Shantenu Jha, Ph.D. - US grants

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Rationale and Specific Aims: According to the NIH Biomedical Information Science and Technology Initiative Consortium (NIH-BISTI) "Bioinformatics is research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data" (http://www.bisti.nih.gov). The rapid growth of biological information has necessitated the development and implementation of advanced biocomputing and bioinformatics resources and capabilities, which are currently deemed essential components for biomedical research. Typically, these resources are optimally used when an efficient user-friendly and shared networking system is in place. The elucidation of the human and other genomes has provided an immense amount of data that must be analyzed by computationally intensive methods launching the era of genomics. Similarly, understanding the proteins coded by individual genomes, their regulated expression, structure, function and interaction (proteomics), will require the development of new hardware and software beyond current capabilities. Some important areas that will greatly impact biomedical research are: advanced visualization;data mining;distributed and shared analysis and storage of large data sets;dynamic simulation and modeling of biological processes. The Bioinformatics/Biocomputing Core (BBC) focuses on providing the necessary networking capabilities to support biomedical research projects distributed throughout the state of Louisiana. It is envisioned that the initial BBC's efforts in biocomputing/bioinformatics will demonstrate competitive research in Louisiana, which will grow as the network expands and faculty are added. A primary goal of the BBC is to provide access to world-class computer resources and computing, and communication support for the entire INBRE. Of particular importance is providing a supportive environment for the specific research projects associated with this core, as well as projects in the MCBC. In addition, the BBC will develop and distribute new biocomputational capabilities to enable competitive research by all INBRE institutions. The BBC includes an initial cadre of pilot projects aimed at achieving this goal. It is anticipated that additional biocomputational projects will be developed during the INBRE as new faculty are hired and the network grows. The BBC will be built upon the existing resources of the Center for Computation and Technology (CCT, see description below) on the LSU campus, the LBRN Phase I resources, including those at the LSU Eye Center (LSUEC), and those being built under the auspices of LONI. The BBC will foster enhanced training of undergraduate and graduate students, as well as faculty, by initiating novel methods of delivering expertise across campus boundaries. This training and interaction will provide: networked courses;access to seminars and workshops;remote access to research equipment;person-to-person teleconferencing;other forms of distance learning, communication and collaboration. We consider these elements as absolutely essential to the enhancement of biomedical research since it promotes the development of state-wide mentoring at all educational levels. The specific aims of the BBC are: Specific Aim I: To support and further develop the existing Access-Grid and high speed networking infrastructure. The goal is to facilitate interactive knowledge and data exchange among INBRE investigators, further enhancing interdisciplinary collaboration. Specific Aim II: To support an initial cadre of investigators who will develop research projects that utilize advanced biocomputation methodologies. Specific Aim III: To provide communication and network support for the development of training and research programs at PUI campuses, including undergraduate, graduate, post-doctoral fellows, and faculty. The intended goal is to provide state-of-the-art resources, mentoring, and research experiences, beyond the boundaries of home departments and institutions.

Award 0936102
Funding US Students to Attend the International Summer School on Grid Computing
PI: Jha, Shantenu


This project will enable ten students from the US to attend the International Summer School on Grid Computing (ISSGC09) in Nice, France in July 2009. ISSGC offers a comprehensive training program in distributed computing consisting of two weeks of lectures and hands-on exercises. An international team of instructors exposes the students to a broad range of concepts and technologies. Students from a wide spectrum of disciplines explore the different aspects of Grid computing through team projects and panel discussions. The students will be encouraged to continue their engagement with follow up mentoring by members of the Summer School in the US and others for Open Science Grid (OSG). In past years, the careful selection process and contact with the local supervisors and mentors of the students has yielded several who have continued on with using distributed computing for their work and research. Many students continue their research and learning beyond the conference, including participation in on-line learning opportunities provided by OSG.

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

This project is to renovate the research component of Louisiana State University's (LSU's) campus network. The network includes a bypass feature which allows traffic between researchers and pre-cleared network addresses to bypass the institution's primary firewall and traffic-shaping devices. Such traffic flows between the border router and the campus network core through a router dedicated to bypass traffic, rather than through the campus firewall and traffic shaper. This architecture was created by LSU to address two primary concerns associated with the older network architecture, lack of adequate bandwidth over the 'last mile' to researchers with equipment generating or consuming large amounts of data in facilities in distributed locations, and the hindrance to high bandwidth, low-latency data flows imposed by various network security and integrity controls. The renovation involves upgrading multiple levels within the campus network. The goal is to upgrade the campus network in such a way that when, subsequently, a new high-bandwidth, low-latency connection is required at some location on campus, this can be deployed rapidly and inexpensively.

The upgraded network will enhance research in many areas, including coastal modeling, the visualization of coastal models, computational biology, relativistic astrophysics, computer science, advanced networking research, and nontraditional areas of computational study such as music, theatre, and the visual arts.

In addition to providing infrastructure for research, the upgraded network will also have an impact outside of science and engineering, on research related to technologies for use in the arts and digital media. The infrastructure will support research training since many of the people using the network in research activities will be graduate and undergraduate students.

Building on the successful selection and funding of two UK e-Science research themes related to data-intensive computing (3DPAS and DIR; to be hosted by the e-Science Institute, Edinburgh), we will couple US Researchers to the intellectual advances that these year long research themes (2010-11) will engender. This award will support participation by ten US-based researchers in three workshops, as part of the eSI Research themes on data-intensive research to be held in Edinburgh, UK.

