Alfred Hero - Research Activities



This page is obsolete and no longer is being updated. For current up-to-date version see (.html)

Links to publications .html

The link .html points to a reverse chronological listing of all my publications (most downloadable). The hero wiki .html has links to reproducible research webpages of some of the more recent publications on this list.
For some transparencies of recent presentations and seminars click here . For my bibtex citation file click here .bib . For my .sty file click here .sty .

Numbers of citations frequently but not always correlate well with quality or impact of research. However, if you are interested in seeing the Hero group's most highly cited research publications look at Google My Citations webpage (.html) .


(Updated: May. 2017) The Hero group focusses on building foundational theory and methodology for data science and engineering. Data science is the methodological underpinning for data collection, data management, data analysis, and data visualization. Lying at the intersection of mathematics, statistics, computer science, information science, and engineering, data science has a wide range of application in areas including: public health and personalized medicine, brain sciences, environmental and earth sciences, astronomy, materials science, genomics and proteomics, computational social science, business analytics, computational finance, information forensics, and national defense. The Hero group is developing theory and algorithms for data collection, analysis and visualization that use statistical machine learning and distributed optimization. These are being to applied to network data analysis, personalized health, multi-modality information fusion, data-driven physical simulation, materials science, dynamic social media, and database indexing and retrieval. Several thrusts are being pursued:

1. Development of tools to extract useful information from high dimensional datasets with many variables and few samples (large p small n). A major focus here is on the mathematics of "big data" that can establish fundamental limits; aiding data analysts to "right size" their sample for reliable extraction of information. Areas of interest include: correlation mining in high dimension, i.e., inference of correlations between the behaviors of multiple agents from limited statistical samples, and dimensionality reduction, i.e., finding low dimensional projections of the data that preserve information in the data that is relevant to the analyst.

2. Data representation, analysis and fusion on non-linear non-euclidean structures. Examples of such data include: data that comes in the form of a probability distribution or histogram (lies on a hypersphere with the Hellinger metric); data that are defined on graphs or networks (combinatorial non-commutative structures); data on spheres with point symmetry group structure, e.g., quaternion representations of orientation or pose.

3. Resource constrained information-driven adaptive data collection. We are interested in sequential data collection strategies that utilize feedback to successively select among a number of available data sources in such a way to minimize energy, maximize information gains, or minimize delay to decision. A principal objective has been to develop good proxies for the reward or risk associated with collecting data for a particular task (detection, estimation, classification, tracking). We are developing strategies for model-free empirical esitmation of surrogate measures including Fisher information, R\'{e}nyi entropy, mutual information, and Kullback-Liebler divergence. In addition we are quantifying the loss of plan-ahead sensing performance due to use of such proxies.

4. Geometric embedding of combinatorial optimization. One of the major roadblocks to making scientific progress in solving grand challenge problems is the curse of dimensionality, This problem is especially acute in combinatorial optimization where the behavior of the objective function under permutations and combinations has no obvious geometric structure. Remarkably, smooth geometric structure emerges as one allows the domain dimension to grow in many Euclidean combinatorial optimization problems including shortest path through a similarity graph and multiobjective pattern matching. This geometric embedding can lead to approximate solution of the combinatorial problem via solution of a simpler variational continuous optimization problem. Further progress in this field could lead to general combinatorial solvers that utilize the considerable machinery available in scientific computing, e.g., general ordinary differential equation (ode) and partial differential equation (pde) solvers. Grand challenge problems that could benefit from this research include: monitoring pandemics (path analysis on epidemic proximity graphs); energy and transportation (optimal routing); and adaptive drug design (computing Pareto frontiers); to name just a few.

