Artificial Intelligence (AI) has emerged as a foundational technology that is profoundly reshaping society. With accelerating advances in a wide array of capabilities including natural language processing, computer vision, and machine learning, AI is quickly finding broad applications in every sector of society. Critically, AI holds significant transformative potential for improving human learning. This National Artificial Intelligence (AI) Research Institutes proposal centers on the establishment of the Institute for an AI-Engaged Future of Learning. Driven by a learner-centered vision of the potential of AI-augmented learning, the ENGAGE AI Institute will conduct (1) foundational AI research on natural language technologies, computer vision, and machine learning and (2) use-inspired AI research on AI-augmented learning, thereby creating learning experiences specifically designed to promote student engagement in formal and informal learning settings. The ENGAGE AI Institute brings together an exceptional interdisciplinary team spanning five organizations with deep expertise in AI and education, including four universities (North Carolina State University, the University of North Carolina at Chapel Hill, Vanderbilt University, and Indiana University) and Digital Promise, which will serve a “nexus” role for the Institute. The Institute will create AI-augmented learning technologies with specific foci on supporting two forms of engaging collaborative inquiry learning experiences: collaborative learning (problem solving and learning that play out in groups) and embodied learning (learning processes that are grounded in the interplay between the body, movement, and senses). The Institute will focus on AI-driven narrative-centered learning environments that create engaging story-based problem-solving experiences to support collaborative inquiry learning. The Institute will explore AI-augmented learning that operates at three levels: individuals, small groups, and larger groups within a range of educational contexts (e.g., classrooms, museums).

Digital innovation is the source of competitiveness and value creation for many types of businesses. The universal desire for rapid digital innovation demands efficient reuse of software code building blocks, which has increased the dependence upon open source and third-party libraries and tools that comprise the software supply chain. Adversaries have moved from finding and exploiting vulnerabilities in end products to a new generation of supply chain attacks where attackers aggressively implant malicious code directly into artifacts in the supply chain and find their way into build and deployment pipelines. Digital innovation depends upon confidence in the software supply chain. As such, our research will enable the following vision: The software industry can rapidly innovate with confidence in the security of their software supply chain. The challenge of software supply chain security has recently received significant interest from industry and government. However, discussions with key stakeholders indicate that the state-of-the-art is preliminary, motivating scientific research to address the underlying fundamental challenges that will limit the practical success of existing approaches. We tackle the challenges of secure software supply chain through three thrusts: prevention, detection, and response, with an explicit objective of moving toward preventing security failures. For each thrust, we consider five hard security problems: (1) Scalability and Composability, such as detecting malicious commits and hardening containers; (2) Policy-governed Secure Collaboration, such as effective use of Software Bill of Materials; (3) Predictive Security Metrics, such as measuring the exploitability of vulnerabilities; (4) Resilient Architectures, such as isolation and sandboxing of components; and (5) Human Behavior, such as studying how to make software developers make more secure decisions. The project will impact the software industry by engaging with current industry players/community, enabling their participation in our research thrusts. Additionally, the project will involve educating the next generation of engineers to eradicate software supply chain security issues and training current employees to make them aware of these issues to help reduce them. To solve these challenging issues, we have created a multidisciplinary proposing team committed to diversity.

The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.

As the correlation of data gains importance in many domains, high-dimensional tensors are becoming an ever more important object to represent data and analyze its inherit properties. Tensor networks targeting very high-dimensional data and extracting physically meaningful latent variables are underdeveloped because of their complicated mathematical nature, extremely high computational complexity, and more domain-dependent challenges. This work proposes Cross-layer cooRdination and Optimization for Scalable and Sparse Tensor Networks.