Workshops associated with the eSI Research Theme will be organized along the following three tracks: (i) Abstractions for distributed and dynamic data-intensive applications, (ii) Understanding the issues to enable Relational technology to be widely and effectively used in the support of data-intensive methods at extreme scales, and (iii) Algorithms, Software and Cyberinfrastructure for data-intensive research

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. We aim to investigate the complicated molecular mechanism of a SAM-I riboswitch RNA element with all-atom Molecular Dynamics (MD) simulations. The riboswitch RNA is a regulatory RNA element residing on 5'-untransrated region (UTR) of a target mRNA and regulate relevant genes in transcriptional or translational levels. Riboswitch RNAs are cis-acting regulatory RNA that comprises the aptamer domain and the expression domain. The complicated interplay between these two domains regulate a downstream gene in a mRNA, but the detailed molecular mechanisms remain elusive that are potentially affected by metabolite binding, roles of Mg2+, competing secondary structure elements, and dynamical distribution of secondary structures before binding a SAM. Therefore, examining the folded state stability as well as unfolding pathways with long time trajectories is beneficial for understanding molecular level information, which is further utilized to rationalize the molecular mechanism of the SAM-I riboswitch.

Large scale, realistic simulations of complex biological and chemical phenomena at the atomic level of resolution level present a grand challenge for molecular simulation. Effective sampling of conformational space may require large numbers of computationally intensive simulations which are coupled to one another. Enhanced conformational sampling algorithms based on the application of biasing forces and replica exchange generalized ensembles, whereby a large number of replicas of the system are simulated in parallel, among the most powerful methods to study a wide variety of physicochemical processes. Uncoupled methods currently in use are very slowly convergent and often of dubious reliability as the independent simulations are not in equilibrium with one another. The key aspect of replica exchange (RE) algorithms is that replicas of the system periodically exchange their state parameters allowing them to rapidly traverse conformational space and to enhance equilibration. Current synchronous formulations of the RE method in wide use, however, are highly limited in terms of scalability and control when many exchanging replicas are involved. This limitation precludes the use of RE simulations to new application areas that require the calculation of high-dimensional free energy surfaces, and necessitate the dynamic control of 103-104 replicas as the landscape evolves. This project involves the development of a robust adaptive force biasing procedure coupled with an asynchronous replica exchange method. The research team is developing a novel infrastructure, the Replica Exchange Frame work (REFW) to enable the execution of very large scale RE simulations on a broad range of production computational resources, including but not limited to NSF TeraGrid (and its successor XD), cloud and campus-level cluster environments, as well as the forthcoming Blue Waters supercomputer. The REFW is being applied to applications that present multiple levels of complexity, such as coupled ligand binding, conformational change and catalysis in the glmS ribozyme/riboswitch that were hitherto not possible.

The cyberinfrastructure created by this research team enables realistic simulations of important biological processes that have relevance in many areas of biology, biophysics, medicinal chemistry, and biophysics with the potential to impact human health. Additionally, the REWF may be applied in many other scientific areas that increasingly rely on realistic simulation including catalysis, earthquake prediction and petroleum engineering. The project is also training the next generation of computational scientists to apply these methods to solve high-impact interdisciplinary research problems. The resulting technology and training enables the study of a host of new reactive chemical problems of unprecedented complexity, and greatly facilitates innovation and discovery through advanced computation.

This is a Cyber-Enabled Discovery and Innovation Program award and is co-funded by the Division of Chemistry and the Division of Physics in the Directorate for Mathematical and Physical Sciences.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Specific Aim I: To support the research efforts and activities of all LBRN project investigators by providing access to national, state and local high-performance computing, computational resources, expertise and support. The two sub-cores described above will work together to meet the goals of this specific aim. 1. Cyberinfrastructure and Computational Services Sub-Core 2. Classical Statistical and Data Mining Consulting Sub-Core Specific Aim II: To provide training and educational support for Bioinformatics, Computational Biology and Biostatistics.

Solving scientific grand challenges requires effective use of cyber infrastructure. Future computing platforms, including Field Programmable Gate Arrays (FPGAs), General Purpose Graphics Processing Units (GPGPUs), multi-core and multi-threaded processors, and Cloud computing platforms, can dramatically accelerate innovation to solve complex problems of societal importance when supported by a critical mass of sustainable software.

This project will organize scientific communities to help leverage the disruptive potential of future computing platforms through sustainable software. Grand challenge problems in biological science, social science, and security domains will be targeted based on their under-served needs and demonstrated possibilities. Users will be engaged through interdisciplinary workshops that bring together domain experts with software technologists with the goals of identifying core opportunity areas, determining critical software infrastructure, and discovering software sustainability challenges. The outcome will be an in-depth conceptual design for a Center for Sustainable Software on Future Computing Platforms, as part of the Software Infrastructure for Sustained Innovation (SI2) program. The design, scoped toward grand challenge problems, will identify common and specialized software infrastructure, research, development and outreach priorities, and coordination with the SSE and SSI components of the SI2 program. The interactions will offer a comprehensive understanding of grand challenges that best map to future computing platforms and the software infrastructure to best support scientists' needs. The workshops will enhance understanding of future platforms' potential for transformative research and lead to key insights into cross-cutting problems in leveraging their potential. Published results will help guide future research and reduce barriers to entry for under-represented groups.

This project, Standards-based CyberInfrastructure for HydroMeteorology (SCIHM), seeks to link two disciplines--hydrology and meteorology--each of which has a sophisticated CI already developed within their respective disciplines. This linkage will be accomplished with hydrometeorology use cases in Europe and America that will be executed in both the European and American grid computing environments using federated data and computing standards. With research and development partners from several American and European institutions, the project is designed to take advantage of standards-based CI for hydrometeorological applications. In doing so, we will foster a unified standards-based hydrometeorological infrastructure where researchers and students from Europe and the US can rapidly simulate complex physical processes and predict extreme weather events and their hydrological, environmental and societal impacts, taking advantage of scalable on demand high-performance cloud-based computational resources and shared data space. Computational and storage layers will be seamlessly integrated with standards-based domain data services, analysis tools and models, enabling researchers and practitioners to quickly tune predictive models to their areas of interest, discover and access distributed sources of information, and engage in a collaborative analysis and interpretation of prediction results. This project will engage the broader hydrologic and meteorologic research community through CUAHSI and UCAR, the respective university consortia for these disciplines, as well as European partners.

