allen d. malony, professor university of illinois, urbana-champaign fulbright research scholar ...

Allen D. Malony, Professor

University of Illinois, Urbana-Champaign Fulbright Research Scholar

The Netherlands Austria

Alexander von Humboldt Research Award National Science Foundation Young Investigator Research interests

Parallel performance analysis, high-performance computing, scalable parallel software and tools

Computational science Neuroinformatics

Director, Neuroinformatics Center

Where is Oregon?

Parallel Performance Tools Research Scalable parallel performance analysis Optimization through performance engineering process

Understand performance complexity and inefficiencies Tune application to run optimally at scale

Design and develop parallel performance technology Integrate performance tools with parallel program

development and execution environments Use tools to optimize parallel applications Research funded by NSF and DOE

NSF POINT project DOE MOGO project

TAU Parallel Performance System

Large-scale, robust performancemeasurement and analysis Robust and mature Broad use in NSF, DOE, DoD

Performance database TAU PerfDMF PERI DB reference platform

Performance data mining TAU PerfExplorer

multi-experiment data mining analysis scripting, inference

http://tau.uoregon.edu

Productivity from Open Integrated Tools (POINT)

Testbed AppsENZONAMDNEMO3D

Model Oriented Global Optimization (MOGO)

Empirical performance data evaluated with respect to performance expectations at levels of abstraction

Performance Refactoring (PRIMA) (UO, Juelich)

Integration of instrumentation and measurement Core infrastructure

Focus on TAU and Scalasca University of Oregon, Research Centre Juelich Refactor instrumentation, measurement, and analysis Build next-generation tools on new common foundation

Extend to involve the SILC project Juelich, TU Dresden, TU Munich

Neuroscience and Neuroinformatics

Understanding of brain organization and function Integration of information across many levels

Physical and functional Gene to behavior Microscopic to macroscopic scales

Challenges in brain observation and modeling Structure and organization (imaging) Operational and functional dynamics (temporal/spatial) Physical, functional, and cognitive operation (models)

Challenges in interpreting brain states and dynamics How to create and maintain of integrated views of the

brain for both scientific and clinical purposes?

Human Brain Dynamics Analysis Problem

Understand functional operation of the human cortex Dynamic cortex activation Link to sensory/motor and cognitive activities Multiple experimental paradigms and methods Multiple research, clinical, and medical domains

Need for coupled/integrated modeling and analysis Multi-modal observation (electromagnetic, MR, optical) Physical brain models and theoretical cognitive models

Need for robust tools Complex analysis of large multi-model data Reasoning and interpretation of brain behavior

Problem solving environment for brain analysis

NeuroInformatics Center (NIC) at UO Application of computational science methods to

human neuroscience problems Tools to help understand dynamic brain function Tools to help diagnosis brain-related disorders HPC simulation, large-scale data analysis, visualization

Integration of neuroimaging methods and technology Need for coupled modeling (EEG/ERP, MR analysis) Apply advanced statistical signal analysis (PCA, ICA) Develop computational brain models (FDM, FEM) Build source localization models (dipole, linear inverse) Optimize temporal and spatial resolution

Internet-based capabilities for brain analysis services, data archiving, and data mining

Observing Dynamic Brain Function

Brain activity occurs in cortex Observing brain activity requires

high temporal and spatial resolution Cortex activity generates scalp EEG EEG data (dense-array, 256 channels)

High temporal (1msec) / poor spatial resolution (2D) MR imaging (fMRI, PET)

Good spatial (3D) / poor temporal resolution (~1.0 sec) Want both high temporal and spatial resolution Need to solve source localization problem!!!

Find cortical sources for measured EEG signals

Computational Head Models Source localization requires modeling Goal:

Full physics modeling of human head electromagnetics

Step 1: Head tissue segmentation Obtain accurate tissue geometries

Step 2: Numerical forward solution 3D numerical head model Map current sources to scalp potential

Step 3: Conductivity modeling Inject currents and measure response Find accurate tissue conductivities

Step 4: Source optimization

CIS Faculty Research Areas

Assistive Technology and Brain Injury Research Technology for people with cognitive impairments

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Multi-disciplinary research Prof. Steve Fickas, CIS

Wearable Computing Lab Prof. McKay Sohlberg, Education NSF grants

CogLink, Inc. Startup company

http://www.go-outside.org/

Salmon calcitonin is up to 50 times more effective than human calcitoninin treating osteoporosis

Genomics and Bioinformatics Research in comparative genomics analyzes similarities

and differences between orthologous genes ortholog = “same word”

Zebrafish, salmon, and other teleostfish often have two orthologs of asingle human gene

UO software to scanhuman chromosomes, identifyco-orthologs in zebrafish

Studying co-orthologsimproves our ability tounderstand functions of genes,potential medical applications

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Computational Paleontology

Dinosaur 3D modeling DinoMorph modeling engine Paleontology-based Reconstructs true dimensions,

poses, flexibility, movements Dinosaur species Other domestic, wild, and fanciful animals

Kaibridge, Inc. Startup company Interactive museum exhibits Dinosaur educational software BBC online mystery game

Computer Science Visualization Laboratory Support interdisciplinary computer science

Informatics Computational science

Resource development Phase 1 (complete)

NSF MRI grant ($1M) ICONIC HPC Grid

Phase II Visualization Lab ($100K)

rear projection» 3D stereo and 2x2 tiled

3x4 tiled 24” LCD display

Phase III …