John Conery - US grants

An important new phase in the study of parallel execution of logic programs is the development of efficient implementation techniques. This project focuses on the design of a virtual machine for the AND/OR Process Model, an abstract execution model in which user goals are solved by independent objects communicating via messages. This work can be characterized as an attempt to do for the AND/OR Process Model what the Warren Abstract Machine (WAM) did for Prolog: achieve significantly faster execution through compilation to a virtual machine tailored to the model of computation. Important aspects of a virtual machine for logic programs are the representation of binding environments and instructions that control the order in which goals are solved. The proposed machine will build what they call "closed environments," environments that have little or no interaction,so that they can be built and moved anywhere in a multiprocessor system. Minimal interaction between environments is important if the system will be implemented on a non-shared memory multiprocessor. The instruction set of the virtual machine will be tailored to operations of a new method for controlling AND-parallel processing.

9418762 Segall This Industry/University Cooperative Research Center (I/UCRC) is being funded as a multi-site research Center. Purdue University and the University of Florida is adding the University of Oregon (including Oregon State University, the Oregon Graduate Institute of Science & Technology and Portland State University) as research sites in the I/UCRC for Software Engineering (SERC). Seven companies will join serc inconjunction with the University of Oregon becoming an additional research site in the Center. The Center's research agenda will be increased by research projects on "Time Insensitive Binary to Binary Translation; "Entropy-Based Measure of Software Complexity and Its Application to Software Testing", "Visual Programming Languages", "Human Computer Interaction", and "Software Engineering Tools for the Construction of Network Applications." The researchers of involved with the Center are qualified to perform the research projects. The Center has been coordinated with Dr. Bruce Barnes, Program Director of the Computer Computation Research Program at NSF. The Program Manager recommends the University of Oregon be awarded $50,000 for the first year of a five year continuing award. Near the end of each 12-month period, the Program Manager and/or the Division Director of the Engineering Education Centers Division will review the progress of the Center on a number of renewal criteria, including the following: 1) the extent to which the industry/university interaction is developing; 2) the extent to which the support base is developing; and 3) the extent to which a robust research program is developing. If the review is satisfactory, the Program Manager will recommends support for the next period of this continuing award.

9413532 Conery This award is for the purchase of a parallel computer to do research in Problem Specific Programming Environments (PSPE). These environments will be built to use domain specific information in order to aid the scientist developing application programs for that area. The three PSPEs to be experimented with on this parallel computer are: Computation of the electronic structure of superconductors; simulation of mutations over generations; and constraint-based computations in molecular evolution. All of these domains are computation intensive needing the power of a parallel computer. ***

Future progress in cognitive neuroscience research will rely increasingly on the application of systems for high-performance computation and high-volume data management to address the challenges of integrated neuroimaging, multi-modality sensor fusion, and cognitive modeling.
With a Major Research Instrumentation award from the National Science Foundation, the University of Oregon will establish the Integrated COgnitive Neuroscience, Informatics, and Computation (ICONIC) Grid, composed of parallel computing clusters, large-scale data servers, workstations, and interactive visualization devices. Connected by a high-bandwidth campus network linking the Department of Psychology, the Center for Neuroimaging , the Neuroinformatics Center, the Department of Computer and Information Science, and the Computational Science Institute, the ICONIC Grid will enhance Oregon's excellence in cognitive neuroscience with needed computing power to solve neuroimaging problems of tissue/feature segmentation, dense-array EEG source localization, multi-modal MRI integration, and functional components analysis. The ICONIC Grid will be organized as a distributed computing environment to promote grid-style collaboration among cognitive neuroscience research groups. Computer science research in high-performance parallel and distributed computing, scientific databases, informatics, and interactive visualization will enhance the ICONIC Grid for highly productive use as a computational science tool.

The interchange between cognitive neuroscience and computational science is now important at both theoretical and empirical levels. For several decades, cognitive psychology has drawn from concepts of cybernetics and information processing in the development of models of human mental function. However, it is in the integration of psychological with neural evidence that the methodological demands for computational advances have become particularly intense. Many investigators in cognitive neuroscience now recognize the limitations of individual brain imaging methods, such as in the temporal or spatial resolution, or practical implementation of the technology. The result is an increasing demand for integrated imaging and analysis, in which convergent methods are brought to bear on a particular issue of brain mechanisms.

The University of Oregon began the decade with a bold Brain, Biology, and Machine Initiative (BBMI) to promote interdisciplinary research between neuroscience, cognitive science, molecular biology, genomics, and computational science. The establishment of the Center for Neuroimaging , which houses a new Siemans Allegra 3-Tesla fMRI machine, and the Neuroinformatics Center, were Oregon's first steps towards integrative cognitive neuroscience. The ICONIC Grid is the next critical piece of the puzzle providing an essential resource to further advancements in cognitive neuroscience research, collaboration, education, and outreach.