The intellectual merit of this project is to identify current shortcomings in the workflow of existing community-based hydrometeorological prediction systems and to remedy those shortcomings through strategic cyberinfrastructure enhancements. There is presently a dire need for earth system scientists to have at their disposal computational systems that are accessible, extensible and scalable for a wide range of research and prediction problems. This project will directly link high-performance computing that supports standards- based management of advanced computing and storage resources, with distributed service-oriented cyberinfrastructure designs. Creating such a capability will permit better depiction of complex earth system processes in models, allow for improved characterization of model and data uncertainties and will greatly facilitate hypothesis testing, ultimately resulting in improved predictions.

One principle goal of this project is to vastly expand data access and computational access through standards-based cyberinfrastructure development. The broader impact of meeting this goal is placing powerful environmental prediction tools into the hands of researchers and decision makers around the world, in places where such capabilities simply cannot presently exist. The cyberinfrastructure enhancements delivered by this project will greatly streamline the hydrometeorological modeling process and accelerate cloud computing in the earth sciences. Development of this cyberinfrastructure within a rigorous standards-based environment will also encourage its adoption by operational government agencies which can directly benefit from it. Ultimately, the improved modeling capacity should drive improved predictive capabilities for floods and droughts by fostering a more accurate prediction system that captures the underlying physical and biological processes controlling water transport.

Collaborative Research: SI2-CHE
ExTASY Extensible Tools for Advanced Sampling and analYsis

An international team consisting of Cecilia Clementi(Rice University), Mauro Maggioni (Duke University) Shantenu Jha (Rutgers University), Glenn Martyna (BM T. J. Watson Laboratory ), Charlie Laughton (University of Nottingham), Ben Leimkuhler ( University of Edinburgh), Iain Bethune (University of Edinburgh) and Panos Parpas(Imperial College) are supported through the SI2-CHE program for the development of ExTASY -- Extensible Toolkit for Advanced Sampling and analYsis, -- a conceptual and software framework that provides a step-change in the sampling of the conformational space of macromolecular systems. Specifically, ExTASY is a lightweight toolkit to enable first-class support for ensemble-based simulations and their seamless integration with dynamic analysis capabilities and ultra-large time step integration methods, whilst being extensible to other community software components via well-designed and standard interfaces.

The primary impacts of this project are in the biological sciences. This software advances our understanding of biologically important systems, as it can be used to obtain fast and accurate sampling of the conformational dynamics of stable proteins ? a prerequisite for the accurate prediction of thermodynamic parameters and biological functions. It also allows tackling systems like intrinsically disordered proteins, which can be beyond the reach of classical structural biology. Along with the research itself, the PIs are involved with outreach programs to attract high school students to science.

Technical Description: This CAREER Award project will develop middleware to support Distributed Dynamic Data-intensive (D3) science on Distributed Cyberinfrastructure (DCI). Existing NSF-funded CI systems, such as the Extreme Science and Engineering Discovery Environment (XSEDE) and the Open Science Grid (OSG), use distributed computing to substantially increase the computational power available to research scientists around the globe; however, such distributed systems face limitations in their ability to handle the large data-volumes being generated by today?s scientific instruments and simulations. To address this challenge, the PI will develop and deploying extensible abstractions that will facilitate the integration of high-performance computing and large-scale data sets. Building on previous work on pilot-jobs, these new abstractions will implement the analogous concept of ?pilot-data? and the linking principle of ?affinity.? The result will be a unified conceptual framework for improving the matching of data and computing resources and for facilitating dynamic workflow placement and scheduling. This research has the potential to significantly advance multiple areas of science and engineering, by generating production-grade middleware for accomplishing scalable big-data science on a range of DCI systems.

Broader Importance: Increasingly, the high-performance computing resources available to scientific researchers are distributed across multiple machines in multiple locations. The integration of these resources requires a fabric of ?middleware,? upon which a wide variety of user applications, tools and services can be built and run. As more accurate and ubiquitous scientific instruments and models produce ever-larger volumes of data, however, this distributed cyberinfrastructure (DCI) is confronting unprecedented data-handling challenges that exceed the capabilities of existing DCI middleware. In this project, the PI will develop, test and implement new middleware solutions, specifically designed for the coming era of big-data distributed supercomputing.

The project will also develop new curricula and new teaching and outreach materials for introducing secondary and college students, secondary school teachers, and the general public to the emerging field of distributed data-intensive science. In partnership with FutureGrid, the PI will design simple and effective vehicles for sharing these resources with Historically Black Colleges and Universities (HBCUs) and other institutions where faculty might otherwise have relatively limited opportunity to develop advanced course materials. The PI will also partner with the Douglass Program for Women in Science, and The Academy at Rutgers for Girls in Engineering and Technology (TARGET), to increase engagement of, and support for, female students in the DCI research community.

This award will support the planning for a scientific software Institute in the area of computational chemistry. Molecular simulation is an integral part of contemporary chemistry due to its broad adoption by academic researchers and industries that use molecular mechanics and dynamics methodology to advance their science. The major molecular software programs have been downloaded by every major research university and biotech and pharmaceutical companies, and their wide usage is well exemplified by the ~30% of awarded cycles on the NSF XSEDE platforms. However, development, testing, and validation of biomolecular simulation software, and the realization of high-throughput production runs made available on various hardware architectures, is something that the user community wants and requires, but is not something that has been adequately supported in a sustained way in the academic environment.

The planning meetings will enable invaluable graduate training by inviting local graduate students in the computational sciences and provide them an opportunity to understand the landscape of research opportunities at the interface of cyberinfrastructure and biomolecular sciences. The second workshop will examine education, outreach and training opportunities that an Institute of such scale and scope provides. The blueprint for the Institute will highlight multiple inter-disciplinary research problems and agenda; collectively, this will contribute to the training of the next-generation computational scientists and application-oriented cyberinfrastructure experts.