The North Carolina Partnership for Cybersecurity Excellence (NC-PaCE) is a coalition of industry, government, and educational organizations committed to outpacing attackers through a partnership of cybersecurity excellence in research, education, and service. The Secure Computing Institute at North Carolina State University (NCSU) leads NC-PaCE organizations to address a growing cybersecurity workforce gap through educational opportunities; to protect financial assets and intellectual property (IP); and to drive economic growth of North Carolina’s public agencies and private sector businesses through cybersecurity research and service. Synergistically, NC-PaCE supports entrepreneurial and economic growth in North Carolina. NC-PaCE partners are NSA National Centers of Academic Excellence in Cybersecurity (CAE-C) educational institutions and include: NCSU, East Carolina University (ECU), Forsyth Technical Community College (FTCC), North Carolina A&T (NCAT), UNC-Charlotte (UNC-C), UNC-Wilmington (UNCW), and Wake Technical Community College (WTCC). The educational institutions will work together to close the cybersecurity talent gap highlighted in the Cyberseek (https://www.cyberseek.org) Supply/Demand Heat Map. Cyberseek is an organization funded by the National Initiative for Cybersecurity Education (NICE), a program of the National Institute of Standards and Technology (NIST) to provide detailed, actionable data about supply and demand in the cybersecurity job market. Based upon Cyerseek’s data, North Carolina is third in the country in terms of supply/demand ratio of cybersecurity workers. The need for cybersec urity-trained professionals is real in North Carolina.

Educating the next generation of cybersecurity professionals is a critical need for the State of North Carolina and the United States. We are utilizing our expertise in cybersecurity research to prepare undergraduate and Masters computer science students at NC State for cybersecurity jobs. Scholarship for Service (SFS) will provide students from North Carolina and the United States, especially from underrepresented groups, the opportunity to receive a high quality cybersecurity focused degree. SFS students will be part of a larger cohort of cybersecurity students who will participate in supplemental activities, events, and conferences as part of their educational experience.

Developing adaptive instruction for teams requires a new generation of Adaptive Instructional Systems that can accurately assess team behaviors in real-time. To effectively adapt tutoring to the complex dynamics of teams calls for the creation of computational models that can operationalize and assess team performance and deliver coaching and feedback to team members as they complete simulated training events. Recent advancements in deep learning-driven natural language processing and reinforcement learning offer significant promise for achieving these capabilities. The goal of this project is to develop tools and methods that can be used by team training researchers to automatically analyzing team communication data and devise tutorial planners that can deliver run time feedback during team training tasks in synthetic environments. In particular, the project will (1) investigate how advances in deep learning-driven natural language processing can be leveraged to analyze team discourse in order to help researchers automatically assess team communication and team performance and (2) investigate how data-driven machine learning approaches can be leveraged to devise tutorial planning models that can automatically deliver run-time feedback during team training tasks in simulated environments.

This project will develop generalizable data-driven tools that addresses the conceptually and practically complex activity of constructing adaptive support for individualized learning in STEM domains.

It has long been recognized that drawing can be a powerful approach to learning. Learning-by-drawing activates a complex set of cognitive processes that requires students to deeply engage with a subject matter. The project centers on the design, development, iterative refinement, and investigation of a sketch-based science learning environment. Specifically, the project will focus on the development and piloting of a sketch-based science learning environment to support students’ conceptual understanding of science with an emphasis on modeling. The project will culminate in a pilot study to investigate the effectiveness of the sketch-based learning environment for improving students’ factual understanding, their inferential understanding, and their ability to engage in science modeling. By utilizing a mixed methods approach integrating quantitative and qualitative work with learning analytics, it is anticipated that the project will yield theoretically-driven, empirically-based advances in sketch-based science learning environments that significantly improve conceptual understanding of science in upper elementary students.

With the rapidly growing recognition of the role that computer science is playing in every aspect of society, enrollments in introductory computer science courses are increasing at an unprecedented pace. As a result of this phenomenal growth, departments of computer science are seeing extraordinary demand for introductory computer science courses. The accelerating growth in enrollments poses significant challenges for introductory programming instructors, who must teach increasingly larger classes while providing effective, engaging learning experiences for students. The overarching objective of this project is to develop an introductory programming teaching support environment, INSIGHT, that will enable instructors to readily understand their students’ progress through introductory computer science coding activities. INSIGHT will fundamentally change classroom dynamics by supporting both students and instructors.

