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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
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Where is Oregon?
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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
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TAU Parallel Performance System Large-scale, robust performance measurement 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
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Productivity from Open Integrated Tools (POINT) Testbed Apps ENZO NAMD NEMO3D
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Model Oriented Global Optimization (MOGO) Empirical performance data evaluated with respect to performance expectations at levels of abstraction
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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
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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?
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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
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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
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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
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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
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CIS Faculty Research Areas
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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/
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Salmon calcitonin is up to 50 times more effective than human calcitonin in treating osteoporosis Genomics and Bioinformatics Research in comparative genomics analyzes similarities and differences between orthologous genes ortholog = “same word” Zebrafish, salmon, and other teleost fish often have two orthologs of a single human gene UO software to scan human chromosomes, identify co-orthologs in zebrafish Studying co-orthologs improves our ability to understand functions of genes, potential medical applications
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Photo by Rick Edwards, AMNH, 2006 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
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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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