Raviv Raich

Associate Professor
Electrical & Computer Engineering
Education: 
  • 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
Biography: 

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.
2012
Behmardi, B., and R. Raich, "On Confidence-Constrained Rank Recovery in Topic Models", IEEE Transactions on Signal Processing, vol. 60, issue 10, pp. 5146 - 5162, 10/2012. Abstract
Briggs, F., X. Z. Fern, and R. Raich, "Rank-loss support instance machines for MIML instance annotation", CM SIGKDD Conf. on Knowledge Discovery and Data Mining, Beijing, China, ACM Press, pp. 534-542, 08/2012. Abstract
Behmardi, B., F. Briggs, X. Z. Fern, and R. Raich, "Regularized joint density estimation for multi-instance learning", IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, IEEE, pp. 740 - 743, 08/2012. Abstract
Sricharan, K., R. Raich, and A. O. Hero, "Estimation of Nonlinear Functionals of Densities With Confidence", IEEE Transactions on Information Theory, vol. 58, issue 7, pp. 4135 - 4159, 07/2012. Abstract
Cao, J., R. Raich, G. C. Temes, and G. Cauwenberghs, "Multi-channel mixed-signal noise source with applications to stochastic equalization", 2012 IEEE International Symposium on Circuits and Systems - ISCAS 2012, Seoul, Korea (South), IEEE, pp. 2497 - 2500, 05/2012. Abstract
Alstrom, T. S., R. Raich, N. V. Kostesha, and J. Larsen, "Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, IEEE, pp. 2125 - 2128, 03/2012. Abstract
Chunikhina, E., G. Gutshall, R. Raich, and T. Nguyen, "Tuning-free joint sparse recovery via optimization transfer", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, IEEE, pp. 1913 - 1916, 03/2012. Abstract
2011
Behmardi, B., and R. Raich, "Convex optimization for exact rank recovery in topic models", IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Beijing, China, IEEE, pp. 1 - 6, 09/2011. Abstract
Lakshminarayanan, B., and R. Raich, "Inference in Supervised latent Dirichlet allocation", IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Beijing, China, IEEE, pp. 1 - 6, 09/2011. Abstract
Sricharan, K., R. Raich, and A. O. Hero, III, "k-nearest neighbor estimation of entropies with confidence", 2011 IEEE International Symposium on Information Theory - ISIT, St. Petersburg, Russia, IEEE, pp. 1205 - 1209, 08/2011. Abstract
Behmardi, B., and R. Raich, "On provable exact low-rank recovery in topic models", 2011 IEEE Statistical Signal Processing Workshop (SSP), Nice, France, IEEE, pp. 265 - 268, 06/2011. Abstract
Behmardi, B., R. Raich, and A. O. Hero, III, "Entropy estimation using the principle of maximum entropy", ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, IEEE, pp. 2008 - 2011, 05/2011. Abstract
Neal, L., F. Briggs, R. Raich, and X. Z. Fern, "Time-frequency segmentation of bird song in noisy acoustic environments", 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, IEEE, pp. 2012 - 2015, 05/2011. Abstract
Carter, K. M., R. Raich, W. G. Finn, and A. O. Hero, III, "Information-Geometric Dimensionality Reduction", IEEE Signal Processing Magazine, vol. 28, issue 2, pp. 89 - 99, 03/2011. Abstract
2010
Sricharan, K., R. Raich, and A. O. Hero, III, "Boundary compensated k-NN graphs", 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Kittila, Finland, IEEE, pp. 277 - 282, 09/2010. Abstract
Lakshminarayanan, B., and R. Raich, "Non-negative matrix factorization for parameter estimation in hidden Markov models", 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Kittila, Finland, IEEE, pp. 89 - 94, 08/2010. Abstract
Sricharan, K., R. Raich, and A. O. Hero, III, "Optimized intrinsic dimension estimator using nearest neighbor graphs", 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, IEEE, pp. 5418 - 5421, 03/2010. Abstract
Carter, K. M., R. Raich, and A. O. Hero, III, "On Local Intrinsic Dimension Estimation and Its Applications", IEEE Transactions on Signal Processing, vol. 58, issue 2, pp. 650 - 663, 02/2010. Abstract
2009
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Briggs, F., R. Raich, and X. Z. Fern, "Audio Classification of Bird Species: A Statistical Manifold Approach", Ninth IEEE International Conference on Data Mining (ICDM), Miami Beach, FL, IEEE, pp. 51 - 60, 12/2009. Abstract
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Lakshminarayanan, B., R. Raich, and X. Z. Fern, "A Syllable-Level Probabilistic Framework for Bird Species Identification", 2009 International Conference on Machine Learning and Applications (ICMLA)2009 International Conference on Machine Learning and Applications, Miami, FL, IEEE, pp. 53 - 59, 12/2009. Abstract