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Allen D. Malony, Professor  University of Illinois, Urbana-Champaign  Fulbright Research Scholar  The Netherlands  Austria  Alexander von Humboldt.

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Presentation on theme: "Allen D. Malony, Professor  University of Illinois, Urbana-Champaign  Fulbright Research Scholar  The Netherlands  Austria  Alexander von Humboldt."— Presentation transcript:

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 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

5 Productivity from Open Integrated Tools (POINT) Testbed Apps ENZO NAMD NEMO3D

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 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

16 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

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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