The overarching objective of this project is to investigate how explanation-based classroom response systems can significantly improve student learning in STEM undergraduate education. It has been widely demonstrated that students who engage in self-explanation learn much more effectively than students who do not engage in self-explanation. By explaining concepts and examples as they learn, students trigger the self-explanation effect, which causes them to actively probe their own understanding, to learn much more deeply. However, students in undergraduate STEM courses have limited opportunity to engage in self-explanation. Building on our prior NSF-supported research on natural language processing-based STEM learning environments, we will investigate student learning in undergraduate STEM classrooms with an explanation-based classroom response system. The system will fundamentally change classroom dynamics by supporting both students and instructors. It will support students by instantly providing realtime formative assessment of their explanations. It will support instructors by instantly providing a summary and analysis of students’ explanations in aggregate, which will enable instructors to make immediate adjustments to pedagogy. Together, these benefits will synergistically lead to improved student learning and stronger student engagement in STEM classrooms.

The U.S. Army???s Force Modernization strategy highlights the critical role synthetic training will play in transforming Soldiers to operate as a multiple domain force. A key affordance of synthetic training environments is their capacity to support competency-based experiential learning (CBEL), which prescribes an active approach to learning and expertise development that incorporates adaptive instruction and intelligent tutoring capabilities. Although synthetic training environments show great promise for supporting CBEL, there is a lack of guidance on how synthetic training experiences should be integrated into Army schoolhouse curricula to support competency development and experiential exposure. To maximize the effectiveness of CBEL, synthetic learning experiences need to be dynamically crafted to support individual learning needs and skill development. Simulation-based training scenarios can offer trainees valuable experiences but are resource-intensive to create, and in most cases, schoolhouses have a limited supply of scenarios that they can utilize for a particular course. Competency-based scenario generators offer considerable promise for addressing these challenges by tailoring synthetic training experiences to the needs of individual learners in support of CBEL. Competency-based scenario generators can dynamically shape training experiences, scenario events, unit behaviors and states, and virtual environments in order to support CBEL. Scenario generators can leverage recent advances in machine learning to provide data-driven approaches to support competency-driven training. Recognizing the opportunity introduced by recent advances in machine learning and data-driven scenario generation, the proposed project will investigate how we can devise generalizable, data-driven scenario generation models that dynamically generate training scenarios that achieve target learning objectives to support CBEL in Army schoolhouses. We will design and develop the CompGen competency-based scenario generation framework and demonstrate its data-driven capabilities for supporting CBEL in an institutional training setting.

Stroke is a leading cause of motor disability. A majority of stroke survivors exhibit upper and lower limb motor impairments, ranging from incapability of reaching and grasping objects to limited ambulation. The objective of this project is to develop a personalized, community-based rehabilitation system to improve daily functions of stroke survivors. The system will include three essential components – a nanomaterial-enabled multifunctional wearable sensor network to monitor arm and leg functional activities; a low-power data acquisition, processing, and transmission protocol; and a user interface (i.e., smart phone APP) to communicate training outcomes to the users and clinicians and receive feedback from the users and clinicians. The proposed community-based rehabilitation system will enable personalized, continuous rehabilitation during daily activities.

Recent years have seen a growing recognition of the national STEM workforce shortage. Although problems abound in all STEM disciplines, the shortage is particularly acute in information and communications technology. This is especially true in artificial intelligence (AI), a field of computer science that focuses on the design of computing systems that solve problems involving human-like capabilities including reasoning, learning, and natural language. Engaging middle-grade students, especially those from underserved populations, in artificial intelligence through the creation of lifelike AI for digital games offers a promising approach to encouraging students to pursue innovative computing careers. The AI Play project will engage students in a broad range of computing activities centered on creating AI for games. The project will see the development of a learning environment and curriculum that introduces artificial intelligence into middle school emphasizing connections to the CSTA K-12 Computer Science Standards. The AI Play project will host a series of five-day camps for underserved populations where students will engage in hands-on learning activities under the guidance of teachers and undergraduate computer scientists, who will serve as mentors and role models as the students engage in artificial intelligence, while designing and developing AI for games. The final year of the project will see an evaluation of the AI Play program and its impact on students’ learning and interest in artificial intelligence.

The Institute for Robust Quantum Simulation will focus on using quantum simulation to gain insight into—and thereby exploit—the rich behavior of complex quantum systems. Combining expertise from researchers in computer science, engineering, and physics, our team will address the challenge of robustly simulating classically intractable quantum systems of practical interest, and verifying the correctness of the simulation result.

