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Jetstream 2: Accelerating Science and Engineering On-Demand

Sponsor: Indiana University
David Hancock [email protected] (Principal Investigator)
Gwen Jacobs (Co-Principal Investigator)
Matthew Vaughn (Co-Principal Investigator)
Marlon Pierce (Co-Principal Investigator)
Nirav Merchant (Co-Principal Investigator)
Award Number: 2005506


The frontiers of science are rapidly evolving in regard to availability of data to be analyzed and the breadth and variety of analytical tools that researchers use. To effectively analyze and make sense of this ever-growing cache of information, and to make it possible to leverage new artificial intelligence tools for research, researchers need on-demand, interactive, and programmatic cyberinfrastructure (CI) delivered via the cloud. Jetstream 2 is a system that will be easy to expand and reconfigure, and capable of supporting diverse modes of on-demand access and use, The system will also revolutionize the national cyberinfrastructure (CI) ecosystem by enabling ?AI for Everyone? with virtual GPU capabilities and widespread outreach through the five partners, led by Indiana University. The project promises to enable the research community to use a greater variety of computational resources and to expand its reach into student populations, drawn from a broad range of disciplines, thus contributing to building the future STEM workforce.

Jetstream 2 will be an 8 PetaFLOPS (PFLOPS) cloud computing system using next-generation AMD ?Milan? CPUs and the latest NVIDIA Tensor Core GPUs with 18.5 petabytes (PB) of storage. Consisting of five computational systems, Jetstream 2?s primary system will be located at Indiana University, with four modest regional systems deployed nationwide at Arizona State University (ASU), Cornell University, the University of Hawaii (UH), and the Texas Advanced Computing Center (TACC). Additional partnerships with the University of Arizona, Johns Hopkins University, and University Corporation for Atmospheric Research (UCAR) will contribute to Jetstream 2’s unparalleled usability and support for a broad range of scientific efforts.

The Jetstream team has been at the forefront of training the research community to transition from batch computing methods to adopt cloud-style usage. Jetstream 2 will continue this path and will ease the transition between academic and commercial cloud computing. Some of the advanced features include push-button virtual clusters, advanced high-availability science gateways services (including commercial cloud integration), federated authentication for JupyterHubs, bare metal and virtualization within the same system through programmable CI, support for on-demand data intensive workloads in addition to on-demand computation, high-performance software-defined storage, and advanced multi-platform orchestration capabilities.

Jetstream 2 will have far-reaching societal benefits. As enhanced educational infrastructure, it will serve more students, from traditional undergraduates to domain-science experts desiring training in computational techniques, than any other NSF-funded CI resource. These students will be better equipped to fully participate in the evolving STEM workforce. In addition to enabling new research, discovery, and innovation across many disciplines, Jetstream 2 will advance the national CI ecosystem and extend the broader impacts of existing NSF investments. Jetstream 2’s ?Core Services? will demonstrate a practical model of distributed cloud computing that will give academic institutions an incentive to invest their own funds in new advanced CI facilities. Although modest in scale, these facilities will represent the state of the art in reconfigurable computing. The implementation of Jetstream 2 will also demonstrate that colleges and universities can invest sustainable amounts of their own funds in highly-effective, flexible CI resources that generate a significant return on investment. In sum, Jetstream 2 will transform the national CI landscape and greatly benefit the nation.

AwardsMovement ThinkingNSFThe Research University

Collaborative Research: Development of Language-Focused Three-Dimensional Science Instructional Materials to Support English Language Learners in Fifth Grade

Sponsor: Stanford University
Award Number: 1502507
Guadalupe Valdes [email protected] (Principal Investigator)


This project was submitted to the Discovery Research K-12 (DRK-12) program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. The project is responsive to the societal challenges emerging from the nation’s diverse and rapidly changing student demographics, including the rise of English language learners (ELLs), the fastest growing student population (see, for example, “U.S. school enrollment hits majority-minority milestone”, Education Week, February 1, 2015). ELLs have grown exponentially: 1 in 5 students (21%) in the nation spoke a language other than English at home in 2011. The project’s main purpose is to develop instructional materials for a year-long, fifth grade curriculum for all students, including ELLs. The planned curriculum will promote language-focused and three-dimensional science learning (through blending of science and engineering practices, crosscutting concepts, and disciplinary core ideas), aligned with the Framework for K-12 Science Education (National Research Council, 2012), the Next Generation Science Standards (Achieve, 2013), and the Conceptual Framework for Language use in the Science Classroom (Lee, Quinn & Valdés, 2013). The grade-level science content will target topics, such as structure and properties of matter, matter and energy in organisms and ecosystems, and Earth’s and space systems, with engineering design embedded in each topic. The language approach will emphasize analytical science tasks aimed at making sense of and constructing scientific knowledge; and receptive (listening and reading) and productive (speaking and writing) language functions. Products and research results from this project will help to reduce the science achievement gaps between ELLs and non-ELLs, and enable all students to attain higher levels of proficiency in subsequent grade levels.