The broader impact of the ICONIC Grid will be important for the University's educational goals, for minority recruitment and retention, and for extending advances in computation to medical advances in society. With on-campus access to both advanced imaging facilities and the computational and visualization infrastructure that processes and presents the experimental data, students in Psychology will be exposed to a state-of-the-art problem-solving environment for cognitive neursocience education. New Psychology curricula are planned for providing students training in the use of such tools. Similarly, the CIS department's academic objectives in parallel and distributed computing, computational science, networking, human-computer interaction, and visualization will benefit greatly from hands-on access to parallel cluster and distributed grid technology.

Phylogenies -- evolutionary trees that represent the historical relationships among species -- provide the framework for comparative analysis in all fields of biology. Most phylogenies are now inferred from DNA or protein sequence data using methods that assume the evolutionary process is largely homogeneous. In reality, however, evolutionary dynamics often differ among sequence sites and among lineages. Our preliminary data indicate that several kinds of heterogeneity can cause current methods to infer the wrong phylogeny. The goal of this proposal is to develop, implement, and validate a new family of mixed-model phylogenetic methods that incorporate evolutionary heterogeneity in a maximum likelihood framework. The accuracy of our method versus current techniques will be evaluated with experiments using both simulated and empirical data sets. We will distribute user-friendly software to the scientific community that implements our method and provides a high-throughput platform for simulating and analyzing heterogeneous sequence data.
This project will accelerate the scientific community's success in reconstructing the Tree of Life and improve our ability to interpret genomic, developmental, and physiological data in a comparative framework. More reliable phylogenies are beneficial to society because an understanding of evolutionary relationships is crucial for characterizing biodiversity and developing strategies to preserve it. Sound phylogenetic knowledge is also central to understanding the evolutionary processes that affect agriculture, ecosystem function, and infectious disease. Our software will also provide scientists a tool for high-throughput phylogenetic experimentation and data analysis, a key goal as whole-genome sequence data become available. This project will also provide graduate and undergraduate education and research training in computer science and biology, an important need as biology becomes increasingly information-driven in the 21st Century.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Building on the success of a previousMRI-funded project, an interdisciplinary group of computer scientists, psychologists, biologists, chemists, and physicists at the University of Oregon is acquiring a large-scale computational resource, the Applied Computational Instrument for Scientific Synthesis (ACISS), to support continued cutting-edge scientific research in these areas. The ACISS hardware will consist of general purpose multicore computing nodes, high performance computing nodes augmented with GPGPU acceleration, a 400TB storage system, high-bandwidth networking infrastructure and additional computing resources that will be incorporated into an existing visualization lab in the Department of Computer and Information Science. A key part of the proposed infrastructure is the unique opportunity to manage ACISS as a computational science cloud.

The ACISS infrastructure will allow an expanded the scope for the current projects in the areas of software tools for performance measurement, programming environments and languages for describing and executing complex simulations and scientific work flows, new algorithms for multiple sequence alignment and phylogenetic inference and undertake new projects in support of the domain sciences. Research projects that will benefit include: a) modeling neural networks in C. elegans to better understand the neural mechanisms responsible for chemotaxis and klinotaxis, and investigation of the evolution of genes involved in development and their role in speciation and phenotypic variation; b) development of neuroinformatic techniques used in brain imaging and analysis, integrating structural information from fMRI and other sources with EEG data; c) molecular modeling research, including the definition of new techniques for meso-scale modeling and applying computational methods to understand phase transitions and nitrogen fixation; d) astrophysical simulations of turbulent plasma flows that influence the early stages of planet formation.

The ACISS infrastructure will provide the computational resources necessary for future multidisciplinary research. ACISS will establish a novel paradigm for computational science research and practice. The experience gained in early adoption of the ACISS cloud computing technologies will allow us to more rapidly apply this knowledge to create new scientific work flows, more productive research collaborations, and enhanced multidisciplinary education programs. Farther reaching, ACISS can be seen as a model for translational computational science, in which ACISS-based services function as cyber-incubators where new work flows for scientific research are prototyped.

BlONINFORMATICS CORE AND HUB OF INNOVATION Mission The Bioinformatics Core Facility will provide scientific, technical and administrative support for the computational needs of META CSB research. The Bioinformatics Core Facility will provide expertise in mathematical and statistical methods and in software applications, will help individual users and lab groups to configure and launch virtual machines in a cloud computing environment, and will help train the next generation of bioinformaticians.