The aim of this project is to achieve early, rapid, and precise detection of harmful downy mildew on cucurbit plants to enhance crop health and production. This objective will be accomplished by employing quantum sensing-enabled spectroscopy, which utilizes entangled photons and a quantum machine learning receiver. The resulting quantum sensing device will be incorporated into a robotic land rover for testing in North Carolina's cucurbit fields.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world and in the US. Climate change is exacerbating weather events that affect crop production and food access for vulnerable areas. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover and evolution of new pathogen genetic lineages. Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance and data analytics to inform decisions and prevent spread. In order to tackle these grand challenges, a new set of predictive tools are needed. In the PIPP Phase I project, our multidisciplinary team will develop a pandemic prediction system called “Plant Aid Database (PAdb)” that links pathogen transmission biology, disease detection by in-situ and remote sensing, genomics of emerging pathogen strains and real-time spatial and temporal data analytics and predictive simulations to prevent pandemics. We plan to validate the PAdb using several model pathogens including novel and host resistance breaking strains of lineages of two Phytophthora species, Phytophthora infestans and P. ramorum and the cucurbit downy mildew pathogen Pseudoperonspora cubensis Adoption of new technologies and mitigation interventions to stop pandemics require acceptance by society. In our work, we will also characterize how human attitudes and social behavior impact disease transmission and adoption of surveillance and sensor technologies by engaging a broad group of stakeholders including growers, extension specialist, the USDA APHIS, Department of Homeland Security and the National Plant Diagnostic Network in a Biosecurity Preparedness workshop. This convergence science team will develop tools that help mitigate future plant disease pandemics using predictive intelligence. The tools and data can help stakeholders prevent spread from initial source populations before pandemics occur and are broadly applicable to animal and human pandemic research.

This project will create a general-purpose, open-access, and programmable quantum network prototype for the quantum information science and engineering (QISE) community to experiment new quantum technologies and train teachers and students. The key applied methodology is virtualization that permits rapid and flexible experimentation via agile software controls, without resorting to daunting hardware modifications. The research team initiates a research agenda consisting of three thrusts, namely re-designing key quantum components, developing communication protocols, and implementing the prototype in the testbed. A new curriculum based on this prototype will be created and disseminated to train a large body of college students.

Fingerprinting has been a known threat to web privacy for over a decade. Yet, automated detection of fingerprinting methods and scripts has been lacking the properties for protecting web users from such an evolving web threat. Our proposed work aims to provide novel detection methods for browser fingerprinting both at its core, the browser and the evolution of its APIs, and at the page level, via dynamic analysis ofJavaScript. We also propose developing countermeasures that are capable of performing more fine-grained blocking not only at the script level, but also at the API level where an instance of a script/API will be blocked depending on inferring the underlying intent behind executing the script or accessing the API.

With the increasing penetration of behind-the-meter solar and energy storage, it is favored to leverage recent advances in artificial intelligence to enhance the accuracy of net-load forecasting, the observability of net-load variability, and the understanding of the coupling between net-load and demand response potentials. The proposed project will develop two models to address the hybrid probabilistic forecasting when small and large data sets are available. The first model will incorporate a new gradient boosting machine, in which a projection of the distribution into a Riemannian space is considered, whose corresponding natural gradient is expected to give better updates at each iteration than the state of the art. Meanwhile, a data-driven type-2 fuzzy system which generates monotone if-then rules will be developed to preprocess inputs. The second model consists of graph attention networks, transformers, and variational autoencoders. The graph attention networks overcome the theoretical issues with spectral based methods. The transformers ensure each time step to attend over all the time steps in the input sequence, compared with recurrent neural networks. The combination can give better spatiotemporal information. Moreover, those two models will be extended to forecast net-load with the consideration of demand response potentials, as a multi-target forecasting task.

Real-time analytics is the leading edge of a smart data revolution, pushed by Internet advances in sensor hardware on one side, and AI/ML streaming acceleration on the other. We propose creation of a Center of Accelerated Real Time Analytics (CARTA) to explore the realm streaming applications of analytics. This center will be lead by University of Maryland, Baltimore County with partners from NCSU, Rutgers, and other affiliated universities. The proposed center will work with next generation hardware technologies, like the IBM Minsky with on board GPU accelerated processors and Flash RAM, a Smart Cyber Physical Sensor Systems to build Cognitive Analytics systems and Active storage devices for real time analytics. This will lead to the automated ingestion and simultaneous analytics of Big Datasets generated in various domains including Cyberspace, Healthcare, Internet of Things (IoT) and the Scientific arena, and the creation of self learning, self correcting “smart” systems. At the core of these technologies are the techniques of data wrangling that enable this end-to-end real-time data processing and the infrastructure of the next generation of high-performance analytics systems.