These areas arise in the context of several sponsored projects in the Hero lab including the following:

1. Mathematical approaches to representing spatio-temporal data, including astronomical data, network data, biomedical diagnostics and predictive health (M_Cubed, DARPA-PHD, NSF, NIH-P01). We are developing methods for high throughput analysis of biomarker data. One project, funded by DARPA (ended in 2014), aims to predict health and disease propagation (epidemics) over a close knit human population based on a combination of genetic, metabolic, and social network data. Another project, funded by NIH, is developing image registration methods that are capable of compensating for patient motion and multimodality distortions. Another project, funded by NSF, is developing machine learning methods that can handle data that comes in the form of distributions. An example is flow cytometry data where a each cell in a blood sample is assayed and assigned a multidimensional label, including antibidy, protein binding, and morphology labels. Another example, funded by a M-Cubed in collaboration with faculty in the Depts of Astronomy and Physics, is astronomical data where the measurements are spatial point process realizations (stars) or spatial and spatio-temporal image patch realizations (solar metrology). In each of these areas we are developing approaches based on high dimensional data analysis, adaptive sampling (when to take an measurement or assay and where to take it from), large scale statistical inference, and multimodality data/information integration. Another project (DOE) is funding us to develop analysis tools for nuclear non-proliferation treaty verification using on-site and remote data collection strategies to monitor declared facilities and detect undeclared facilities.

2. Subspace processing for imaging and information fusion (NIH-P01, ARO-MURI, AFRL-UES, AFRL-ATR). Subspace models are models that are sparse in a basis spanning a low dimensional subspace of the data. Such models allow fusion of multi-modality data without overfitting and accomplish denoising of high dimensional datasets. Several methods have been pursued here including dictionary learning, non-negative factor analysis, and measure transformation generalization of PCA, ICA, and CCA that allow non-linear components to be captured in the original coordinates (unlike kernel methods of PCA, ICA and CCA). These methods have been applied to different projects including spatio-temporal gene expression analysis, predicting health and disease, materials science, video imaging, radar imaging, and social nets.

3. Network measurements and analytics (ARO-Social, NSF, AFOSR). We are developing distributed models and methods for analysis of high dimensional spatio-temporal network data. One focus, previously funded by NSF, is on Internet data analysis, including flows (TCP, UDP, etc), application level (email, http), and transport (end-to-end delays, packet losses) to detect anomalies and reconstruct topology of the network. Another focus, funded by ARO, is on emergent behavior analysis on social networks where we are developing methods to aggregate different layers of social network information, including behavioral and relational edge attributes. Under an AFOSR grantm we are considering the problem of reliably estimating structural properties of graphical models in sample-starved network data collection regimes. These areas are being pursued in the context of applications such as network tomography, topology estimation for distributed activity detection, target tracking, event classification. Modalities that have been investigated are: Internet traffic data, email data, fMRI brain activiation, 12 lead EKG monitoring and diagnostics, and gene regulation networks.

4. Database indexing and retrieval (ARO-Databases, ended in 2014). In this ARO funded project, the objective is to develop methods based on sparsity and dimensionality reduction for searching large multimedia databases of images and videos. This involves the development of scalable methods of feature selection, similarity matching, and spatio-temporal modeling that can improve precision and recall performance. See webpage (.html) for a short bibliography on this topic. Current areas of focus are event detection and correlation in videos, pose estimation and 3D shape retrieval, multimodality retrieval using information theoretic measures, and multiple criteria image search. Some applications areas that we are currently considering are: automated recommendation systems, human-in-the-loop indexing and retrieval, and.

5. Value-centered information-driven sensing (ARO-MURI). Under this ARO funded project we are investigating ways to improve the value of information delivered to the end-user in sensor networks that are limited by finite bandwidth, temporal dynamics, latency, and other factors. We are developing new models that account for these network limitations and for which performance guarantees deliver more timely and relevant information to the operator of the sensor network. This project involves sensor modalities including radar, sonar, video, LIDAR, and atmospheric sensors. One of our main focii is developing an information theory that accounts for human-in-the-loop. One of the principal applications being considered is centralized and decentralized cooperative target search: by querying a pair of human and machine sensors to localize an image, classify a scene, or to estimate the position of a weak target.

Lab Resources


Software Resources


Past and Present Postdoctoral Students



  1. Salimeh Sekeh (2015-)

  2. Sijia Liu (2015-2018)

    , IBM Research, Cambridge, MA.

