Collaborations

From Voimuri Wiki
Collaborations
Jump to: navigation, search
(John Fisher (MIT) and Al Hero (UM))
(Doug Cochran (ASU) and Al Hero (UM))
Line 20: Line 20:
  
 
==Doug Cochran (ASU) and Al Hero (UM)==
 
==Doug Cochran (ASU) and Al Hero (UM)==
 
 
*Student collaborators: None yet.
 
*Student collaborators: None yet.
  
Line 28: Line 27:
 
Independently, Hero has been pursuing an information geometric framework for static classification, clustering and estimation of  
 
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.
 
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)==
 +
 +
*Student collaborators: John Duchi (UC Berkeley) and Jeff Calder (UM).
 +
 +
*Interactions: In Feb 2013 John Duchi spent 3 days at UM. In May 2013 Jeff Calder spent 4 days at UC Berkeley.
 +
 +
*Research goal: Duchi and Jordan have studied the asymptotics of ranking algorithms, in particular in the context of inferring partially ordered rankings of human preferences.  Calder and Hero
 +
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.

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.

Personal tools
Namespaces
Variants
Actions
Navigation
Toolbox
EECS @ UM
Tools