The STARS Computing Corps Alliance for Broadening Participation in Computing (BPC) will serve as national resource for transforming computer science education through 1) building capacity among faculty and students for creating an equitable and inclusive learning climate in their computing departments, 2) building capacity among faculty and students for conducting research and taking action to address BPC challenges, and 3) promoting persistence in computing degree programs, particularly among groups that are underrepresented in computing. A multi-year study provides evidence that shows that the STARS Computing Corps approach is effective for supporting these goals, and indicates the value of a community of practice that engages computing faculty and students at institutions of higher education (IHEs) with a shared commitment to take action to advance diversity, equity, and inclusion in computing. This proposal seeks to further develop STARS as a national resource that builds broader capacity for research and practice, ignites action, and fosters a wider academic community centered on building capacity for inclusive computing education experiences, environments, and practices in higher education.

The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North Carolina’s sweet potato growers.

Machine learning (ML) is a powerful computing tool for building models from data, which is becoming a vital skill across STEM disciplines. However, ML is a challenging subject, requiring students to construct complex ML "pipelines," often with little one-on-one support from instructors. The goal of this CAREER proposal is to aid students in learning to design and implement ML pipelines through a data-driven tutoring system. To do so, the project will develop novel techniques for evidence-centered, real-time assessment of students' ML knowledge and novel forms of automated support for ML, including design feedback, and adaptive code examples.

Quantum computing has the potential to provide a significant advantage over classical computing in terms of algorithmic complexity. The STAQ project is focused on demonstrating such an advantage on an ion trap quantum hardware platform developed at Duke with 64 or more qubits. This requires a co-design between hardware and software to be successful, which Duke University has been developing. NCSU proposes to complement these efforts, potentially leading to earlier demonstration of quantum advantage, by creating a transpiler to translate Qiskit programs to run on STAQ devices, assess benefits of creating complex native gates, and modeling the reliability of STAQ ion trap quantum computers.

Telephone users are regularly besieged by unsolicited sales and scam calls, cannot verify identities of callers, and enterprises frequently fall prey to expensive compromises of their telephone infrastructure. This proposal will deliver techniques to detect these issues, conduct network-wide systematic measurement, and provide practical defenses for these problems. The vision of this 5-year project is to provide technologies that will restore the telephone network to its former status as a trusted and trustworthy network.

This project aims to address the following question: How to model the combined information of a pan-genome collection succinctly (and in a biologically meaningful way) such that the genomic analysis on that representation is both easy-to-compute and accurate? Pan-genome collections may be represented as high-scoring Multiple Sequence Alignment (MSA) data, indexed text data, or the more popular graph-based representations (pan-genome graphs). These models need to support read mapping queries efficiently. This research will lead to a new class of string/graph algorithms for the analysis of pan-genomic data.

The global cellular telecommunication system is critical infrastructure that has become a ubiquitous platform for Internet connectivity supporting a wide range of use cases for both consumers and industry. We are now on the cusp of widespread adoption of 5G technology. While 5G is widely marketed for its gigabit per second rates and ultra-low latency, it also also fundamentally changes the internal network architecture, providing dynamic provisioning of software-defined services that offer enhanced control to network tenants including virtual operators and enterprises. This new threat model necessitates deep investigation of the many technical components that comprise the cellular system. Whereas several initial studies have formally modeled and evaluated the security of 5G cryptographic protocols, little is known about the security of software and hardware systems that implement them. To this end, the goal of this work is to aid mobile network operators in deploying secure cellular systems through the development of tools and techniques that extract, model, and analyze security-sensitive logic of the source and binary code that exists within cellular system functional entities.