  3. Hye Won Chung (2014-2017), Asst. Prof at KAIST, Seoul.

  4. Yassin Yilmaz (2014-2016), Asst. Prof at University of South Florida

  5. Taposh Banerjee (2014-2015), Postdoc at Harvard.

  6. Jie Chen (2014-2015), Asst. Prof at Northwestern Polytechnic University, China.

  7. Goran Marjanovic (2013-2014), Univ South Wales, Australia

  8. Greg Newstadt, (2012-2014), Software Engineer at Google, Inc.

  9. Alex Kulesza, (2012-2014) Postdoc at Univ of Michigan

  10. Joyce Liu, (2012-2013) Postdoc at UC Berkeley and UPenn

  11. Dennis Wei (2011-2013) (.html), IBM Watson Research

  12. Sung Jin Hwang, (2012), Software Engineer at Google, Inc.

  13. Koby Todros (2010-2012), Assistant Professor, Ben Gurion Univ, Israel.

  14. Francesca Bassi (2011-2012), Research Scientist, ENST Paris.

  15. Roni Mittelman (2010-2011), Post-doc Univ of Michigan.

  16. Xu Chen (2010-2011) (.html). Research Engineer. Sharp Labs of America.

  17. Ami Wiesel (2007-2009), Associate Professor, Hebrew University, ami at cs.huji.ac.il

  18. Eran Bashan (2008) CEO Hygiea Inc, bashan at umich.edu

  19. Nicolas Dobigeon (2007), Professor ENSEEIHT (Toulouse)

  20. Mark Kliger (2006-2007), CTO Medasense Biometrics, Ltd, mkliger at medasense.com

  21. Neal Patwari (2005-2006), Associate Professor University of Utah, npatwari at ece.utah.edu

  22. Raviv Raich (2004-2007), Associate Professor Oregon State University, raich at eecs.oregonstate.edu

  23. Pei-Jung Chung (2004), Lecturer, University of Edinborough, UK, peijung_chung at yahoo.com

  24. Cyrille Hory (2003-2004), Research Staff INRETS, Paris France hory at inrets.fr.

  25. Christophe Vignat (2000-2001), Professor Univ. of Paris XI, Paris France. Christophe.VIGNAT at lss.supelec.fr.

  26. Stephane Chretien, (1995-1998), Associate Professor University of Besancon, Besancon, France, chretien at descartes.univ-fcomte.fr

  27. Olivier Michel, (1993-1994), Professor INP, Grenoble, France olivier.michel at gipsa-lab.grenoble-inp.fr

Students Doing Research Under my Supervision


  1. Joel LeBlanc (EECS G) jwleblan at umich.edu

  2. Brendan Oselio (EECS G)

  3. Elizabeth Hou (Statistics MS)

  4. Yaya Zhai (Bioinformatics MS)

  5. Yun Wei (Mathematics/AIM)

  6. Audelia Wittbrodt (Applied Physics)

Former PhD Students


  1. Tianpei Xie, Applied Scientist, Amazon. Thesis title: Robust Learning from Multiple Information Sources, (.pdf). Dept of EECS May 2017.

  2. Kristjan Greenewald, Post-doc Dept. of Statistics University of Michigan, Thesis title: High dimensional covariance estimation for spatio-temporal processes, (.pdf). Dept of EECS Jan. 2017.

  3. Pin-Yu Chen , Research Staff Member, IBM Watson. Thesis title: Analysis and actions on graph data, (.pdf). Dept of EECS Sept. 2016.

  4. Kevin Moon, Post-doc Yale University. Thesis title: Nonparametric Estimation of Distributional Functionals and Applications, (.pdf). Dept of EECS Aug. 2016.

  5. Yu-Hui Chen . Software Engineer, Google, Mountain View. Thesis title: Multimodal Image Fusion and Its Applications, (.pdf). Dept of EECS Nov. 2015.

  6. Dae-Yon Jung . Research Scientist, Korean manpower establishment. Thesis title: Feature Selection and Non-Euclidean Dimensionality Reduction: Application to Electrocardiology, (.pdf). Dept of EECS June 2015.

  7. Hamed Firouzi. Vice President, Goldman Sachs, New York, NY. Thesis title "High Dimensional Correlation Networks and Their Applications." (.pdf). Dept of EECS Feb. 2015.

  8. Zhaoshi Meng. Senior researcher at Vicarious, Inc., San Francisco CA. Thesis title "Distributed learning, prediction and detection in probabilistic graphs." Dept of EECS Aug. 2014.

