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