Collaborations

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(Doug Cochran (ASU) and Al Hero (UM))
(John Fisher (MIT) and Al Hero (UM))
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==John Fisher (MIT) and Al Hero (UM)==
 
==John Fisher (MIT) and Al Hero (UM)==
  
 
 
*Student collaborators: Georgios Papachristoudis (MIT), Greg Newstadt (UM), Hamed Firouzi (UM).
 
*Student collaborators: Georgios Papachristoudis (MIT), Greg Newstadt (UM), Hamed Firouzi (UM).
  
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*Research goal: Fisher and Papachristoudis have obtained distributed sensor planning strategies and characterized value of information for state prediction in the context of Gaussian observations and known topology.  
 
*Research goal: Fisher and Papachristoudis have obtained distributed sensor planning strategies and characterized value of information for state prediction in the context of Gaussian observations and known topology.  
 
The goal is to develop an analagous distrbiuted sensor planning framework for topology discovery using Poisson asymptotics developed by Firouzi and Hero.
 
The goal is to develop an analagous distrbiuted sensor planning framework for topology discovery using Poisson asymptotics developed by Firouzi and Hero.
 
  
 
==Doug Cochran (ASU) and Al Hero (UM)==
 
==Doug Cochran (ASU) and Al Hero (UM)==

Revision as of 16:36, August 25, 2013

Contents

Al Hero (UM) and Jon How (MIT)

value-driven mission planning applications dveloped in How's laboratory. A paper on this work is currently in preparation for submission to ICASSP.

John Fisher (MIT) and Al Hero (UM)

The goal is to develop an analagous distrbiuted sensor planning framework for topology discovery using Poisson asymptotics developed by Firouzi and Hero.

Doug Cochran (ASU) and Al Hero (UM)

Independently, Hero has been pursuing an information geometric framework for static classification, clustering and estimation of data that comes in the form of probability distributions. The overall goal is to combine these two perspectives.

Mike Jordan (UC Berkeley) and Al Hero (UM)

have studied the asymptotics of Pareto ranking, in particular for the problem of multicriterion indexing and retrieval over image databases. Our objective is to explore tangencies between these two approaches to asymptotic ranking theory and to translate them to human-in-the-loop applications like indexing and retrieval. The collaboration has impacted a publication on Pareto ranking that has been submitted by Calder and Hero to a math journal.

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