After the curriculum has been developed and field-tested during Years 1-3, a pilot study will be conducted in Year 4 to investigate promise of effectiveness. Using a randomized controlled trial design, the pilot study will address three research questions: (1) What is the impact of the intervention on science learning and language development for all students, including ELLs and former ELLs?; (2) What is the impact of the intervention on teachers’ instructional practices?; and (3) To what extent are teachers able to implement the instructional materials with fidelity? To address research question 1, a sequence of multi-level models (MLMs) in which the posttest score for each student measure (the state/district science test score, and the science score and the language score on the researcher-developed assessment) will be regressed on a dummy variable representing condition (treatment or control) and pretest covariates. To examine whether the intervention is beneficial for students of varying levels of English proficiency, subgroup analyses will be conducted comparing ELLs in the treatment group against ELLs in the control group; former ELLs in the treatment group against former ELLs in the control group; and non-ELLs in the treatment group against non-ELLs in the control group, using the same MLMs. Exploratory analyses will be employed to examine the extent to which the level of English proficiency moderates the impact of the intervention on ELLs. To address research question 2, a 2-level model (teachers as level-1, and schools as level-2) in which the post-questionnaire scale score will be regressed on a dummy variable representing condition (treatment or control) will be conducted. To address research question 3, plans are to analyze ratings on coverage, adherence, and quality of instruction from classroom observations, along with ratings on program differentiation and participant responsiveness from the implementation and feedback form.

AwardsNSFThe Research University

Learning From Diverse Populations: A Complexity-Theoretic Perspective

Sponsor: Stanford University
Omer Reingold [email protected] (Principal Investigator)
Award Number: 1908774


Despite the successes of machine learning at complex prediction and classification tasks (such as which add a reader will click? or which word a speaker pronounced?), there is growing evidence that “state-of-the-art” predictors can perform significantly less accurately on minority populations than on the majority population. Indeed, a notable study of three commercial face recognition systems, known as the “Gender Shades” project demonstrated significant performance gaps across different subpopulations at natural classification tasks. Systematic errors on underrepresented subpopulations limit the overall utility of machine-learned prediction systems and may cause material harm to individuals from minority groups. To address accuracy disparity and systematic biases throughout machine learning, the project pursue a principled study of learning in the presence of diverse populations. The project puts high value on education, service to the research community, and wide dissemination of knowledge. The research activities will be accompanied by and integrated with curriculum development, research advising (for students at all levels), service, and outreach to other scientific communities and in popular writing. In addition, in the age of machine-learning and big data, the project’s societal impact is twofold: making sure that algorithms work for everyone but also making sure algorithms uncover all potential talent, which exists in all communities.

The project combines theoretical and empirical investigations to develop algorithmic tools for mitigating systematic bias across subpopulations and to answer basic scientific questions about why discrepancy in accuracy across subpopulations emerges in the first place. Specifically, the project aims to ask and resolve questions that arise in the context of learning from diverse populations along three main axes: (1) Improving predictions for underrepresented populations: Can learning algorithms be developed that provably do not overlook significant subpopulations, (2) Representing individuals to improve the ability to audit and repair models, (3) Understanding the causes for biases in machine common learning models and algorithms.

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.

AwardsFuture of WorkInventXRMovement ThinkingNSFThe Research University

Collaborative Research: Enhancing Human Capabilities through Virtual Personal Embodied Assistants in Self-Contained Eyeglasses-Based Augmented Reality (AR) Systems

Sponsor: University of North Carolina at Chapel Hill
Award Number: 1840131
Henry Fuchs [email protected] (Principal Investigator)
Jan-Michael Frahm (Co-Principal Investigator)
Mohit Bansal (Co-Principal Investigator)
Felicia Williams (Co-Principal Investigator)
Prudence Plummer (Co-Principal Investigator)


The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by NSF. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim.

