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Adaptive Exploitation of Non-Commutative Multimodal Information Structure


  1. Prof. Yoram Bresler, University of Illinois Urbana Champaign
  1. Prof. Todd Coleman, University of California San Diego
  2. Prof. Alfred O. Hero, University of Michigan
  3. Prof. Raj R. Nadakuditi, University of Michigan
  4. Prof. Robert D. Nowak, University of Wisconsin
  5. Prof. Maxim Raginsky, University of Illinois Urbana Champaign
  6. Prof. Silvio Savarese, Stanford University
  7. Prof. Vahid Tarokh, Duke University

A complete roster and web page links may be found at People.

Executive Summary

Non-commutativity is intrinsic to emerging complex sensing and processing systems. The performance of a distributed sensor network depends on the ordering or partial ordering of the sequence of information sharing actions taken across the network. Multiuser brain-computer interfaces provide directed channels of neural communication from human to machine and between humans. Understanding human activities from video requires differentiating between different ordered or partially ordered sequences of gestures and actions. Hierarchical models for signals, images, and videos, such as convolutional networks and HDP representations, are directed graphs that may link multiple layers of data types including real-valued scalars, vectors, and matrices; categorical and ordinal variables (ranks); graphs and hypergraphs; and symbolic categories with complicated spatial and temporal co-dependencies. Extracting useful information from such complex data sources is always constrained by limited resources, such as energy or computation power, limited sensing opportunity (i.e., transient visibility), and limited transmission capacity. The powerful toolboxes of convex optimization, information theory, and statistical machine learning have enabled breakthroughs in computer vision, speech recognition, and database indexing, for which algorithms can be designed offine from large amounts of training data. A grand challenge is to move beyond offine static designs to online, sequential, and adaptive designs of data collection and analysis. To do so, we must grapple with the increased design complexity associated with adaptive procedures. Principal among the reasons for increased complexity is non-commutativity of the sequence of sensing actions: the optimal action depends critically on the result of previous actions. The objective of our proposed effort is to address this challenge by developing new tools for the next generation of adaptive online sensing and processing systems.


publications: publications resulting from this program.

Annual Review

The 4th annual review of the MURI will take place on Oct 21 2020.

The meeting will be virtual and will take place in the Zoom room

The high level agenda is below. All times are EDT.

10:00 - Introduction (Bresler, Hero and Krim).

10:05 - MURI overview of accomplishments (Al and Yoram)

10:30 - Technical talks

12:00 - East coast lunch break & Poster Session (in Zoom breakout rooms)

12:30 - Technical talks

14:00 - West coast lunch break & Poster Session (in Zoom breakout rooms)

14:30 - Technical talks

16:00 - Wrapup (Al and Yoram)

16:15 - Govt Caucus

17:30 - Adjourn

Detailed Technical Agenda

10 AM: Overview of MURI accomplishments (Al and Yoram)

Thrust 1: Non-commutative information: representation of uncertainty

10:30 AM: Overview of area (Vahid Tarokh)


Thrust 2: Fusion: multimodal learning in high dimensions

12:30 AM: Overview of area (Todd Coleman)


Thrust 3: Non-commutative information gathering and decision making

2:30 AM: Overview of area (Rob Nowak and Silvio Savarese)


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