Many scientific problems depend on the ability to analyze and compute on large amounts of data. This analysis often does not scale well; its effectiveness is hampered by the increasing volume, variety and rate of change (velocity) of big data. This project will design, develop and implement building blocks that enable a fundamental improvement in the ability to support data intensive analysis on a broad range of cyberinfrastructure, including that supported by NSF for the scientific community. The project will integrate features of traditional high-performance computing, such as scientific libraries, communication and resource management middleware, with the rich set of capabilities found in the commercial Big Data ecosystem. The latter includes many important software systems such as Hadoop, available from the Apache open source community. A collaboration between university teams at Arizona, Emory, Indiana (lead), Kansas, Rutgers, Virginia Tech, and Utah provides the broad expertise needed to design and successfully execute the project. The project will engage scientists and educators with annual workshops and activities at discipline-specific meetings, both to gather requirements for and feedback on its software. It will include under-represented communities with summer experiences, and will develop curriculum modules that include demonstrations built as 'Data Analytics as a Service.'

The project will design and implement a software Middleware for Data-Intensive Analytics and Science (MIDAS) that will enable scalable applications with the performance of HPC (High Performance Computing) and the rich functionality of the commodity Apache Big Data Stack. Further, this project will design and implement a set of cross-cutting high-performance data-analysis libraries; SPIDAL (Scalable Parallel Interoperable Data Analytics Library) will support new programming and execution models for data-intensive analysis in a wide range of science and engineering applications. The project addresses major data challenges in seven different communities: Biomolecular Simulations, Network and Computational Social Science, Epidemiology, Computer Vision, Spatial Geographical Information Systems, Remote Sensing for Polar Science, and Pathology Informatics. The project libraries will have the same beneficial impact on data analytics that scientific libraries such as PETSc, MPI and ScaLAPACK have had for supercomputer simulations. These libraries will be implemented to be scalable and interoperable across a range of computing systems including clouds, clusters and supercomputers.

High-Performance and Distributed Computing (HPDC) has made significant impact in simulation and modeling of many domains, ranging from understanding
fundamental physical processes to designing new and functional materials. With the growing richness of capabilities, increased ease of access and availability of high-performance and distributed computing resources, the opportunity to employ such resources to novel problems types and domains arises. The aim of the proposed workshop is to bring the HPDC and the Polar Science communities together to collectively explore, examine and identify the opportunities and barriers in the use of high performing computing techniques and resources for polar science studies. The workshop has the potential to bring advantages to both fields, where NSF has been investing considerable resources over the past years. Outcomes from the workshop will benefit science and society through the laying of the foundation for collaborative efforts aiming at improving the understanding of the variability of the polar regions at different timescales, and their connection to climate and other Earth systems. Moreover, it will contribute to the building of a polar cyberinfrastructure, one of the goals of the Polar Cyberinfrastructure Program at the National Science Foundation.
The workshop will be held at the Rutgers Climate Institute and will host approximately 40 participants, lasting two and a half days, at the end of October or early November. A broad group of scientists from the polar science and HPDC communities, both broadly defined, will be engaged as members of the organizing committee to identify major challenges, opportunities and questions such as: What HPDC/CI support is already available to the Polar Science community? What HPDC/CI capabilities are missing? Unsatisfactory? Insufficient or Incomplete? What are the cyberinfrastructure barriers beyond availability that limits further advancing Polar Science? The workshop participants will be also asked to create a ranked list of scientific challenges that the community aims to tackle on a 1-year, 5-year and a decadal time frame. The final outcome of the workshop will be a report that defines and conveys a community-based vision for the Polar Cyberinfrastructure Program at the National Science Foundation.

To support science and engineering applications that are the basis of many societal and intellectual challenges in the 21st century, there is a need for comprehensive, balanced, and flexible distributed cyberinfrastructure (DCI). The process of designing and deploying such large-scale DCI, however, presents a critical and challenging research agenda. One specific challenge is to produce tools that provide a step change in the sophistication of problems that can be investigated using DCI, while being extensible, easy to deploy and use, as well as being compatible with a variety of other established tools. RADICAL Cybertools will meet these requirements by providing an abstractions-based suite of well-defined capabilities that are architected to support scalable, interoperable and sustainable science on a range of high-performance and distributed computing infrastructure. RADICAL Cybertools builds upon important theoretical advances, production-software-development best practices, and carefully-analyzed usage and programming models. RADICAL Cybertools is posed to play a role in grand-challenge problems, ranging from personalized medicine and health to understanding long-term global and regional climate. All software developed through the project will be open source and will be licensed under the MIT License (MIT). Version control on the SVN repository will be accessible via http://radical.rutgers.edu.

Existing and current utilization of RADICAL Cybertools is built upon preliminary research prototypes of RADICAL Cybertools. There is a significant difference, however, in the quality and capability required to support scalable end-usage science, compared to that of a research prototype. It is the aim of this project to bridge this gap between the ability to serve as a research prototype versus the challenges of supporting scalable end-usage science. This will be achieved by addressing existing limitations of usability, functionality, and scalability. We will do so by utilizing conceptual and theoretical advances in the understanding of distributed systems and middleware, resulting in a scalable architecture and robust design. We will employ advances in performance engineering, data-intensive methods, and cyberinfrastructure to deliver the next generation of RADICAL Cybertools. This project will take the existing research prototypes of RADICAL Cybertools to the next level towards becoming a hardened, extensible, and sustainable tool that will support a greater number of users, application types, and resource types.

In association with the "big data" phenomena with greater volumes of data being generated, there has been an increase in the need to analyze and process that data in real-time. Example sources of this data include social media, robots, scientific instruments and simulations. Real-time analysis of "big data" holds great promise for industry, finance, communication, first responders, and public safety, to name just a few promising areas. Common to these applications is the requirement to process, analyze and respond to data immediately to make "live" decisions and steer applications. This project will organize and hold a workshop that will lay the foundations for a common understanding of the requirements and help the scientific community prepare for data of ever greater volumes and acquisition velocity.