  9. Jeffrey Calder. Assistant Professor of Mathematics, University of Minnesota. Formerly, Morrey Assistant Professor of Mathematics at UC Berkeley. Thesis title "Hamilton-Jacobi equations for sorting and percolation problems," (.pdf) . Dept. Mathematics, AIM Program, Apr. 2014. (Co-chair Selim Esedoglu).

  10. Ko-Jen (Mark) Hsiao. Senior research scientist at Netflix, Inc. Thesis title "Combining Disparate Information for Machine Learning," (.pdf) . Dept. EECS Apr. 2014.

  11. Ted Tsiligkaridis . Technical Staff, MIT Lincoln Laboratory, Lexington MA. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing, (.pdf) . Dept. EECS Dec. 2013.

  12. Se-Un Park . Research staff, Schlumberger Research, Cambridge MA. Reconstruction, Classification, and Segmentation for Computational Microscopy, (.pdf) . Dept. EECS Aug. 2013.

  13. Paul Shearer , (shearer.pr at gmail.com). Data scientist at MassMutual Financial Group, Boston MA. Separable inverse problems, blind deconvolution, and stray light correction for extreme ultraviolet solar images, (.pdf) , Program in Applied and Interdisciplinary Mathematics, May 2013. (Co-chairs Anna Gilbert and Rich Frazin).

  14. Greg Newstadt (newstage37 at gmail.com). Software Engineer, Google, Inc, Pittsburgh PA. Adaptive sensing techniques for dynamic target tracking and detection with applications to synthetic aperture radars, (.pdf) , Dept. EECS, Jan 2013.

  15. Tzu-Yu Liu (joyliu at umich.edu). Senior machine learning scientist, Freenome, San Francisco. Thesis Statistical Learning for Sample-Limited High-dimensional Problems with Application to Biomedical Data (.pdf) , Dept. EECS, Jan 2013. (Co-chair Clayton Scott).

  16. Sung Jin Hwang (ssjh at umich.edu). Software Engineer at Google, Inc, Mountain View, CA. Thesis Geometric representation of high dimensional data , (.pdf) , Dept. EECS, Oct 2012. (Co-chair Steven Damelin).

  17. Kevin Xu (xukevin at umich.edu). Assistant Professor, University of Toledo, OH. Thesis ,Computational methods for learning and inference on dynamic networks (.pdf) , Dept. EECS, May 2012.

  18. Kumar Sricharan (kksreddy at umich.edu). Principal Data Scientist, Intuit. Thesis Neighborhood graphs for estimation of density functionals, (.pdf) , Dept. EECS, April 2012.

  19. Arnau Tibau Puig (arnau.tibau at gmail.com). Director of Data Science, LetGo, Barcelona. Thesis Learning from high-dimensional multivariate signals, (.pdf) , Dept. EECS, Jan 2012.

  20. Yilun Chen (alwyn.chen at gmail.com). Vice President - Engineering, Join Intelligent Solution. Shanghai China. Thesis Regularized Estimation of High-dimensional Covariance Matrices (.pdf) , Dept. EECS, March 2011.

  21. Yongsheng Huang (huangys at umich.edu). Software Engineer, DataBricks Boston. Thesis (Bioinformatics) Integrative Statistical Learning and Applications in Predicting Features of Diseases and Health (.pdf) , Bioinformatics Program, Jan 2011. (Co-chair Jay Hess).

  22. Patrick Harrington (plhjr at umich.edu). Director of Engineering, Walmart Labs, San Francisco CA. Thesis (Bioinformatics) Inverse problems in high dimensional stochastic systems under uncertainty, (.pdf) , Bioinformatics Program, Aug. 2010.

  23. Kevin Carter (kmcarter at umich.edu). Principal Program Manager Lead, Microsoft, Inc. Formerly Associate Group Leader, at MIT Lincoln Laboratory. Thesis (EECS) Dimensionality Reduction on Statistical Manifolds, (.pdf) , Dept. of EECS, Dec. 2008.

  24. Arvind Rao (ukarvind at umich.edu). Associate Professor, Dept. of Computational Biology and Bioinformatics, University of Michigan. Thesis (EECS/Bioinformatics) Prospective identification of long-range transcriptional regulatory regions via integrative genomics, (.pdf) . Bioinformatics Program and Dept. of EECS, July 2008. (Co-chairs David states and Douglas Engel).

