Alfred O. Hero III is the R. Jamison and Betty Williams Professor of Engineering and co-director of the Michigan Institute for Data Science (MIDAS) at the University of Michigan, Ann Arbor. His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D from Princeton University (1984), both in Electrical Engineering. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has served as President of the IEEE Signal Processing Society and as a member of the IEEE Board of Directors. He has received numerous awards for his scientific research and service to the profession including the IEEE Signal Processing Society Technical Achievement Award in 2013 and the 2015 Society Award, which is the highest career award bestowed by the IEEE Signal Processing Society. Alfred Hero's recent research interests are in the data science of high dimensional spatio-temporal data, statistical signal processing, and machine learning. Of particular interest are applications to networks, including social networks, multi-modal sensing and tracking, database indexing and retrieval, imaging, biomedical signal processing, and biomolecular signal processing.
The Hero group is currently focusing on the following broad research themes in statistical signal processing, bioinformatics and imaging:
1. Probabilistic models for high dimensional datasets. This includes information geometric approaches to dimensionality reduction, sparsity regularized parameter estimation and classification, and correlation screening problems (large p small n).
2. Graphical models for multivariate data. This includes sparse mathematical models for developing algorithms for clustering, anomaly detection, pattern matching, multimodality image registration, and database indexing and retrieval in the large p small n regime.
3. Information theoretic surrogates for signal and image processing. This involves identification and empirical estimation of surrogate measures of performance that can be used in place of intractable task-specific measures such as mean squared error, probability of misclassification error, and detection error.
K. Todros and A.O. Hero, "On Measure Transformed Canonical Correlation Analysis," IEEE Trans. on Signal Processing, to appear. Available as arXiv:1111.6308. ARO.
A.O. Hero and B. Rajaratnam, "Hub discovery in partial correlation graphs," IEEE Trans. on Information Theory, to appear 2012. Author preprint. NSF, DIGITEO
K. Sricharan, R. Raich and A.O. Hero, "Estimation of non-linear functionals of densities with confidence," to appear in IEEE Trans on Information Theory 2012. Available as arXiv:1012.4188. AFOSR
S.-U. Park, N. Dobigeon, A.O. Hero, "Semi-blind Sparse Image Reconstruction with Application to MRFM," IEEE Trans on Image Processing.To appear 2012. Available as arXiv:1203.4723. ARO MRFM MURI.
P. Shearer, R. Frazin, A.O. Hero, and A. Gilbert, "The first stray light corrected EUV images of solar coronal holes," Astrophysical Journal Letters, ApJ, 749, L8, Mar. 2012.
Y. Chen and A.O. Hero, "Recursive lasso," IEEE Trans. on Signal Processing, to appear. Author preprint. AFOSR MURI.
R. Mittelman, N. Dobigeon and A.O Hero, "Hyperspectral image unmixing using multiresolution sticky hierarchical Dirichlet process," IEEE Trans. on Signal Processing, vol. 60, no. 3, Mar 2012. Author preprint. AFOSR MURI.