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CNS Core: Small: Moving Machine Learning into the Next-Generation Cloud Flexibly, Agilely and Efficiently: University of North Carolina at Charlotte

Dazhao Cheng

[email protected]

Machine Learning (ML) is being used in new ways to develop intelligent software. Meanwhile, serverless computing is redefining how to use cloud computing platforms. With serverless computing, ML specialists only need to define a set of stateless functions that have access to a common data store. Current ML software systems are generally specialized for first-generation cloud computing systems that do not have the flexibility required for serverless infrastructures. This project aims to take advantage of serverless computing to deploy machine learning software. This simplifies the deployments, avoids the need for infrastructure maintenance, and includes built-in scalability and cost-control.<br/> <br/>Due to the ease of management and ability to rapidly scale, serverless computation has become the trend to build next-generation ML services and applications. This project proposes a unified serverless computing framework that aims to be flexible, agile and efficient for moving ML into the next-generation cloud to achieve better simplicity, manageability and productivity. In particular, to bridge the semantic gap between the serverful ML model and the serverless cloud platform, this project identifies three major tasks: fine-grained computation management, an efficient communication strategy and a cost-effective service model.<br/> <br/>This project aims for widespread serverless computing by removing server and operation system level details and simplifying the process of building and managing ML applications. It is a continuum along which developers and operations teams become more accustomed to increased automation and abstraction, and more comfortable breaking ML applications into simple, easy-to- manage microservices, application interfaces, and functions. The result is that developers are free to target the right tools for the right tasks and to build ML applications easily that span any number of different serverless services.<br/> <br/>This project emphasizes open-source software development. This will enhance the access to stream data analytic frameworks and broaden the project?s impact. Furthermore, the models and workloads/traces from this project may enable further research by others. The project repository[https://abclab-uncc.github.io/website/grants.html] (data, code, results, emulators, and simulators) will be maintained in the next 5 years.<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.

NSFOpen Education ResourcesThe Research University (TRU)

Evolutionary inference in the presence of gene tree discordance: Indiana University

Matthew Hahn

[email protected]

Large DNA sequencing projects have revealed that there is not just one way that species are related. Genes and pieces of the same genome can have different relationships. Sometimes these relationships differ because of ancient mating between species called hybridization. When hybridization occurs, important traits can be shared between unrelated species. This project will develop new methods to determine the history of traits shared between unrelated species. It also will develop methods to more accurately determine the relationships among species. The research will allow researchers to identify cases where key traits, such as insecticide resistance, arise once and are passed between species. The research combines mathematical modeling with new statistical approaches. These will be combined to produce open-source software for carrying out the analyses. The software will ensure that all scientists will be able to take advantage of the tools developed by the research, adding to the national infrastructure by enabling new biological discoveries. This work also will improve and accelerate biological research with multiple societal benefits, including helping to identify genes controlling important traits. This project will support the training of a postdoctoral researcher, graduate students, and undergraduates from groups that are underrepresented in science and technology careers. Workshops will be developed to provide training in the use of the software developed by this project.<br/><br/>Species trees make it possible to understand the evolutionary history of traits. Previous work has documented how gene trees that are discordant with the species tree lead to inaccurate inferences about the timing and direction of trait transitions, the number of trait transitions, how often such changes are thought to be driven by adaptive evolution, and even the species tree itself. This research goes beyond documenting the problems of gene tree discordance, providing solutions to these problems. The theoretical approach used will make it possible to incorporate the multiple causes of discordance?including introgression?into a single framework, allowing researchers to make statistically rigorous inferences about evolutionary processes in the presence of discordance. Integrating these results into a single software package enables these insights and methods to be applied widely, for both binary and continuous traits.<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.