  25. Eran Bashan (bashan at umich.edu). CEO, Director and Founder, Hygiea Inc, Ann Arbor MI. Thesis (EECS) Efficient resource allocation schemes for search, (.pdf) , Dept. EECS, May 2008. (Co-chair Jeffrey Fessler).

  26. Jay Marble , Senior EW Engineer at URS Federal Services, Bloomington IN. Thesis (EECS) Advances in surface penetrating technologies for imaging, detection, and classification, (.pdf) . Dept. EECS, Dec. 2007.

  27. Raghuram Rangarajan . Principal Engineer at Mimosa Networks, San Jose CA. Thesis (EECS) Resource constrained adaptive sensing, (.pdf) , Dept. EECS, Aug 2006.

  28. Derek Justice (djusticed at mmm.com). Research Staff at SAS Institute Inc, Raleigh NC. Thesis (EECS) "Inference methods for message endpoint localization in networks," (.pdf) , Dept. EECS, Aug. 2006.

  29. Dongxiao Zhu (dongxiaozhu at gmail.com). Associate Professor at Wayne State University, Detroit MI. Thesis (EECS) "Reconstructing Signaling Pathways from High Throughput Data" (.pdf) , Dept. EECS, May 2006.

  30. Doron Blatt (dblatt at DRWHoldings.com). Head of Algorithmic Trading at DRW Trading Group, Chicago IL. Thesis (EECS) "Performance Evaluation and Optimization for Inference Systems: Model Uncertainty, Distributed Implementation, and Active Sensing," (.pdf) . Dept. EECS, May 2006.

  31. Michael Ting (mting at umich.edu). Software developer at Criteo, Paris France. Thesis (EECS) "Signal Processing for Magnetic Resonance Force Microscopy," (.pdf) , Dept. EECS, May 2006.

  32. Neal Patwari (npatwari at ece.utah.edu). Professor at Univ. of Utah, Salt Lake City, UT. Thesis (EECS) Location estimation in sensor networks (.pdf) , Dept. EECS, Sept. 2005.

  33. Jose Costa . Quantitative Analyst at Teza Technologies, Chicago. Thesis (EECS) (.pdf) Random graphs for structure discovery in high dimensional data , Dept. EECS, Aug. 2005.

  34. Chris Kreucher (christopher.kreucher at umich.edu). Senior Systems Engineer at Integrity Applications Incorporated in Ann Arbor, MI. Thesis (EECS) (.pdf) An information-based approach to sensor resource allocation , Dept. EECS, Feb. 2005.

  35. Clyde Shih (mfshih at gmail.com). Senior Machine Learning Scientist, Apple, Inc. Thesis (EECS) (.pdf) Unicast Internet Tomography , Dept. EECS, Jan. 2005.

  36. Huzefa Neemuchwala . Senior Director for Data and Digital Solutions, Medtronic. Thesis (Biomedical Engineering) (.pdf) Entropic graphs for image registration , Dept. Biomedical Engineering, Jan. 2005.

  37. Tom Kragh (thomas.kragh at baesystems.com). Senior Principal Research Engineer, BAE Systems Inc, NH. Thesis (EECS) (.pdf) : Tradeoffs and limitations in statistically based image reconstruction problems , Dept. EECS, Sept. 2002.

  38. Jia Li (li4 at oakland.edu). Associate Professor at Oakland University, MI. PhD Thesis (Biomedical Engineering) (.pdf) : Three dimensional shape modeling: segmentation, reconstruction and registration , Dept. BME, Jan. 2002,

  39. Mahesh Godavarti (mgodavarti at ditechnetworks.com). Chief Scientist at Ditech Networks (mgodavarti at ditechnetworks.com). Thesis (EECS) (.pdf) : Antenna arrays in wireless communications, , Dept. EECS, July 2001.

  40. Riten (Robby) Gupta Research Staff TRW Inc, Los Angeles (Riten.Gupta at trw.com). Thesis (EECS) (.pdf) : Quantization Strategies for Low-Power Communications, , Dept. EECS, May 2001.

  41. Hyungsoo Kim, Research Engineer at Samsung Electronics, Korea (hns.kim at samsung.com, dearhs at hotmail.com). Thesis (EECS) (.pdf) : Adaptive target detection in radar imaging, , Dept. EECS, Jan. 2001.