This project will support a community workshop to understand the class of applications that entail streaming data and related (near) real-time steering and control. Such applications are increasing in their sophistication, pervasiveness and scale. The goal of the workshop is to identify application, infrastructure and technology communities in this area and to clarify challenges that they face. Workshop participants will focus on application features and requirements as well as hardware and software needed to support them and will identify recommendations for NSF's production distributed cyberinfrastructure (hardware and software) for both the short and long term.

There is a critical need to solve problems of societal and technical importance faster and at larger scales than is currently possible. This project will support several new applications to utilize the Blue Waters Supercomputer at scales that are scientifically needed but simply not possible otherwise or elsewhere. These include applications ranging from protein conformations to polar sciences. If successful, this research will engender a significant step up in capabilities towards extreme scale computing and data-intensive science.

The computational resources made available as part of this project will enable the design, development and testing of multiple new algorithms, middleware and methods. In the first track, the researchers propose a new approach to characterize the conformational landscape of the NMDAr LBD in its different forms, by using novel sampling methodologies and workload management tools. Leveraging sophisticated sampling methods, simulations will provide an unprecedented atomically detailed picture of the different states the protein can adopt as a function of the ligands it interacts with, and will also provide predictions of the kinetic pathways that link these states together. The result will be a conceptual framework to understand the sometimes perplexing experimental results and a springboard for the rational design of further experiments.

This study will open the way to a conceptually different approach to studying large conformational changes in complex macromolecules, as the same methodology and computational infrastructure can in principle be applied to a large number of biomedically relevant systems. The second track of this project is concerned with development, scaling and optimization of SPIDAL (Scalable Parallel Interoperable Data Intensive Libraries) and MIDAS (Middleware for Data-intensive Analysis and Science) that will be used to enhance the scalability of a range of data-intensive applications.

Ierapetritou, 1547171

The overall objective of this project is to develop a data-enabled computational framework for the efficient design and improved operation of pharmaceutical processes using advances in cyberinfrastructure (CI). At present, product/process design in the pharmaceutical industry is largely performed in an empirical manner relying primarily on heuristic experimentation - hence the alarm raised by the FDA in the Critical Path Initiative to transition toward a more Quality-by-Design (Qbd) paradigm. For such QbD-based decision making to be practically applicable in the pharmaceutical industry, a robust computational framework is required. The proposed framework will allow the seamless integration of high-fidelity simulations, experimental data and physical sensors with a runtime system that supports the dynamic execution of model simulations, validation and refinement, on advanced computing platforms.

Intellectual Merit :
Motivated by the these considerations, this work will target the following specific aims: 1) CI enabled multi-scale process modeling of an integrated production line; and 2) model integration within a pilot-plant experimental facility and real-time refinement of the multi-scale model. A pilot plant available to the PIs via the NSF-ERC on structured organic particulate systems and will allow the validation and testing in a realistic setting. The work should lead to several theoretical advances namely: 1) an efficient method to develop a multi-scale model of a mixer-granulator process, and 2) strategies to integrate developed models with physical sensors and processes, experimental data, in combination with a robust computational framework. The proposed approach will result in flexible solutions for decision-makers as they can utilize the developed framework as a virtual experimental toolkit to perform what-if-scenarios in silico to obtain optimal operating conditions, prior to implementation in the real plant.

Broader Impacts :
Research findings can be used to enhance the profitability and sustainability of many industries that deal with particulate processes, thus directly impacting the US economy including food, pharmaceutical, and chemical industries. Software prototypes and a library of solutions to problems developed during the project will be made available for other researchers in the field to use and improve upon. The PIs will integrate research findings into the current undergraduate design course. This will enable seniors to not only work on current chemical/biochemical problems but also on problems relevant to the predominantly particulate-based industries that surround Rutgers and are critical to the New Jersey economy. Co-PI Jha will also introduce a new elective in ECE titled "Advanced High-Performance and Distributed Computing" and will use research problems and findings as case-studies. The PIs will work with industrial collaborators involved in this proposal to obtain realistic case studies that are highly industrially relevant, thereby increasing the employability of the graduating senior class. To encourage under represented groups, the PIs will also work with minority societies within Rutgers, (National Society of Black Engineers), and the Douglas Science Institute for Women, which have established programs in place, to expose and thereafter recruit qualified women and minority students at the graduate level.

Biomolecular simulation is rapidly growing in popularity and scope of application. It is no longer the preserve of a few specialist groups but is widespread - part of the ?toolkit? used by researchers in a wide variety of fields, often closely integrated with other experimental research techniques. As the use of biomolecular simulation grows, a corresponding boom in software development is taking place. Giving researchers the right tools and training to develop such software will reduce the amount of ?wheel-reinvention?, consequently improve research productivity, and make research more cost-effective.

An important question that arises however, is how do we judge the effectiveness of the tools and training used? This grant will help develop answers to such questions in the context of CECAM Extended Software Development workshop on biomolecular simulations, that will be held in Juelich, Germany on October 12-25 2015. This workshop will provide hands-on tutorials incorporating active learning approaches. The workshop will advance the way interdisciplinary computational science training is conducted as well as how tools to support computational science are developed. This grant will also support the participation of up to twelve US-based scientists to attend this workshop and contribute to achieving workshop objectives.

The impact of this workshop will be significant in three directions: It will (i) provide support for US junior and early career scientists to learn a plethora of new tools and techniques central to their science, (ii) lead to an improvement to the ?tools? being developed as well as the methodology by which the effectiveness of tools is assessed, and (iii) move towards a method of data-driven and dynamic organization of workshops. This provides a natural evolution in the sophistication of simple ?hackathons?. It will provide a template for how future multidisciplinary hands-on workshops training can be organized.