The RET Site at NC State University will immerse a diverse group of teachers in a vibrant research community building and analyzing cutting-edge socially relevant and human-centered applications including games, tutors, and analytics platforms. We will recruit teacher teams to include at least one who is learning to teach introductory computer science (e.g. Computer Science Principles), as well as STEM teachers and one teacher or undergraduate with significant programming experience. Teachers will learn about the socially relevant applications of computing and how computer science can be used within almost all careers, and they will develop lessons that help raise student interest in computing while teaching disciplinary content. We will connect teachers to resources from the STARS Computing Corps, an NSF-funded national consortium of institutions dedicated to broadening participation in computing. We will to create a supportive culture of collaboration while promoting individual contributions to research through just-in-time training throughout the summer.

Computers are increasingly being used to simulate and analyze complex social phenomena, but do not account geographical, cultural, economic, and sociopolitical systems that influence social relationships. We identify the need to account for real-world, localized information in social simulation. Our research objectives are to create computational models of social influence and opinion change that support believable social simulation and facilitate novel insights for experts through scaffolded interaction. This project, if successful, will contribute fundamental advances in computational social science, including advances individually in both computer science and social science as well as bidirectional exchange of ideas across disciplines.

We study the web differently from how users explore it, as browsers are not meant to be monitoring tools. Researchers build either ad-hoc solutions or use high-level information from the browser that is inadequate to identify some of the most advanced web attacks. This research aims at building the fundamental blocks for studying an increasingly complex web by developing a monitoring platform that sheds light into the inner workings of modern browsers and websites. Our research outcomes will allow any researcher, web developer or web user to understand better how the web works.

Cognition is central to any programming task---from understanding and reading source code, selecting programming abstractions and algorithms, and problem-solving and debugging implementations. Despite its vast capacity and associative powers, the human brain limits what programming tasks can be performed without process or tools to support it. In this project, we use brain imaging techniques to study software engineers, by examining them perform programming tasks under various conditions. From these studies, we are able to explain the neural mechanics of cognition in programming and derive more effective mental representations, strategies, and training techniques. Finally, we design more effective tools and processes for understanding and supporting programmer cognition.

There is a real need for a follow-on course once high school students, and especially girls, to take after their interest in computing has been elevated by the new Computer Science principles course. We proposed to design and study Beyond CSP, a new course focused on CS concepts that have broad appeal but are traditionally considered advanced and are only taught to CS majors in college. The course topics will include distributed computing, computer networking, cybersecurity, machine learning, the Internet of Things and others. We theorize that a course that teaches these advanced computational methods in disciplinary contexts across a variety of STEAM fields, will make the connection to skills that a modern workforce needs readily apparent. Moreover, it will also have a much broader appeal to young learners. In fact, we propose to tailor the curriculum especially to appeal to girls by focusing on specific disciplines such as healthcare and climate change, and emphasizing collaboration and team work.

Regulatory policies, especially those governing data privacy, must satisfy seemingly contradictory requirements of precision and transparency. Prior research on usable privacy has led to annotating policies with information designed to assist user understanding; meanwhile, the desire for provable guarantees generated efforts on encoding policies in formal logic to answer questions about specific scenarios. The PI proposes to unify these approaches through formalizations amenable to analysis, interactive exploration, and question-answering. This work will enable stakeholders to formulate and answer questions about regulations, protocols, and scenarios, to generate counterexamples and recommendations for policy repair, leading to improved understanding and minimized risk.Impact Tabs/Community

The goal of this work is to investigate the role of self-regulated learning (SRL) in computing education by validating and analyzing fine-grained trace data from students' interactions with programming tools. We will: 1) Conduct instructor interviews and classroom observations to identify SRL strategies related to programming tool use; 2) Instrument the tools to record student behavior, adding a priori design choices that make students' SRL strategies more visible; 3) Conduct laboratory studies and collect think-aloud protocols, then code the data with strategies identified earlier; 4) develop educational data mining techniques to identify SRL behaviors from log data; 5) deploy the SRL detectors in both introductory and more advanced CS classrooms, using the detected behaviors to validate and extend SRL theories in the domain of CS.

