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Information & Simulation Information & Simulation Faculty Faculty Ricardo Gutierrez Ricardo Gutierrez Frank Shipman Frank Shipman Nancy Amato Nancy Amato James Caverlee James Caverlee Jinxiang Chai Jinxiang Chai Rick Furuta Rick Furuta Tracy Hammond Tracy Hammond Andruid Kerne Andruid Kerne John Keyser John Keyser Scott Schaefer Scott Schaefer Research Projects Research Projects Real-time swarm algorithms for crowd Real-time swarm algorithms for crowd control control Simplifying physically-based simulations Simplifying physically-based simulations Adaptive information delivery Adaptive information delivery Active chemical sensing Active chemical sensing Sensing Storage Connectivi ty Computatio n Visualizat ion Interactio n Modeling Collaborat ion Data intensive technologi es Human centere d computi ng Core expertise @ TAMU Information retrieval Digital humanities User interfaces E-collaboration Cognitive science Computer graphics Pattern recognition Image processing Algorithm design Web 2.0 Sensor networks Secure computing Human Data

Information & Simulation Faculty Ricardo GutierrezRicardo Gutierrez Frank ShipmanFrank Shipman Nancy AmatoNancy Amato James CaverleeJames Caverlee Jinxiang

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Information & SimulationInformation & Simulation

FacultyFaculty• Ricardo GutierrezRicardo Gutierrez• Frank ShipmanFrank Shipman• Nancy AmatoNancy Amato• James CaverleeJames Caverlee• Jinxiang ChaiJinxiang Chai• Rick FurutaRick Furuta• Tracy HammondTracy Hammond• Andruid KerneAndruid Kerne• John KeyserJohn Keyser• Scott SchaeferScott Schaefer

Research ProjectsResearch Projects• Real-time swarm algorithms for crowd controlReal-time swarm algorithms for crowd control• Simplifying physically-based simulationsSimplifying physically-based simulations• Adaptive information deliveryAdaptive information delivery• Active chemical sensingActive chemical sensing

Sensing

Storage

Connectivity

Computation

Visualization

Interaction

Modeling

Collaboration

Dataintensive

technologies

Humancentered computing

Core expertise @ TAMU

Information retrievalDigital humanities

User interfacesE-collaboration

Cognitive scienceComputer graphics

Pattern recognitionImage processingAlgorithm designWeb 2.0Sensor networksSecure computing

HumanData

Multi-Application Interest Modeling Multi-Application Interest Modeling for Information Triagefor Information Triage

Work on interest modeling assumes one Work on interest modeling assumes one application or one interest but many applications application or one interest but many applications are involved in information triageare involved in information triage

When applications do share a user model, it is When applications do share a user model, it is with regard to a well-known domain modelwith regard to a well-known domain model

We are generating multi-application interest We are generating multi-application interest models that perform better than single-models that perform better than single-application modelsapplication models(IUI 2006).(IUI 2006).

Need to move theNeed to move thetheory to practice.theory to practice.

Multi-Application Interest Modeling Multi-Application Interest Modeling for Information Triage (2)for Information Triage (2)

Where we are:Where we are:

Multi-Application Interest Modeling Multi-Application Interest Modeling for Information Triage (3)for Information Triage (3)

On-going questions:On-going questions:• How to merge interests across applications?How to merge interests across applications?

Multiple possibilities to explore – clustering based on Multiple possibilities to explore – clustering based on text analysis, clustering based on temporality of user text analysis, clustering based on temporality of user actions, bothactions, both

• How to support human feedback on interest How to support human feedback on interest models?models?

Should users intervene when the model is incorrect? Should users intervene when the model is incorrect? How?How?

• Do visualizations lead to more time on valuable Do visualizations lead to more time on valuable documents and better decisions?documents and better decisions?

Adaptation of Information Delivery Adaptation of Information Delivery based on User’s Mental Statebased on User’s Mental State

Requires:Requires:• Real-time recognition of mental load and Real-time recognition of mental load and

attentionattention• Information delivery techniques to match Information delivery techniques to match

mental statemental state

Experimental ResultsExperimental Results

2

3

4

5

6

02:53 05:46 08:38 11:31 14:24 17:17 20:10 23:02 25:55

Ski

n c

on

du

ctan

ce (

S)

Time (min:sec)

Stress test (game)Relaxation Relaxation

Sensor development in houseSensor development in house Detection of stress in game playingDetection of stress in game playing

Making Sense of Physiological DataMaking Sense of Physiological DataD

ay

Time(Hour)

80

90

100

110

120

130

140

8 10 12 14 16 18 20

Polar HRM strap

Embeddedsystem Battery inside

Respiration sensor

Respiration transmitter

Flash memory

Adaptive Support for Analyzing Adaptive Support for Analyzing Sensor DataSensor Data

A mixed-initiative strategyA mixed-initiative strategy• User preferences taken as model constraintsUser preferences taken as model constraints• Underlying pattern recognition engine suggests Underlying pattern recognition engine suggests

potential matchespotential matches