Lessons learned from this workshop will be used as a basis to improve future computational science classes at Rutgers, Rice and elsewhere. The workshop participants will be selected in order to have a broad representation of the communities we want to engage. The PIs will have a special outreach activity to early career scientists (newly established faculty and post-doctoral researchers) as well as graduate students, and will encourage the participation of minorities and women.

This workshop will (i) improve the ability of non-computing specialist scientists to utilize existing tools and software better, but also help formulate their needs and requirements better, (ii) enable tool-smiths to answer a critical and recurring question, viz., how to separate the impact and suitability of tools from the effectiveness of the techniques used to teach and train?, (iii) develop exercises that will not only improve scientific productivity but also enable domain scientists to understand how to employ tools that reach adequate scales seamlessly from their local environments to remote resources, (iv) propose an agenda and a set of exercises that will be adaptive based upon real-time feedback and assessment, and serve as a template that can be generalized and instantiated to other domains.

One of the major current challenges with polar cyberinfrastructure is managing and fully exploiting the
volume of high-resolution commercial imagery now being collected over the polar regions. This data can be used to understand the changes in polar regions due to climate change and other processes. The potential of global socio-economic costs of these impacts make it an urgent priority to better understand polar systems. Understanding the mechanisms that underlie polar climate change and the links between polar and global climate systems requires a combination of field data, high-resolution observations from satellites, airborne imagery, and computer model outputs. Computational approaches have the potential to support faster and more fine-grained integration and analysis of these and other data types, thus increasing the efficiency of analyzing and understanding the complex processes. This project will support advances in computing tools and techniques that will enable the Polar Sciences Community to address significant challenges, both in the short and long-term.

The impact of this project will be in the improvements in the ability to utilize advanced cyberinfrastructure and high-performance distributed computing to fundamentally alter the scale, sophistication and scope of polar science problems that will be addressed. This project will not implement those changes but will identify and lay the groundwork for such impact across the Polar Sciences. The Project personnel will identify primary barriers to the uptake of high-performance and distributed computing and will help alleviate them through a combination of community based solutions and training. The project will also produce a roadmap detailing a credible and effective way to meet the long-term computing challenges faced by the Polar Science community and possible plans to effectively address them. This project will establish mechanisms for community engagement which include, gathering technical requirements for polar cyberinfrastructure and supporting and training early career scientists and graduate students.

The Molecular Sciences Software Institute (MolSSI) will become a focus of scientific research, education and scientific collaboration for the worldwide community of computational molecular scientists. The MolSSI aims to reach these goals by engaging the computational molecular science community in multiple ways to remove barriers between innovations that often occur in small single-researcher groups and the implementation of these ideas in software that is used in the production of science by the entire community. Thus, great ideas will not languish in the "just get the science right" mode, but be incorporated into usable software for the wider community to enable bigger and better molecular science. The MolSSI will catalyze significant advances in software infrastructure, education, standards, and best-practices. These advances are critical because they are needed to address the next set of grand challenges in molecular science. Activities catalyzed by the Institute will improve the interoperability of the software used by the community, make easier the use of this software on the varied and heterogenous computing architectures that currently exist, enable greater scalability of existing and emerging theoretical models, as well as substantially improving the training of molecular-science students in software design and engineering. Through the range of outreach efforts by its multiple institutions, the MolSSI will engage the community to increase the diversity of its workforce by more effectively attracting and retaining students and faculty from underrepresented groups. All of these endeavors will result in fundamentally and dramatically improved molecular science software and its usage, that will reduce or eliminate the current delays - often by years - in the practical realization of theoretical innovations. Ultimately, the Institute will enable computational scientists to more easily navigate future disruptive transitions in computing technology, and most importantly, tackle problems that are orders of magnitude larger and more complex than those currently within their grasp and to realize new, more ambitious scientific objectives. This will accelerate the translation of basic science into new technologies essential to the vitality of the economy and environment, and to compete globally with Europe, Japan, and other countries that are making aggressive investments in advanced cyber-infrastructure.

The MolSSI aims to reach these goals by engaging the computational molecular science community in multiple ways to remove barriers between innovations that often occur in small single- principle investigator groups and the implementation of these ideas in software that is used in the production of science by the entire community. The MolSSI will create a sustainable Molecular Sciences Consortium that will develop use cases and standards for code and data sharing across the software ecosystem and become a focus of scientific research, education and scientific collaboration for the worldwide community of computational molecular scientists. The Institute will create an interdisciplinary team of Software Scientists who will help develop software frameworks, interact with community code developers, collaborate with partners in cyber-infrastructure, form mutually productive coalitions with industry, government labs, and international efforts, and ultimately serve as future experts and leaders. In addition, the Institute will support and mentor a cohort of Software Fellows actively developing code infrastructure in research groups across the U.S., and, in turn, they will engage in MolSSI outreach and education activities within the larger molecular science community. Through a range of multi-institutional outreach efforts, the Institute will engage the community to increase the diversity of its workforce by more effectively attracting and retaining students and faculty from underrepresented groups. The Institute will educate the next generation of software developers by providing workshops, summer schools, on-line forums, and a Professional Master's program in molecular simulation and software engineering. MolSSI will be guided by an internal Board of Directors and an external Science and Software Advisory Board, both comprised of leaders in the field, who will work together with the Software Scientists and Fellows to establish the key software priorities. MolSSI will be sustained by a mix of labor contributed by the community, revenue from education programs and license revenues. In summary, the MolSSI's ultimate impact will be in the translation of basic science into future technological advances essential to the economy, environment, and human health.

The study of hazards and renewable energy are paramount for the development and sustainability of society. Similarly, the emergence of new climatic patterns pose new challenges for future societal planning. Geospatial data are being generated at unprecedented rate exceeding our analysis capabilities and leading towards a data-rich but knowledge-poor environment. The use of advanced computing tools and techniques are playing an increasingly important role in contributing to solutions to problems of societal importance. This project will create specialized computational tools that will enhance the ability of scientists to effectively and efficiently study natural hazards and renewable energy. The use of these tools will support novel methods and the use of powerful computing resources in ways that are not currently possible.