The overarching goal of this proposal is to develop a generic HPC data registration and retrieval framework (named HPC-FAIR) to make both training data and AI models of scientific applications findable, accessible, interoperable, and reusable. This framework provisions significant speedup of the research and development of ML-based approaches for analyzing and optimizing scientific applications running on heterogeneous supercomputers. The datasets and AI models from HPC-FAIR will also serve as common baselines to quickly, consistently, and fairly evaluate new AI models for quality, complexity, and overhead.

Abstract: The goal of this CAREER project is to fill the gap between growing application complexity and performance requirements, and existing application-agnostic network management, to enable and incentivize rigorous performance guarantees for distributed real-time applications at the network edge. The core contribution is the design, analysis, and evaluation of WolfPack, a general edge resource provisioning framework for real-time applications. The PI will focus on three key thrusts: 1) modeling and optimization of edge resource provisioning, 2) stochastic models and robustness techniques to control the risk, and 3) incentive mechanisms to enable truthful and competitive network edge resource trading.

Recent advances in artificial intelligence have raised concerns of ethics in regards to intelligent, adaptive agents. This project begins from a model of a sociotechnical system (STS) comprising autonomous social entities (people and organizations -- principals) and technical entities (agents, who help principals). Its objective is to uncover principles of multiagent systems that enable developing sociotechnical systems that incorporate ethical concerns adaptively and from multiple perspectives, and with high confidence. This project will develop a formal computational representation of an STS in terms of social controls over its principals and technical controls over its agents.

Semantic code search uses behavioral specifications, such as input/output examples, to identify code in a repository that matches the specification. Challenges include handling scenarios when 1) there are too few solutions, 2) it is difficult to understand how solutions differ, and 3) there are too many solutions. I propose techniques to 1) expand the scope of code that can be modeled and find approximate solutions when an exact one does not exist, 2) determine the differences between two code fragments, and 3) navigate a large space of possible solutions are needed by selecting inputs that maximally divide the solution space.

This project advances the state of knowledge about how to infer misconceptions and generate explanations without any explicit models of a programming language. In contrast to existing approaches, which involves manual identification of misconceptions in programming languages, or cross- language migrations—which provide translations but no explanations—our technique automatically discovers inconsistencies cross-languages and supports automatic resolution for problematic translations.

This collaborative project between NCSU and ETS is focused on developing new noninvasive process-based measurements for students engagement with writing tasks, including analyses of their writing quality, working habits, and responses to feedback. As part of this project we will develop a secure instrumented platform for online writing tasks that will provide analytical tools for instructors and researchers to monitor and evaluate student's work.

The goal of this work is to detect, measure, and remediate a software project's use of external, open source software dependencies with security flaws. First,we will introduce two new static analysis primitives: a global dependency graph (GDG) and a global vulnerable-dependency graph (GVDG) to simplify the detection and measurement of the extent and effects of vulnerable dependencies. We will then create novel techniques for analyzing code and textual artifacts of software projects to identify when a new version has fixed a vulnerability,even if a security advisory has not been announced. In doing so, we will help developers know when dependencies must be updated, ultimately leading to more secure software.

Aquaculture, the rearing and harvesting of organisms in water environments, is a rapidly expanding industry that now produces more seafood than all wild caught fisheries worldwide. This inevitable growth must be steered towards sustainable production practices, which requires intensive monitoring in areas that are difficult and potentially dangerous to access. The vision of this project is to improve the efficiency and sustainability of near-shore aquaculture production through integrating a flexible, customizable, multi-task vehicle fleet, consisting primarily of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), with a biologically-relevant framework for accelerated prototyping. This project will use oyster production along the Eastern US shoreline as a case study and testbed.

This project will develop adaptive rendering and display technologies supporting emerging immersive displays, including wall-spanning, glasses-free stereo windows and lightweight AR/VR glasses. Such displays demand high-bandwidth, low-latency input not available today. We will attack this problem with software and hardware that exploit perceptual asymmetries and spatiotemporal redundancies. The resulting immersive displays will help realize nascent applications such as immersive entertainment and simulation, socially engaging conferencing and first-person wayfinding.

We propose to develop infrastructure to enhance and scale CSEd research by leveraging the power of data-driven AI and ML. To do so, we need to overcome 3 challenges: data (there is not enough quantity and quality of data), analytics (developing and sharing data mining and AI methods for CSEd is highly siloed and disconnected) and evaluation (AI-based interventions and tools are not easily deployed and replicated). To address these challenges, we will develop a large collection of resources including datasets, analytical approaches, reusable smart learning content, and tools and user services that enables the community to reuse the resources and contribute to the collection.