NSFOpen Education ResourcesThe Research University (TRU)

RAPID: Making the Transition to Remote Science Teaching and Learning: Concord Consortium

Daniel Damelin

[email protected]

Across the nation, teachers have shifted to delivering instruction remotely due to COVID-19. This transition to remote instruction has presented challenges for secondary science teachers who previously engaged their students in hands-on learning through empirical tests and observations of real-world phenomena, but whose students might not now have the equipment or materials in their homes to enable hands-on investigations. In this project, Concord Consortium will develop and test a remote professional development program that is designed to support secondary science teachers in making the transition from face-to-face to remote instruction, while still providing their students with engaging opportunities to learn from empirical data. Through this professional development program, science teachers will gain practice in using an open-source tool that teaches data-based modeling in the context of complex systems. For example, this tool will enable students to use data related to the pandemic to develop models that can predict relevant outcomes. As students develop skills related to modeling, they will be better prepared for the STEM (science, technology, engineering, and mathematics) workforce of the future, which increasingly requires the ability to interpret and use large-scale data. Research will identify the features of professional development that support teachers in providing remote instruction that is aligned with the Next Generation Science Standards (NGSS) related to modeling and systems thinking. <br/><br/>Concord Consortium will provide remote professional development to ten secondary science teachers on modeling using complex systems via open-source software. They will collect surveys, interviews, and teacher-generated curricular materials to ascertain how the teachers develop pedagogical strategies for remote instruction, which are designed to support the development of their students' modeling skills and practices. Additionally, they will collect data from student work, including log files from the software, to determine how the students demonstrate modeling practices and knowledge of systems in the context of this remote instruction. The results from inductive descriptive analyses of these data will be submitted to empirical journals. Other dissemination materials will outline the design principles that support teachers in modifying NGSS-aligned curricular materials for remote delivery. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.<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.

NSFOpen Education ResourcesThe Research University (TRU)

CDS&E: Ab Initio Ultrafast Dynamics of Spin, Valley and Charge in Quantum Materials: University of California-Santa Cruz

Yuan Ping

[email protected]

This grant is being funded by the Condensed-Matter and Materials Theory program in the Division of Materials Research and by the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry.<br/><br/><br/>Nontechnical Summary<br/><br/>The promise of quantum computers to perform calculations beyond the reach of any current or conceivable non-quantum computer has made them one of the nation's highest research priorities. This award supports computational research and education on the motion of electrons in quantum materials. Several recently-discovered materials exhibit the potential to store quantum information in individual electrons that may hold the key to the next generation of quantum computers and quantum communication. Realizing the full potential of these materials requires precise understanding of how long quantum information can be stored in electron spins and how it disappears eventually by interacting with the vibrations of atoms in the material.<br/><br/>The investigators will develop a computational methodology to simulate quantum electron motion on large supercomputers. They will use this technique to predict how electron spin changes over times ranging from femtoseconds to microseconds in several promising materials, such as lead halide perovskites, containing heavy atoms that couple spin to the movement of electrons. Electrons in transition-metal dichalcogenides, another alternative for storing quantum information, can be found in multiple so-called "valleys;" the investigators will also study how electron valley and electron spin couple. For each of these materials, they will simulate the interaction of these quantum states with extremely short laser pulses to interpret experimental measurements of spin and valley dynamics.<br/><br/>This award will also support the team's effort in increasing participation and representation of women in STEM disciplines, especially in the physical sciences. By integrating simulations into intuitive visualizations using augmented reality, they will make electron dynamics understandable to undergraduate and high school students. Finally, this project will strengthen the research infrastructure at UCSC, a Hispanic Serving Institution.<br/><br/><br/>Technical summary<br/><br/>The goal of this research project is to predict quantitatively quantum dynamics of electrons with spin, valley, or other internal degrees of freedom, entirely from first principles. The research team will develop a novel computational methodology and associated massively-parallel open-source software rapidly to evolve density matrices of quantum materials in a Lindbladian formulation, with ab initio treatment of electron-electron, electron-phonon, and electron-photon interactions. This will facilitate calculation of both coherent dynamics and dephasing of spin or valley polarization, along with their experimental signatures in ultrafast spectroscopy. Using this technique, they will investigate spin dynamics in systems with strong spin-orbit coupling and Rashba splitting such as lead halide perovskites and ferroelectric oxides, and valley dynamics in layered transition metal dichalcogenides. This fundamentally new predictive capability will facilitate quantitative analysis of ultrafast optical and free-electron laser measurements with linear and circular polarization, and accurate predictions of spin relaxation of quantum materials. This will be critical for the design and discovery of new material platforms for spintronics, valleytronics and quantum information.<br/><br/>The proposed work will arm the materials research community with first-principles quantum dynamics methods in open-source software. These will include a hierarchy of methods that keep track of different levels of coherence, with corresponding computational requirements ranging from a small computer cluster to future exascale supercomputers. It will thereby deliver a key computational technique necessary for predicting coherent and incoherent ultrafast dynamics in quantum materials, extending significantly beyond the capabilities of existing first-principles methods. The work funded in this project responds directly to one of NSF's 10 Big Ideas, the Quantum Leap, by facilitating quantitative simulation of spin relaxation and carrier dynamics critical for quantum information science. The educational activities associated with this project aim to increase participation and representation of women in STEM disciplines, especially in the physical sciences. It will expand the reach of materials simulations to K-12 education through the platform of augmented reality. This project will also strengthen the research infrastructure at UCSC, a Hispanic Serving Institution.<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.

