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Allen D. Malony, Professor

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

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Allen D. Malony, Professor

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  1. 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

  2. Where is Oregon?

  3. 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

  4. 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

  5. Productivity from Open Integrated Tools (POINT) Testbed AppsENZO NAMDNEMO3D

  6. Model Oriented Global Optimization (MOGO) • Empirical performance data evaluated with respect to performance expectations at levels of abstraction

  7. 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

  8. 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?

  9. 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

  10. 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

  11. 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

  12. 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

  13. CIS Faculty Research Areas

  14. Assistive Technology and Brain Injury Research • Technology for people with cognitive impairments • Navigation • Email • Trimet • 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/

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

  16. 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 Photo by Rick Edwards, AMNH, 2006

  17. 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 …

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