Many scientific applications in the geosciences are increasingly reliant on "ensemble-based" methods to make scientific progress. This is true for applications that are both net producers of data, as well as aggregate consumers of data. In response to the growing importance and pervasiveness of ensemble-based applications and analysis, and to address the challenges of scale, simplicity and flexibility, the research team will develop the Ensemble Toolkit for Earth Sciences. The Ensemble Toolkit will provide an important addition to the set of capabilities and tools that will enable the geosciences community to use high-performance computing resources more efficiently, effectively and in an extensible fashion. This project represents the co-design of Ensemble Toolkit for Earth Sciences and is a collective effort of an interdisciplinary team of cyberinfrastructure and domain scientists. It will also support the integration of the Ensemble Toolkit with a range of science applications, as well as its use in solving scientific problems of significant societal impact that are currently unable to utilize the collective capacity of supercomputers, campus clusters and clouds

Satellite imagery is rapidly transforming the way we see the planet, including our ability to study the most remote parts of the Arctic and Antarctic. Satellite imagery can help us map networks of rivers, study changes in the flow and thickness of glaciers, identify rock and soil types, and even find animals like penguins and seals. Because the availability of imagery in polar areas has increased rapidly over the last decade, we are now faced with a challenge: To move from small pilot-studies to pan-Arctic or pan-Antarctic analyses of geological and biological processes requires new infrastructures that link scientists, satellite imagery, and fast computing. The project, called ICEBERG - Imagery Cyberinfrastructure and Extensible Building-Blocks to Enhance Research in the Geosciences, aims to build the cyberinfrastructure required to make the most of satellite imagery for geosciences, starting with researchers working in polar areas, and then branching out to the larger community. The Broader Impacts of this proposal include the training of undergraduate and graduate students, as well as young investigators and female scientists. Moreover, the scientific findings enabled by this proposed cyberinfrastructure will have immediate benefits for our ability to predict the future dynamics of the polar regions, and critical to the management of Arctic and Antarctic resources.

Polar geosciences stands at the precipice of a revolution, one enabled by the confluence of cutting edge analytical tools, petabytes of high-resolution imagery, and an ever growing array of high performance computing resources. With these tools at hand, we can look beyond incremental improvements in our understanding of the polar regions. Near-real time datasets of geological and biological importance at the continental scale are within our reach if we create those critical cyberinfrastructure components that allow the geosciences community to exploit existing assets and establish a common workflow for reproducible imagery-enabled science. The research objective of this proposal is to understand the biological, geological, and hydrological functioning of the polar regions at spatial scales heretofore beyond the reach of individual PIs, and to develop tools for imagery-enabled science that can be applied globally. The resulting cyberinfrastructure, which we call ICEBERG - Imagery Cyberinfrastructure and Extensible Building-Blocks to Enhance Research in the Geosciences, is an extensible system for coupling open-source image analysis tools with the use of high performance and distributed computing (HPDC) for imagery-enabled geoscience research. We propose a project to (1) develop open source image classification tools tailored to high-resolution satellite imagery of the Arctic and Antarctic to be used on HPDC resources, (2) create easy-to-use interfaces to facilitate the development and testing of algorithms for application specific geoscience requirements, (3) apply these tools through use cases that span the biological, hydrological, and geoscience needs of the polar community, (4) transfer these tools to the larger non-polar community.

Glutamate receptors, and understanding their binding characteristics, are of fundamental biomedical importance as they mediate neuronal signaling. This project proposes to characterize and understand glutamate binding to the N-methyl-D-aspartate receptor (NMDAr), a member of the glutamate receptor family of proteins, with potential profound consequences for neuroscience and pharmacology. However, the characterization of the configurational landscape of NMDAr is a High Performance Computing (HPC) problem. It requires simulations with timescales and system sizes well beyond any that have previously been undertaken. The project will use the petascale computing capabilities of Blue Waters to study such a system, using new sampling methods and original computing and data processing techniques.

The project will use molecular dynamics (MD) simulations to study this macromolecular system. However, it remains a challenge to obtain an adequate sampling of the configurational space of complex chemical systems to accurately describe the structural properties of important substates, their relative propensities, and accessible transitions between them. The project proposes to use a novel software framework that on the right computational resource makes a step-change in our ability to sample the conformational space of macromolecules by MD. The project will study a protein of great biomedical relevance that exemplifies these issues, namely the ligand binding domain (LBD) of the N-methyl-D-aspartate receptor (NMDAr). The idea at the core of the software strategy is similar to many other multiscale methods -- such as umbrella sampling, metadynamics, adaptive biasing methods, or transition path sampling: instead of one or a few long MD trajectories being run, many (hundreds or thousands) of short trajectories may be simulated concurrently. Information is extracted from these very large datasets using sophisticated data reduction and analysis methods, and the coarse-grained information -- which embodies the chemical insight necessary to understand the system, e.g. an approximate free energy -- is used to refine the way in which further trajectories are generated (i.e., how we sample). Results from the analysis of the space sampled are then used in an iterative process to further direct the search of the conformational space (i.e., where we sample). This Blue Waters allocation will allow the project to access a total of 2.7 milliseconds of simulation of the NMDAr LBD system. With the three orders of magnitude (at least) speed-up in sampling allowed by our methodology with respect to plain MD, the project will be able to map the configurational landscape of this protein relevant for conformational dynamics up to a timescale of seconds, that is, to completely characterize the role of the ligand binding domain in the biological function and mechanism of NMDAr.

