2020 - SIAM Fellow For contributions to the mathematical foundations of signal processing and data science.
1998 - IEEE Fellow For contributions to the theory and practice of statistical signal processing, particularly in estimation, detection, and imaging.
Alfred O. Hero spends much of his time researching Algorithm, Artificial intelligence, Pattern recognition, Mathematical optimization and Statistics. His Algorithm study combines topics in areas such as Mean squared error, Rate of convergence, Estimator and Iterative reconstruction. His Estimator research incorporates elements of Wireless sensor network, Estimation theory, Upper and lower bounds and RSS.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Graph theory and Computer vision. In his research, Data mining is intimately related to Cluster analysis, which falls under the overarching field of Pattern recognition. The various areas that Alfred O. Hero examines in his Mathematical optimization study include Convergence and Applied mathematics.
Alfred O. Hero mainly focuses on Artificial intelligence, Algorithm, Mathematical optimization, Pattern recognition and Estimator. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Radar imaging and Computer vision. His Algorithm research includes themes of Rate of convergence, Upper and lower bounds, Iterative reconstruction and Expectation–maximization algorithm.
His Mathematical optimization study often links to related topics such as Convergence. His Pattern recognition study frequently draws connections between related disciplines such as Entropy. His work carried out in the field of Estimator brings together such families of science as Mean squared error, Estimation theory, Divergence and Applied mathematics.
The scientist’s investigation covers issues in Algorithm, Artificial intelligence, Estimator, Applied mathematics and Random variable. The study incorporates disciplines such as Matrix, Noise, Finite set, Unit cube and Bayes' theorem in addition to Algorithm. He interconnects Machine learning, Multivariate statistics and Pattern recognition in the investigation of issues within Artificial intelligence.
He has included themes like Parametric statistics, Mutual information, Mean squared error, Information theory and Divergence in his Estimator study. His study looks at the relationship between Random variable and topics such as Rate of convergence, which overlap with Discrete mathematics. His Convergence study frequently draws connections to other fields, such as Mathematical optimization.
His primary areas of investigation include Estimator, Artificial intelligence, Algorithm, Applied mathematics and Random variable. The Estimator study combines topics in areas such as Kullback–Leibler divergence, Parametric statistics, Time complexity, Mutual information and Divergence. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
In general Algorithm study, his work on Minimum spanning tree often relates to the realm of Binary number, thereby connecting several areas of interest. His study in Applied mathematics is interdisciplinary in nature, drawing from both Mean squared error, Kernel density estimation and Basis. His Random variable research integrates issues from Computational complexity theory, Rate of convergence and Joint probability distribution.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Locating the nodes: cooperative localization in wireless sensor networks
N. Patwari;J.N. Ash;S. Kyperountas;A.O. Hero.
IEEE Signal Processing Magazine (2005)
Relative location estimation in wireless sensor networks
N. Patwari;A.O. Hero;M. Perkins;N.S. Correal.
IEEE Transactions on Signal Processing (2003)
Space-alternating generalized expectation-maximization algorithm
J.A. Fessler;A.O. Hero.
IEEE Transactions on Signal Processing (1994)
Distributed weighted-multidimensional scaling for node localization in sensor networks
Jose A. Costa;Neal Patwari;Alfred O. Hero.
ACM Transactions on Sensor Networks (2006)
Sparse LMS for system identification
Yilun Chen;Yuantao Gu;Alfred O. Hero.
international conference on acoustics, speech, and signal processing (2009)
A. Coates;A.O. Hero;R. Nowak;Bin Yu.
IEEE Signal Processing Magazine (2002)
A Fast Spectral Method for Active 3D Shape Reconstruction
Jia Li;Alfred O. Hero.
Journal of Mathematical Imaging and Vision (2004)
Using proximity and quantized RSS for sensor localization in wireless networks
Neal Patwari;Alfred O. Hero.
sensor networks and applications (2003)
Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms
J.A. Fessler;A.O. Hero.
IEEE Transactions on Image Processing (1995)
Lower bounds for parametric estimation with constraints
J.D. Gorman;A.O. Hero.
IEEE Transactions on Information Theory (1990)
Profile was last updated on December 6th, 2021.
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