This award supports basic research underpinning development of an eyeglass-based 3D mobile telepresence system with integrated virtual personal assistant. This technology will increase worker productivity and improve skills. The system automatically adjusts visual focus and places virtual elements in the image without eye strain. The user will be able to communicate to the system by speech. The system also uses sensors to keep track of the user’s surroundings and provide the relevant information to the user automatically. The project will explore two of the many possible uses of the system: amplifying a workers capabilities (such as a physical therapist interacting with a remote patient), and accelerating post-injury return to work through telepresence (such as a burn victim reintegrating into his/her workplace). The project will advance the national interest by allowing the right person to be virtually in the right place at the right time. The project also includes an education and outreach component wherein undergraduate and graduate students shall receive training in engineering and research methods. Course curriculum at Stanford University and the University of North Carolina at Chapel Hill shall be updated to include project-related content and examples.

This project comprises fundamental research activities needed to develop an embodied Intelligent Cognitive Assistant (GLASS-X) that will amplify the capabilities of workers in a way that will increase productivity and improve quality of life. GLASS-X is conceived of as an eyeglass-based 3D mobile telepresence system with integrated virtual personal assistant. Methods include: body and environment reconstruction (situation awareness) from a fusion of images provided by an eyeglass frame-based camera array and limb motion data provided by inertial measurement units; fundamental research on adaptive focus displays capable to reduce eye strain when using augmented reality displays; dialog-based communication with a virtual personal assistant, including transformations from visual input to dialog and vice versa; human subject evaluations of GLASS-X technology in two workplace domains (remote interactions between a physical therapist and his/her patient; burn survivor remote return-to-work). This research promises to push the state of the art in core areas including: computer vision; augmented reality; accommodating displays; and natural language and dialogue models.

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.

AwardsMovement ThinkingNSFThe Research University

Building a Framework for Developing and Evaluating Contextualized Items in Science Assessment (DECISA)

Sponsor: Stanford University
Award Number: 1710657
Investigator(s): Maria Araceli Ruiz-Primo [email protected] (Principal Investigator)


This collaborative project involving the University of Colorado at Denver and the University of Washington at Seattle in conjunction with Facet Innovations, will build a framework for addressing the use of contextualized items in the assessment of STEM learning. The primary goal is to systematically investigate the effects of characteristics of contextualized items on student performance to strengthen practices in science assessments, ensure fairness in science testing, and increase support for both assessment and instructional purposes. Test items with contexts are called contextualized items which include supplemental information that precedes or follows a test item question. Such information may include a description of a lab setup, a natural phenomenon, or a practical problem often depicted as a scenario, background, vignette, or cover story. The project findings will help to understand how students make sense of contextualized items focusing on complex scientific concepts that they usually encounter in science assessments. Currently, contextualized items are constructed from either conventional wisdom or non-contextualized item writing rules. Such items could mislead students to attend to irrelevant information or interfere with the targeted construct, and, therefore lead to inaccurate inferences about student learning. This project will develop a framework for developing items to help address this problem. Approximately, 70 classroom teachers and 4800 students, in secondary grades, will participate in the study.

The project will offer a theoretical articulation of the characteristics of contextualized items and empirically test the effects on student performance. It seeks to address four gaps in the literature on contextualized items: (1) insufficient knowledge about how to conceptualize construct-relevant contextualized items; (2) lack of research on contextualized items in science; (3) lack of research that systematically studies the characteristics of contexts and evaluates their effects on student performance; and (4) the need for studies that examine differential effects related to subgroups of students to gain a greater clarity of what types of contexts affect whom. The research design and data analyses will be guided by three research questions: (1) what are critical context characteristics that may affect student performance and should therefore be considered when developing science test items? (2) what are context characteristics associated with construct-relevant variance? (3) what context characteristics are associated with differential student performance patterns due to gender, ELL status, and socioeconomic-status variables?

The project will take place over three years through a two phase process. During Phase 1 the project will refine a proposed theoretical framework that will identify the item context characteristics and articulate the item development guidelines. During Phase 2, the goal is to apply the framework by selecting, revising, and developing science items with varying profiles of contexts; conduct field tests of the items; and perform a range of psychometric and statistical procedures with test scores, and qualitative analyses of students’ cognitive interview responses and teacher interviews. Items resulting from this process will aim to evoke students’ stored knowledge relevant to the content and/or process skills targeted. The project will involve a team of researchers specialized in assessment development and validation, science education, content knowledge, linguists, and expert classroom teachers. The item development approach and items generated from this project will have immediate implications for researchers and practitioners in science education nationally and internationally.