The idea of sharing resources between institutions is not new and has been relatively successful in a few instances and largely unsuccessful in many cases. This proposal elaborates a plan to construct the Campus Compute Cooperative (CCC), a shared cyberinfrastructure as well as a social fabric; both computational resources and support staff expertise will be pooled and shared. The CCC will: i) connect and federate campus physical infrastructures; ii) use market-based mechanisms for resource allocation and quality of service to avoid the tragedy of the commons; iii) leverage InCommon and local identity management systems to provide integrated cross-institution authentication and access control of resources; iv) facilitate secure data and storage sharing between institutions and research labs; v) offer "cloud bursting" to member institutions; and, vi) provide paid-for, differentiated quality of service.

The impact of on-demand, pay-per-use (cloud) resources on society has been huge. Making the most of limited resources is critical to the success of the nation's research enterprise. By rationally and securely sharing the limited computational and human resources available for research the CCC will both accelerate discovery and provide a research cyberinfrastructure with better cost/benefit.

The proposed exploratory research project aims to develop a next-generation autonomous manufacturing process for pharmaceutical production that integrates product and process informatics with knowledge management. The integration of process data, process models, and information management tools will enable adaptive adjustment to the operating conditions to compensate for variability in raw materials and changing product needs. The research team will take advantage of the facilities of the Center for Structured Organic Particulate Systems (C-SOPS) at Rutgers University for proof of principle studies and generation of experimental data for advancing fundamental understanding of each process.

To enable the transition towards more autonomous and de-centralized decisions across the entire manufacturing supply chain, it is imperative to develop an integrated platform to: (a) acquire data regarding process and product operations from the manufacturing facility using data historian platforms; (b) utilize the data to extract further knowledge on process understanding; and (c) use this knowledge to dynamically and adaptively improve process operations. For task (a), the use of a data management system, such as OSI PI, is proposed with the ability to receive data from multiple sources including the control platform as well as the Process Analytical Technology (PAT) data management tool. This platform has the capability to build up recipe hierarchical structure using Event Frame functionality and periodically push the data into a cloud system for permanent enterprise-wide data storage and efficient sharing. For task (b), the use of advanced statistical and machine learning methods is proposed, in combination with data reconciliation methods. Finally, for task (c), information acquired will be utilized to adapt the model feasible space by building accurate surrogate models and adaptively refine them using the online data acquisition. Although the focus will be on pharmaceutical production processes, the proposed work, if successful, can have significant broader impacts on a variety of industrial processes. Two PhD students will be trained on the development of a cutting-edge framework for autonomous manufacturing processes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project builds upon a previously funded middleware development effort by the Research in Advanced Distributed Cyberinfrastructure and Applications Laboratory (RADICAL) at Rutgers University; the previously developed middleware building blocks were known as RADICAL-Cybertools (RCT). In the current project, the team pursues a targeted set of developments, driven by the need to scale the number of software components, user, and supported platforms; and improve performance, engineering processes, and sustainability. The resulting capabilities will serve scientific applications in multiple domains, including software engineering, chemical physics, materials science, health science, climate science, drug discovery and particle physics.

This project builds upon a prior prototype investment, which developed a pilot system for leadership-class HPC machines, and a Python implementation of SAGA, a distributed computing standard. The current effort is organized around three activities:
- Extending RCT functionality to reliably support a range of novel applications at scale (examples include tightly coupling traditional HPC simulations with machine-learning methods);
- Enhancing RCT to be ready to support new NSF systems, such as the Frontera supercomputing system and other new systems;
- Prototyping a new component: a campaign manager for computational resource management.
Data-driven approaches will be used to improve software development, engineering, and life-cycle management, and to enhance the long-term sustainability of RCT and the supported communities. The project includes use cases that are representative examples of the growing community that RCT engages and supports, such as the ATLAS high-energy physics project and the QCArchive project enabling large-scale force-field construction and physical property prediction.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

High-fidelity computer simulations underpin discovery in a broad range of scientific domains. However, their computation cost limits their full potential. There have been increasing efforts in approximating scientific simulations with deep neural networks, to accelerate simulation workflows by orders of magnitude. Current practice, however, largely relies on fixed network architectures and offline simulation data -– predefined by experience, rather than optimized by quantitative metrics. This leads to an empirical, subjective, and laborious practice, yet with a suboptimal outcome. This research addresses the above critical gaps with a new conceptual, mathematical, and infrastructure framework for developing Smart Surrogates. As a domain-agnostic framework, Smart Surrogates will deliver timely support for an increasing but yet-to-be-met demand for surrogate modeling for scientific simulations. The prototype surrogates created in this project will also directly enable long-term follow-on research in each of the domains involved. This collaborative research provides multidisciplinary training at the intersection of artificial intelligence, high-performance computing, and scientific simulations in a variety of domains, helping prepare next-generation researchers adept at transdisciplinary thinking and skill. It plans to proactively recruit students from underrepresented groups, and develop a hands-on workshop on Smart Surrogates for dissemination to a broader student body. Finally, the dissemination of ROSE as an open-source toolkit will impact HPC simulation workflows in a broad range of social applications, including but not limited to drug design and the study of climate change.<br/><br/>The development of Smart Surrogates includes three parallel but interwoven methodological, infrastructure, and domain evaluation thrusts: 1) Thrust I – Methodological Innovations: This thrust develops fundamental innovations in deep active learning to jointly optimizes training-data selection and neural architectures, in a Bayesian setting equipped with uncertainty quantification. This allows Smart Surrogates to support the intelligent active selection of training simulations along with dynamic adjustment of neural architectures; 2) Thrust II – Infrastructure innovations): This thrust designs, implements, and disseminates the RADICAL Optimal & Smart-Surrogate Explorer (ROSE) toolkit to support the concurrent and adaptive executions of simulation and surrogate training and selection tasks.; 3) Thrust III – Scientific innovations: This thrust grounds the developments and evaluation of Smart Surrogates in two domain problems: surrogates for 1) diffusion equations with singular initial conditions and 2) personalized virtual heart simulations, built on the team’s past works with established domain collaborators. This allows fast prototyping, while setting the basis for a continuum of follow-up research to adopt Smart Surrogates in a larger range of complex scientific simulations.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.