Gregory Wornell, Ph.D.
Department of Electrical Engineering and Computer Science
Professor Electrical Engineering and Computer Science
Gregory W. Wornell received the B.A.Sc. degree from the University of British Columbia, Canada, and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology, all in electrical engineering and computer science, in 1985, 1987 and 1991, respectively.
Since 1991 he has been on the faculty at MIT, where he is Professor of
Electrical Engineering and Computer Science, and Chair of the department`s Graduate Area I (Systems, Communication, Control, and Signal Processing) within the department`s doctoral program. He has held visiting appointments at the former AT&T Bell Laboratories, Murray Hill, NJ, the University of California, Berkeley, CA, and Hewlett-Packard Laboratories, Palo Alto, CA.
Prof. Wornell`s research interests and publications span the areas of information and coding theory, signal processing, and communication. He has been involved in the Signal Processing and Information Theory societies of the IEEE in a variety of capacities, and maintains a number of close industrial relationships and activities. He has won a number of awards for both his research and teaching.
The Signals, Information, and Algorithms Lab is interested in developing efficient solutions to address emerging problems involving manipulation of signals and information in diverse settings. Our research focuses on the development of fundamental limits and architectural principles at one end of the spectrum, and implementation issues and experimental investigations at the other. In the systems biology area, we are interested in understanding in a fundamental sense the mechanisms by which information is encoded, distributed, and interpreted in living organisms. Such investigations not only are important to progress in health science and bioengineering, but can also be expected to provide insights into the design of efficient distributed systems and networks in engineering more generally.
In our work, we draw on diverse mathematical tools to address important new problems that frequently transcend the boundaries between disciplines. Some examples of these tools include: the theory of information, computation, and complexity; statistical inference and learning, signal processing and systems; coding and communication; and networks and queuing. In the investigations we are beginning in biological systems, we are drawing on our experience in applying such tools to problems in wireless, sensor, multimedia, and broadband networks.
Information networks in living organisms
The human body has developed efficient mechanisms for distributing information throughout the body, with the brain providing central coordination. The communication between these different areas is mediated through stimulation of neurons in the body. The Signals, Information, and Algorithms Lab is interested in understanding how information is encoded and managed by the brain and the way such encoding is matched to the characteristics of the underlying propagation medium and the ultimate response mechanism. Research in this area will lead to important advances in both basic neuroscience and the associated biomedical applications. In addition, such knowledge is likely to suggest new paradigms for engineering efficient systems for non-biological applications.
The tasks faced by living organisms in extracting and encoding information from multiple sources, and subsequently distributing the resulting signals in a complex heterogeneous propagation environment are similar to kinds of functionality required of modern systems and networks in engineering. Despite the ubiquity of such systems and networks and several decades of research, we still know surprisingly little about the fundamental limits of such manmade networks, nor what fundamental design principles they imply. By investigating the methods evolution has led to in structuring such networks in living organisms, we expect to learn more about what may be the right architectural principles for designing engineering systems. Such research requires close interaction with experimentalists to develop meaningful models and analysis.
Just one example of a class of questions that would be important to understand include the relationship between stimulus and the joint firing patterns of collections of neurons in the different parts of the brain and nervous system � in particular, what aspects of the stimulus are important, how the stimulus is in turn encoded into such firing patterns, and how the associated neurons are selected. Recent advances in measurement instruments and other experimental apparatus are beginning to enable such investigations.
- S. Cohen, S. C. Draper, E. Martinian, G. W. Wornell, "Stealing Bits from a Quantized Source," to appear in IEEE Trans. Inform. Theory.
- S. C. Draper and G. W. Wornell, "Sensor Networks: Side Information Aware Coding Strategies," to appear in IEEE J. Select. Areas Commun.
- R. J. Barron, B. Chen, and G. W. Wornell, "The Duality Between Information Embedding and Source Coding with Side Information and Some Applications," IEEE Trans. Inform. Theory, vol. 49, no. 5, May 2003.
- H. C. Papadopoulos, G. W. Wornell, and A. V. Oppenheim, "Signal Encoding from Noisy Measurements using Quantizers with Dynamic Bias Control," IEEE Trans. Inform. Theory, vol. 47, no. 3, pp. 978-1002, Mar. 2001.
- B. Chen and G. W. Wornell, "Analog Error- Correcting Codes Based on Chaotic Dynamical Systems," IEEE Trans. Commun., vol. 46, no. 7, pp. 881-890, July 1998.
Last Updated: April 16, 2008