Stanford University Thingpedia Open Source Research: Autonomy and Privacy with Open Federated Virtual Assistants

Sponsor: Stanford University
Award Number: 1900638
Monica Lam [email protected] (Principal Investigator)
James Landay (Co-Principal Investigator)
Michael Bernstein (Co-Principal Investigator)
Christopher Manning (Co-Principal Investigator)
David Mazieres (Co-Principal Investigator)


Virtual assistants, and more generally linguistic user interfaces, will become the norm for mobile and ubiquitous computing. This research aims to create the best open virtual assistant designed to respect privacy. Instead of just simple commands, virtual assistants will be able to perform complex tasks connecting different Internet-of-Things devices and web services. Also, users may decide who, what, when, and how their data are to be shared. By making the technology open-source, this research helps create a competitive industry that offers a great variety of innovative products, instead of closed platform monopolies.

This project unifies all the internet services and “Internet of Things” (IoT) devices into an interoperable web, with an open, crowdsourced, universal encyclopedia of public application interfaces called Thingpedia. Resources in Thingpedia can be connected together using ThingTalk, a high-level virtual assistant language. Another key contribution will be the Linguistic User Interface Network (LUInet) that can understand how to operate the world’s digital interfaces in natural language. LUInet uses deep learning to translate natural language into ThingTalk. Privacy with fine-grain access control is provided through open-source federated virtual assistants. Transparent third-party sharing is supported by keeping human-understandable contracts and data transactions with a scalable blockchain technology.

This research contributes to the creation of a decentralized computing ecosystem that protects user privacy and promotes open competition. Natural-language programming expands the utility of computing to ordinary people, reducing the programming bottleneck. All the technologies developed in this project will be made available as open source, supporting further research and development by academia and industry. Thingpedia and the ThingTalk dataset will be an important contribution to natural language processing. The large-scale research program for college and high-school students, with a focus on diverse students, broadens participation and teaches technology, research, and the importance of privacy. All the information related to this project, papers, data, code, and results, are available at until at least 2026.

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.

AwardsNSFThe Research University

Educating Generative Designers in Engineering

Sponsor: University of Arkansas
Zhenghui Sha [email protected] (Principal Investigator)
Charles Xie (Co-Principal Investigator)
Onan Demirel (Co-Principal Investigator)
Molly Goldstein (Co-Principal Investigator)
Darya Zabelina (Co-Principal Investigator)


With support from NSF’s Accelerating Discovery program, this project aims to re-envision undergraduate engineering education to include generative design. Generative design is a transformative design technology that uses open-ended artificial intelligence algorithms to arrive at solutions for engineering problems. Generative design software can be freed from preconceived ideas or past solutions. As a result, it allows exploration of a wider variety of potential solutions, with the goal of arriving at an optimal solution in partnership with the human engineer. The proposed project will support the development of open-source educational tools for teaching and learning generative design. These tools will be based on existing computer-assisted design and engineering software, and will include a set of project modules to guide students through authentic design problems. The software and associated design problems will be pilot tested by students at thirteen institutions, including community colleges, Historically Black Colleges and Universities, liberal arts colleges, and public universities. Information from these pilots will be used iteratively to refine the software and teaching approach. This project represents a novel application of artificial intelligence to engineering that could augment the creativity and productivity of the engineering workforce of the future.

The overall goal of this project is to facilitate the teaching and learning of generative design at the undergraduate level. To accomplish this goal, the University of Arkansas, the University of Illinois at Urbana-Champaign, Oregon State University, and the Concord Consortium will collaborate to define, implement, and disseminate generative design tools and projects for use in undergraduate courses. Research questions from three perspectives will drive the project: 1) Theoretical perspective: What are the essential elements of generative design thinking that students must acquire so they can work effectively at the human-technology frontier in engineering? 2) Practical perspective: To what extent and in what ways can the curriculum and materials support the learning of generative design as indicated by students’ gains in generative design thinking? and 3) Affective perspective: To what extent and in what ways can artificial intelligence affect the professional formation of engineers as indicated by the changes of students’ interest and self-efficacy in engineering? To answer these questions, interdisciplinary research that integrates the perspectives and knowledge in engineering design, computer science, learning science, and workforce development will be conducted. The project will involve more than 1,000 students at 13 institutions around the country. The research will include data from demographic surveys, questionnaires, self-efficacy measures, design reports, screencast videos, classroom observations, and participant interviews. The materials developed by the project will be open source, including an open-source tool for teaching and learning generative design and a set of project-based learning modules that guide use of the tool to solve authentic design problems in architectural engineering and energy engineering. The products of this project are expected to equip students with essential skills and mindsets needed to master using artificial intelligence approaches in contemporary engineering practices.

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.

