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Raviv Raich

Associate Professor
Electrical & Computer Engineering
  • Ph.D., Electrical Engineering, Georgia Institute of Technology, Atlanta, Georgia, 2004
  • M.Sc., Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel, 1994
  • B.Sc., Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel, 1994

Raviv Raich joined the faculty in the School of Electrical Engineering and Computer Science at Oregon State University in Fall 2007. Raviv Raich received the B.Sc. and M.Sc. degrees in electrical engineering from Tel-Aviv University, Tel-Aviv, Israel, in 1994 and 1998, respectively and the Ph.D. degree in electrical engineering from Georgia Institute of Technology, Atlanta, Georgia, in 2004. Between 1999 and 2000, he worked as a researcher with the communications team, Industrial Research Ltd., Wellington, New Zealand.

Most recently, he was a postdoctoral research fellow at the University of Michigan, Ann Arbor, Michigan. His main research interest is in statistical signal processing with specific focus on manifold learning, sparse signal reconstruction, and adaptive sensing. Other research interests lie in the area of statistical signal processing for communications, estimation and detection theory.

Research Interests: 
  1. Adaptive Sensing/Sampling. In the classical setting, a measurement setup remains fixed throughout the process of data acquisition. In adaptive sensing, at every time step, the measurement setup is altered based on past measurements to overall maximize the information of interest. Some of the active areas of application of this concept appear in mine detection, see through the wall, SAR imaging, and target tracking.
  2. Manifold Learning. Manifolds offer the capability to describe high dimensional data using a low dimensional representation. Dimensionality reduction of high-dimensional data that lies on a manifold allows visualization of the data, reduction in computational complexity of data processing, and the capability of intrinsic data processing. Areas of application include: medical diagnosis, target recognition, analysis of internet data, and sensor networks.
  3. Sparse Representations for Signal Processing. We are interested in investigating data that is sparse according to some basis or dictionary. In other words, the data can be represented using only a small number of basis/dictionary elements. Image compression methods, which are based on vector quantization, demonstrate that an image can be represented in a sparse fashion through fixed bases, e.g., discrete cosine transform (DCT) and wavelets. Areas of application include: electromagnetic imaging, molecular imaging, and sensor/waveform selection.