NSFOpen Education ResourcesThe Research University (TRU)

CRII: III: Learning Dynamic Graph-based Precursors for Event Modeling: Stevens Institute of Technology

Yue Ning

[email protected]

From epidemic outbreaks to civil strife, societal events that involve large populations often deeply affect people?s lives and cause economic burden. Forecasting these events while providing context analysis helps social scientists and health practitioners to interpret and study human societies. Although many existing research efforts strive to forecast societal events, providing structured explanations for prediction is still limited given the underlying connections among entities, actions, and locations behind these events. This project presents a novel paradigm of identifying and organizing multiple types of precursors while predicting events. It identifies changing relations among entities as events evolve and studies the hidden geographical influence on events. Both entity relations and geographical connections are represented by dynamic graphs. Organizing event precursors in graphs greatly reduces the complexity of comprehending unstructured input data and delivers interpretable summarizations for event prediction. This work will involve educational activities such as development of course curriculum; training of graduate, undergraduate, and high-school students; encouraging participation of women and minority groups in academic research; and dissemination of outcomes such as software and datasets for the general public.<br/><br/>To achieve these goals, this project will integrate multiple data sources and analyze complex hierarchical features in modeling events. Although a variety of online data has been utilized to analyze and predict societal events, it also raises new challenges such as: (1) accounting for dynamic relationships within data sets; (2) preserving and learning complex knowledge structures with heterogeneous data sets; and (3) ensuring interpretable results for predictions and decision making. This project will address the challenges in the following ways: (i) it will integrate multi-source data by learning a unified multi-level semantic encoding; (ii) it will identify historical key semantics by paying attention to hierarchical text structures in a recurrent learning process; (iii) it will provide explanations for event prediction by incorporating local dynamic graph patterns and global influence graph patterns. The specific research aims will be complemented with an extensive set of evaluation plans including a retrospective evaluation on real-word event records and a user survey to evaluate graph visualizations of event precursors. The project results, including graph based empirical data, predictive evaluation tools, and open source software for analyzing events, will be shared with computer science research community and stakeholders in computational healthcare, and social science.<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.

NSFOpen Education ResourcesThe Research University (TRU)

CCRI: Planning: Enabling Quantum Computer Science and Engineering: Georgia Tech Research Corporation

Arijit Raychowdhury

[email protected]

Quantum computing promises to revolutionize the entire paradigm of computing with significant societal impact. Along with advances is research and development, it also requires the next-generation of quantum-trained workforce who will be able to take advantage of the technological changes. This project is a planning grant to determine the requirements for development of an academic hosted and online accessible trapped ion-based quantum computer testbed. A testbed of this nature will give computer scientists access to a functioning quantum computer to test new ideas in the area quantum algorithms. This planning grant would have four goals: 1) determine the desire of the broader quantum computer scientist community in having access to open quantum computer; 2) define the requirements for the minimum number of qubits to have an effective resource for researcher; 3) determine the at what level in the computing stack would researcher like access to the computer; 4) determine the appropriate balance between a computing resource the broad quantum community and the academic nature of the device as a tool to educational and workforce development. <br/> <br/>The approach in this planning grant is to enable the next generation of quantum computing platforms for computer engineering and computer science researchers to perform algorithm research. Georgia Tech Research Institute (GTRI) has demonstrated leadership in developing ion trap quantum system hardware over the last fifteen years. The project will leverage the expertise in this joint effort between GTRI and Georgia Institute of Technology and enable a ten-qubit quantum computing testbed with open source software infrastructure and capability of remote access. It is expected to directly impact the following research vectors: (1) Microarchitecture research to explore topics related to but not limited to physical model driven error correction, resiliency, noisy intermediate state quantum computing models etc. (2) Algorithm research to understand the fundamental advantages and limitations of quantum algorithms from space-time complexity on a practical system with its full software stack. (3) Applications research which will span over engineering as well as science disciplines ? with a focus on CISE related research.<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.

NSFOpen Education ResourcesThe Research University (TRU)

CAREER: Coarse-grained Theory and Simulation of Ion-containing Liquids: Study of Ion Solvation by Polymers and Ionic Liquids and between Nanoparticles: Michigan Technological University

Issei Nakamura

[email protected]

NONTECHNICAL SUMMARY<br/>This CAREER project supports research and education to study properties of liquids containing charged molecules. This project is focused on advancing understanding of how charged molecules, also known as ions, are surrounded by liquid molecules. This solvation process is a crucial step in making novel materials for emerging technologies; the lithium-ion polymer battery is one example. Existing computer simulations are unable to access the scales of length and time required for an adequate understanding of ion solvation.<br/>In this project, the PI will develop simplified computational models, called coarse-grained models, which represent units containing multiple atoms as effective particles with effective interactions. This procedure reduces the computational intensity of simulations making them tractable, but at the expense of some accuracy. This method will enable more straightforward identification of the key parameters and general mechanisms of ion solvation in polymers, which are long chain-like molecules. The project is aimed to advance the current state-of-the-art of molecular simulations. The knowledge acquired in this study will also help to refine existing theory at the level of atoms and associated simulation methods. Using the developed methods, the PI will also study the role of various nanoparticles in ion solvation. <br/><br/>The education component involves framing a pedagogy for both college and high school students, in which students can develop scientific problem-solving skills and cultivate interdisciplinary approaches to problems using computer simulations and visualization. Education objectives will be achieved through utilizing a combination of programming languages and open-source software with an aim to help students visualize mathematical expressions and bridge the gap between practice and theory while enriching their programming skills. The PI also aims to use education innovations developed in this CAREER project to bridge the gap in education between soft- and hard-condensed matter physics. <br/><br/>To extend the reach of the project, the PI and his group will collaborate with a K-12 educator to develop and hold short summer programs on topics related to this research. These programs are designed to provide a taste of soft-matter physics to local secondary school students using open-source software, such as PhET and Physlets, and simulation techniques, along with introductory programming. Ultimately, the aim is to develop, evaluate, and disseminate these outreach program resources. <br/><br/><br/>TECHNICAL SUMMARY<br/>This CAREER project supports research and education to study the thermodynamic and electrochemical properties of ion-containing liquids. When liquid mixtures, polymers, different time and length scales, and significant spatial inhomogeneity of dielectric responses appear together, electrostatic interactions become amazingly intricate making understanding ion-containing liquids challenging.<br/>This study is aimed to provide a deeper understanding of ion solvation at molecular and atomistic scales to enable the design of novel electrochemical materials for next-generation technologies. The PI will focus mainly on the solvation energy of ions and the solvation mechanism in polymers and ionic liquids. The solvation mechanism of nanometer-sized solid bodies, such as metal oxide nanoparticles and quantum dots, will also be investigated with an aim to evaluate the effect of Lifshitz forces. <br/><br/>The PI will investigate the hypothesis that the key factors in determining physical properties of ion-containing liquids are: (1) the strong fluctuation of electrostatic potentials, (2) the spatial inhomogeneity of the dielectric response, (3) the synergy among specific interactions such as hydrogen bonding and aromatic interactions, and (4) van der Waals forces from solid bodies. The complexity of polymers, such as chain architecture and chain connectivity, often makes understanding the physical properties that arise from these factors challenging. To address this issue, the PI will develop coarse-grained molecular simulations by connecting dipolar and quadrupolar monomeric units. The PI will also develop effective force fields between uncharged nanoparticles, which account for the molecular interactions of polyelectrolytes and ionic liquids.<br/><br/>The proposed education plan seeks to frame a pedagogy for soft-matter sciences in physics. The plan will use open-source software such as PhET, Physlets, and LAMMPS that can be executed on standard computers to ensure wide accessibility. The PI?s main aim is to minimize a gradually surging concern in physics education, specifically, ??the gap in education between hard- and soft-condensed matter physics.?? To further the impact of the CAREER project, the PI and his group will hold short summer programs based on the PI?s expertise to provide a taste of soft-matter physics to local secondary schools. Developed and delivered in coordination with a K-12 education specialist, these outreach sessions will use open-source software, such as PhET and Physlets, and associated simulation techniques along with introductory programming. The aim of the outreach component is to develop, evaluate, and disseminate resources for use in similar programs nationwide.<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.

NSFOpen Education ResourcesThe Research University (TRU)

Molecular Crystal Polymorph Prediction: High Accuracy at Lower Computational Cost: University of California-Riverside

Gregory Beran

[email protected]

Professor Gregory Beran of the University of California-Riverside is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop new computational tools that will facilitate the prediction of three-dimensional crystal structures. Knowledge of molecular crystal structures is essential in pharmaceuticals and many other areas of chemistry. Different crystal packing arrangements, or ?polymorphs,? of the same molecule can exhibit vastly different properties. The occurrences of undesirable polymorphs have caused major drug recalls and other serious problems for patients and pharmaceutical manufacturers in the past. The pharmaceutical industry increasingly employs crystal structure prediction to complement their experimental drug formulation efforts and to reduce the potential for polymorphism-related problems. It has recently been discovered that the current theoretical models in widespread use exhibit significant problems for predicting the crystal structures of drug molecules. This project is developing new computational models that correct these weaknesses and improve the reliability with which crystal structures can be predicted. Software developed by this project will be released to the community as free, open-source<br/>software. Beyond the core research, Professor Beran is actively involved in pedagogical efforts to help train next-generation scientists, a large proportion of whom come from low-income, first-generation, and/or traditionally underrepresented minority demographics. <br/><br/>This research occurs in three parts. First, new computationally-practical electronic structure methods for modeling the non-covalent interactions that govern molecular conformation and crystal packing are being developed to enable identification of good initial crystal structures. Second, an approach that combines the strengths of the new electronic structure methods for describing intramolecular interactions with the lower computational costs of density functional theory for modeling intermolecular interactions is being developed to enable improved crystal structure predictions in pharmaceutical compounds. Finally, new lower-cost approximations for handling the vibrational contributions to crystalline stability are being investigated to allow investigation of how temperature affects polymorph stability. This project is developing new computational models that correct weaknesses in current theoretical models and improve the reliability with which crystal structures can be predicted. Software developed by this project will be released to the community as free, open-source software.<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.

NSFOpen Education ResourcesThe Research University (TRU)

CCRI: ENS: Chipyard: University of California-Berkeley

Krste Asanovic

[email protected]

Advanced computing systems lie at the heart of many innovative products, from intelligent earbuds to autonomous vehicles, and these are increasingly built as customized systems-on-a-chip (SoCs). Customized SoCs incorporate a complex mix of general-purpose and customized processing logic, and both software and hardware must be highly tuned to achieve the needed performance and energy efficiency for the target application. Chipyard combines and extends existing community infrastructure components to provide a rich unified framework for research into SoC architecture and implementation, supporting activities ranging from research into new software and new architecture simulation techniques all the way down to test chip fabrication.<br/><br/>Chipyard is an integrated SoC design, simulation, and implementation environment to support research and development of specialized computing systems required to meet new application demands in the face of the slowdown in technology scaling. Chipyard is based around the widely used Rocket Chip SoC generator, which includes RISC-V processors, coherent caches, interconnect, and other IP blocks written in the Chisel HDL. Due to the widespread adoption of RISC-V in both academia and industry, there is extensive software support both in upstream open-source software projects as well as increasingly from commercial software providers. Chisel has a growing community, with a series of Chisel Community Conferences and multiple commercial tapeouts of Chisel-based designs. Chipyard IP modules can also be imported from legacy HDLs. RTL designs are converted into a common intermediate representation, FIRRTL, which supports powerful circuit transformations and experimentation with new hardware design tools. For fast, accurate simulation, Chipyard generates FireSim cloud-FPGA-accelerated simulators. FireSim can simulate entire datacenter racks at the RTL level with only a 100X slowdown. FireSim includes performance monitoring, analysis, and debugging tools to allow high observability of design behavior while running at high simulation speed. Chipyard also includes FireMarshal, a software workload management system that allows complete workloads to be easily packaged and recompiled to match an SoC configuration and execution environment. Chipyard integrates the Hammer modular physical design flow, which supports plugins for different tool chains and process technologies, and automates many tapeout steps. Chipyard will also integrate the Berkeley Analog Generator for mixed-signal and analog blocks. Overall, Chipyard provides an integrated environment where a single SoC description can be used to drive conventional open-source or commercial software RTL simulators, or pushed all the way to GDSII layout using industry-standard CAD tools and/or open-source ECAD tools once available. This proposal will fund further development of Chipyard capabilities, managed releases, and community engagement and outreach.<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.

NSFOpen Education ResourcesThe Research University (TRU)

Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting: Virginia Polytechnic Institute and State University

Cayelan Carey

[email protected]

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software ? FLARE: Forecasting Lake And Reservoir Ecosystems ? that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.<br/><br/>The FLARE forecasting system is novel in its architecture, as it integrates a software-defined virtual distributed infrastructure spanning resources from sensor gateway devices at the edge of the network to cloud computing and storage. FLARE will support the flexible deployment of software in close proximity to water quality sensors in lakes and reservoirs, and in cloud resources for end-to-end data acquisition and processing. FLARE interconnects its distributed resources through a virtual private network to ensure data integrity and privacy in communications, and supports a flexible model applicable across a variety of lakes and reservoirs. Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, widely-used open-source software frameworks in a manner that lowers the barrier to the deployment and management of ecological forecasting workflows by ecologists. Importantly, this project?s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forecasts will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other ecosystems. This project will build on and expand an existing program for cross-disciplinary teaching tools and research exchanges of undergraduate and graduate students to provide training at the intersection of computer science, freshwater science, and ecosystem modeling. Ultimately, this project will develop scalable, robust, secure workflows that will advance the capacity, practice, and training opportunities for ecological forecasting worldwide. Results from this project can be found at http://flare-forecast.org<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.

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