AwardsInventXRMovement ThinkingNSFThe Research University

A Partnership to Adapt, Implement and Study a Professional Learning Model and Build District Capacity to Improve Science Instruction and Student Understanding

Sponsor: University of California-Berkeley
Emily Weiss [email protected] (Principal Investigator)
Craig Strang (Co-Principal Investigator)
Award Number: 1720894


The Lawrence Hall of Science (the Hall) and Stanford University teams have previously developed and tested the efficacy of a program of Professional Learning (PL) which is focused on improving teachers’ ability to support students’ ability to engage in scientific argumentation. Key components of the PL model include a week-long summer institute and follow-up sessions during the academic year that incorporate additional pedagogical input, video reflection, and planning time. In this project, the Hall and Stanford are working in partnership with the Santa Clara Unified School District (SCUSD) to adapt the PL model based on the District’s objectives and constraints, to build the capacity of teacher leaders and a program coordinator to implement the adapted PL program. This will enable the District to continue to adapt and implement the program independently at the conclusion of the project. Concurrently, the project is studying the adaptability of the PL model and the effectiveness of its implementation, and is developing guidelines and tools for other districts to use in adapting and implementing the PL model in their local contexts. Thus, this project is contributing knowledge about how to build capacity in districts to lead professional learning in science that addresses the new teaching and learning standards and is responsive to the needs of their local context.

This project is examining the sustainability and scalability of a PL model that supports the development of teachers’ pedagogical content knowledge and instructional practices, with a particular focus on engaging students in argument from evidence. Results from the Hall and Stanford’s previous research project indicate that the PL model is effective at significantly improving teachers’ and students’ classroom discourse practices. These findings suggest that a version of the model, adapted to the context and needs of a different school district, has the potential to improve the teaching of science to meet the demands of the current vision of science education. Using a Design-Based Implementation Research approach, this project is (i) working with SCUSD to adapt the PL model; (ii) preparing a district project coordinator and cadre of local teacher leaders (TLs) to implement and further adapt the model; and (iii) studying the adaptation and implementation of the model. The outcomes will be: a) a scalable PL model that can be continually adapted to the objectives and constraints of a district; b) a set of activities and resources for the district to prepare and support the science teacher leaders who will implement the adapted PL program internally with other teachers; and c) knowledge about the adaptations and resources needed for the PL model to be implemented independently by other school districts. The team also is researching the impact of the program on classroom practices and student learning.

Please report errors in award information by writing to: [email protected]

AwardsNSFThe Research University

NeuroTech – Bringing Technology to Neuroscience

Sponsor: Stanford University
Award Number: 1828993
Eduardo Chichilnisky [email protected] (Principal Investigator)
James McClelland (Co-Principal Investigator)
Jin Hyung Lee (Co-Principal Investigator)
Surya Ganguli (Co-Principal Investigator)


Deciphering how the brain works could have untold impacts on medicine, technology, commerce, and our understanding of ourselves. For example, advances in neurotechnology could lead to brain-machine interfaces to overcome sensory impairments and loss of movement due to neurodegenerative disease. Many of the most important advances in neuroscience have required interaction with technical fields such as physics, electrical and chemical engineering, bioengineering, statistics, and computer science, and this will increasingly be the case as the field advances. However, the path for top students from these disciplines to enter the field of neuroscience has always been challenging because they lack the appropriate background and awareness of key questions and technological limitations in the field. This National Science Foundation Research Traineeship (NRT) award to Stanford University will accelerate fundamental developments in neuroscience by attracting promising young talent from these technical disciplines to neuroscience and training them to be leaders in the field. The program will allow students to apply technological developments in diverse fields to the most important problems in neuroscience today and train a new generation of neuroscientists who will bring these technologies to fruition in academia, medicine, and the private sector. The project anticipates training thirty (30) PhD students, including twelve (12) funded trainees, from physics, electrical and chemical engineering, bioengineering, materials science, computer science, and other technical fields.

This traineeship program consists of a novel integrated curriculum of coursework, internship and training experiences, and outreach to achieve its goals. The program will emphasize training for acquiring and analyzing vast data sets, enabling an understanding of nervous system circuitry at a scale that was unimaginable just a few years ago, and connecting the novel data to Stanford’s strength in theory, inference from large data sets, and computational modeling. The program will introduce a rigorous multi-year curriculum for trainees, building on their home-discipline training and allowing them to collaborate with each other and with the members of the Neurosciences PhD program. Training will leverage the highly successful Stanford ADVANCE program that supports new PhD students with a special summer program prior to the start of graduate training, and build on it with several approaches customized to this program. The program will be specifically designed to optimize trainee preparation for a career in academia or in a technology industry setting, utilizing internship placements with both startups and established corporations.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

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.

Please report errors in award information by writing to: [email protected]

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