Alan Oppenheim, Ph.D.
Department of Electrical Engineering and Computer Science
Ford Professor of Engineering
Margaret Mac Vicar Faculty Fellow
Alan V. Oppenheim received the S.B. and S.M. degrees in 1961 and the Sc.D. degree in 1964, all in electrical engineering, from the Massachusetts Institute of Technology. He is also the recipient of an honorary doctorate from Tel Aviv University, which was conferred upon him in 1995. In 1964, Dr. Oppenheim joined the faculty at MIT, where he is currently Ford Professor of Engineering and a MacVicar Faculty Fellow. Since 1967 he has been affiliated with MIT Lincoln Laboratory and since 1977 with the Woods Hole Oceanographic Institution. His research interests are in the general area of signal processing and its applications. He is coauthor of the widely used textbooks Discrete-Time Signal Processing and Signals and Systems. He is also editor of several advanced books on signal processing.
Dr. Oppenheim is a member of the National Academy of Engineering, a fellow of the IEEE, a member of Sigma Xi and Eta Kappa Nu. He has been a Guggenheim Fellow and a Sackler Fellow at Tel Aviv University. He has also received a number of awards for outstanding research and teaching, including the IEEE Education Medal, the IEEE Centennial Award, the Society Award, the Technical Achievement Award and the Senior Award of the IEEE Society on Acoustics, Speech and Signal Processing. He has also received a number of awards at MIT for excellence in teaching, including the Bose Award and the Everett Moore Baker Award.
The Digital Signal Processing Group in the MIT Research Laboratory of Electronics focuses on developing general methods for signal processing that can be applied to a wide range of applications. Our research over the last four decades has focused both on traditional areas such as signal modeling, sampling and signal representations, and signal estimation, and on non-traditional topics such as fractal signals and chaotic behavior in nonlinear dynamic systems. Some of the specific classes of signals that we have studied include speech, images, sensor network data, communication signals, and signals associated with problems in ocean acoustics. We also often look to nature for inspiration and as a metaphor for new signal processing directions. Currently, we are studying signal processing in cell biology both to model signal processing mechanisms in those systems and to use them as a potential metaphor for new signal processing algorithms.
Biological Signal Processing
Our goals in studying biological signal processing are to understand and model the underlying processes, and to emulate these processes so that we can apply them to other applications unrelated to biology. Our research in this area focuses on developing new frameworks to model the information processing in biological cells. We wish to understand the algorithms that are used in the signaling pathways and then exploit the results to develop a new generation of algorithms for engineered distributed networks.
Understanding the Information Processing in Biological Cells
We are developing models at different resolutions. High-level models focus on network topology and node interconnectivity, using concepts from random graph theory to examine and analyze the distribution and properties of the signaling network. Low-level models examine the dynamics of cellular signal processing within a new framework that we developed based on interacting Markov chains. We combine these modeling results with experimental data obtained through collaborations with other CSBi investigators to understand how the topologies and dynamics of biological signaling networks influence the properties of the system.
Evolution of biological signalling networks
These two complementary modeling approaches also allow us to examine the evolution of biological signaling networks. High-level models provide a framework for formulating hypotheses about the process of network evolution. Low-level models allow us to compare conserved pathways in order to understand how their complexity and network topology lead to functionality and refinement of the system responses.
New Algorithms Inspired by Biology
We are using these biological network models and tools to explore a new generation of networks that are composed of engineered systems such as sensors and processors. Our goal is to formulate new algorithms and topologies for signal processing on distributed networks. As an example, we have developed a surface-mapping and flattening algorithm inspired by bacterial chemotaxis and are using it to formulate new approaches to signal sampling.
- M.R. Said, T.J. Begley, A.V. Oppenheim, D.A. Lauffenburger, L.D. Samson. "Global Network Analysis of Phenotypic Effects: Protein Networks and Toxicity Modulation in Saccaromyces Cerevisiae". Proc. Nat`l. Acad. Sci. USA, December 2004.
- C. K. Sestok, M. R. Said, A. V. Oppenheim, "Randomized Data Selection in Detection with Applications to Distributed Signal Processing" Proceedings of the IEEE, Nov. 2003.
- M. R. Said, A. V. Oppenheim, D. A. Lauffenburger. "Modeling Cellular Signal Processing Using Interacting Markov Chains". Proc. Int. Conf. on Acoustics, Speech, Signal Processing (ICASSP-2003). (Hong Kong), April 2003.
- Boufounos P., El-Difrawy S., Ehrlich D., "Basecalling Using Hidden Markov Models." submitted to the Journal of Franklin Institute, special issue on Genomics, Signal Processing and Statistics (invited paper to appear). Y. C. Eldar and A. V. Oppenheim, "Quantum Signal Processing", IEEE Signal Processing Magazine, November, 2002.
- Y. C. Eldar and A. V. Oppenheim, "Quantum Signal Processing", IEEE Signal Processing Magazine, November, 2002.
- A.V. Oppenheim and R.W. Schafer with John Buck, Discrete-Time Signal Processing, Prentice-Hall 1999. 2003.
Last Updated: April 16, 2008