  42. Bing Ma , Assistant Professor, University of Nevada, Las Vegas NV. (bingm at umich.edu). Thesis (EECS) (.pdf) : `Parametric and non-parametric approaches for multisensor data fusion Dept. EECS, Jan. 2001.

  43. Robinson Piramuthu , Principal Research Scientist and Director of Computer Vision at eBay Research Labs. Thesis (EECS) Robust fusion of MRI and ECT data, and acceleration of EM algorithm using proximal point approach , Dept. of EECS, May 2000.

  44. Anne Sauve , Data Scientist at Cisco. PhD Thesis System modeling, sampling, interprolation and iterative reconstruction for the 3D Compton camera, Dept. of EECS, Jan. 2000.

  45. Steven Titus, CTO, BIS Global, Raleigh NC. Formerly Director of Engineering at Echo360, Raleigh NC. Thesis (EECS) Improved penalized likelihood image reconstruction of anatomically correlated emission computed tomography data , Dept. EECS, Oct. 1996. (Co-chair Jeffrey Fessler).

  46. Ilan Sharfer, Senior Algorithms Engineer, ECI Telecom, Israel. PhD Thesis Recursive algorithms for digital communications using the discrete wavelet transform , Dept. EECS, Oct. 1996

  47. Mohammad Usman, Vice President of Engineering, Masimo Corporation, Irvine CA (usman92691 at gmail.com). Thesis (EECS) Biased and unbiased Cramer-Rao bounds: computational issues and applications (.pdf) , Dept. EECS, Aug. 1994.

  48. Ron Delap, Professor and Dean of Engineering and Engineering Technology, LeTourneau University, Longview TX. PhD Thesis ADEPT: Task Specific Adaptive Beamforming , Dept. EECS, May 1994.

  49. Nick Antoniadis, Professor of Computer Engineering, TEI of Epirus, Greece. Thesis (EECS) Time Delay Estimation for Inhomogeneous Poisson Processes in the Presence of Gaussian Noise , Dept. EECS, Oct. 1992.

  50. Bulent Baygun, Market Strategist and Quantitative Researcher at Laurion Capital. Formerly, Head of Interest Rate Strategy, BNP-Paribas, New York NY (bbaygun at bpa_su70.sbi.com). Thesis (EECS) Optimal Strategies and Tradeoffs for Joint Detection and Estimation , Dept. EECS, Oct. 1992.

  51. Nick Petrick, Deputy Director of the Division of Imaging and Applied Math and Director of the Image Analysis Laboratory, Food & Drug Administration (FDA). Thesis (EECS) Optimal Arrival Time Estimators for Electromagnetic Radiation Detectors , Dept. EECS, July 1992.

  52. John Gorman, Program Manager at DARPA.Thesis (EECS) Error Bounds in Constrained Estimation , Dept. EECS, June 1991.

  53. Ling Shao, Director of Imaging Physics, Philips Healthcare, CA. Thesis (Bioengineering Program) Mutual Information Optimization and Evaluation of Single Photon Computed Tomography , Bioengineering Program, Oct. 1989.

  54. Joong K. Kim, Professor at Sung Kyun Kwan University, Korea (jkkim at yurim.skku.ac.kr). Thesis (EECS) Time Delay Estimation with Nuisance Parameters: Performance Approximation and Coarse Acquisition , Dept. EECS, Oct. 1989.

Former MS Students who have done research in my group


Former BSE Students who have done research in my group


Other students who have done research in my group


Current (last 5 years) collaborators on publications and research grants


Other (more than 5 years ago) collaborators on publications and research grants


Links of Interest

Work in Progress

Click here for papers in preparation or submitted (Restricted Access - for information send me email at hero@eecs.umich.edu)

Visitor count:[an error occurred while processing this directive] accesses since July 2, 1996.

Contact Information:


Prof. Alfred O. Hero III ,
Dept. of Electrical Engineering and Computer Science
The University of Michigan
1301 Beal Avenue
Ann Arbor, MI 48109-2122
Tel. (313) 763-0564
FAX: (313) 763-8041
WWW: http://www.eecs.umich.edu/~hero/hero.html
email: