arising out of the optimality conditions for these systems have also been developed. Incorporation of the model in the simulation package OMULA/OMSIM has been undertaken in joint work with Prof. Astrom and his group at Lund, Sweden. Professor Mitter has been working with Professors Borkar and Chandru of the Indian Institute of Science, Bangalore on solving questions and problems in logic using mathematical programming. It is planned to unify this work with the previously mentioned work on Hybrid Systems.

Center For Intelligent Control Systems

The Center for Intelligent Control Systems (CICS) combines distinguished faculty from MIT, Harvard University, and Brown University in interdisciplinary research on the foundations of intelligent machines and intelligent control systems. Established in October 1986, CICS is headed by Professor Sanjoy Mitter, Director; Professor Roger Brockett, Harvard University, Associate Director; and Professor Donald McClure, Brown University, Associate Director. The research activities of the Center are loosely grouped in five areas: Signal Processing, Image Analysis, and Vision; Automatic Control; Mathematical Foundations of Machine Intelligence; Distributed Information and Control Systems; and Algorithms and Architectures. A number of outstanding graduate students are appointed Graduate Fellows. The Center also hosts several senior visitors for varying lengths of time each year.


Speakers in the LIDS Colloquium and Seminar Series included: Professor Shankar Sastry, University of California, Berkeley, Professor John Tsitsiklis, LIDS, MIT, Professor Sassam Bamieh, University of California, Santa Barbara, G. David Forney, Bernard M. Gordon Adjunct Professor, LIDS, MIT, Professor Bin Yu, University of California, Berkeley, Lucent Technologies Bell Labs, Professor Karl J. Astrom, Lund Institute of Technology, Sweden, Dr. David Clark, LCS, MIT, Professor Eytan Modiano, LIDS and Aeronautics and Astronautics, MIT, Professor Stephen Boyd, Stamford University, Professor Jose M. F. Moura, Carnegie Mellon University, Dr. Debasis Mitra, Bell Laboratories/Lucent Technologies, Dr. Nigel Newton, Essex University, Colchester, England, Professor Steven Pinker, Brain and Cognitive Sciences, MIT, Dr. Richard Barry, Chief Technology Officer, Sycamore Networks, Jeffrey P. Sutton, M.D., Ph.D., Massachusetts General Hospital, Harvard-MIT HST, Professor Bhubaneswar Mishra Courant Institute, New York University, Professor Jay W. Forrester, Sloan School, MIT, Professor Kack Keil Wolf, University of California, San Diego, Professor Dimitris Bertsimas, Sloan School, OR & LIDS, MIT, Dr. Hans-Andrea Loeliger, Endora Tech AC, Switzerland, Professor Anthony Ephremides, University of Maryland, Professor Pramod P. Khargonekar, University of Michigan, Professor Kameshwar Poolla, University of California, Berkeley, Fred Baker, Fellow, Cisco Systems, Chair, Internet Engineering Task Force.

Visitors to the Laboratory for Information and Decision Systems included Professor Jose M. F. Moura, Carnegie Mellon University; Dr. Nigel Newton, Essex University, Colchester, England; Professor Vevek Borkar, TATA Institute of Technology, India; and Dr. Roberto Segala, University of Bologna, Italy.

Robert G. Gallager Elected Fellow of IEC, June 6, 2000, awarded IEEE Millenium Medal, 2000.

Professor John Tsitsiklis was the plenary speaker at the Fourteenth International Symposium of Mathematical Theory of Networks and Systems, Perpignan, France, June 2000.

Alan Willsky has been appointed the Edwin S. Webster Professor of Electrical Engineering.

Vincent Chan

MIT Reports to the President 1999–2000