Memory safety is essential. Despite decades of research, unauthorized memory reads and writes are still among the most common security attacks. The emerging persistent memory (PM) amplifies the importance of strong memory protections. As a promising supplement or substitute of DRAM as main memory, PM offers higher density, better scaling potential, lower idle power, and non-volatility, while retaining byte addressability and random accessibility. Data in a PMO is long lived; its existence and structure are preserved across process runs. The longevity, plus direct byte-addressability, makes it more vulnerable as attacks to a PMO could span across executions. This proposal aims to improve the understanding of the problem and provide innovative solutions to strengthen memory security for future NVM-based systems.

Wireless devices are inherently faculty which can result in multifaceted data errors in computing, caching, and communications (C3). These errors have been widely deemed harmful, but recent studies have shown that they can be benign or even beneficial. The research objective of this project is to proactively harvest, render, and control data errors across C3 of wireless devices for significant performance gains in energy efficiency, throughput, data privacy, etc. Moreover, the research efforts will be coupled with educational innovations through the development of new laboratories, lecture contents, outreach demos and a novel undergraduate/graduate co-learning pedagogy.

Diabetic retinopathy (DR) is expected to affect over 11 million people in the US by 2030 and is the leading cause of blindness in working age Americans, despite being almost entirely preventable with timely detection, treatment, and adherence to follow-up care. To reach the over 30 million adults living with diabetes in the US, the Retinal Care-DR program is designed to eliminate the deficiencies of the current care delivery model by implementing a first-of-its-kind, end-to-end solution for DR care and blindness prevention. This will be accomplished through the application of machine learning to prioritize patients for care coordination by DR risk, development of patient-specific engagement strategies to identify and modify adherence behaviors, implementation of agent-based simulation to maximize patient health outcomes while minimizing the cost of care coordination, and the identification of care coordination strategies that result in higher rates of screening using a user-centered design process.

This proposal will generate novel abstractions for computing that extend serverless functions to better leverage unique hardware characteristics, and for memory to allow more automated leveraging of workload characteristics such as locality and compute intensity. Further, this work expands currently limited secure enclaves to include parallel, heterogeneous hardware needed to support a wide range of applications, and enhances serverless databases to leverage heterogeneous compute resources.

In this project, we propose to develop methods and protocols to automatically onboard BLE devices so that they can connect to any available network and be able to exchange the desired information. We will further develop methods to utilize metadata for analytics and implement appropriate access control policies so that entities with proper permissions can get timely access to the right type of data.

The proposed project will see the design, development, and investigation of a multimodal affect-sensitive learning environment for generating student interest in middle school science. We will capture rich multi-channel data (eye gaze, facial expression, posture, interaction traces) on student problem solving with an inquiry learning environment. We will utilize multimodal machine learning to induce affect recognition models, which will drive run-time affect-sensitive interventions to trigger and sustain student interest. The project will culminate in a classroom experiment to evaluate the impact of the multimodal affect-sensitive learning environment on student learning and science interest.

The REU Site at NC State University will immerse a diverse group of undergraduates in a vibrant research community of faculty and graduate students building and analyzing cutting-edge human-centric applications including games, tutors, and mobile apps. We will recruit students from underrepresented groups and colleges and universities with limited research opportunities through the STARS Computing Corps, an NSF-funded national consortium of institutions dedicated to broadening participation in computing. Using the Affinity Research Groups and STARS Training for REUs models, we will engage faculty and graduate student mentors with undergraduates to create a supportive culture of collaboration while promoting individual contributions to research through just-in-time training for both mentors and students throughout the summer.

Continuous Integration (CI) has become an essential component of the modern software development cycle. Developers engineer CI scripts, commonly called workflows or pipelines, to automate most software maintenance tasks, such as testing and deployment. Security issues in workflows can have devastating effects resulting in supply-chain attacks. We propose to handle these research challenges by (1) defining a threat model and deriving security properties from first principles; (2) developing a framework based on our Workflow Intermediate Representation (WIR) that enables us to verify and define security properties in a platform-agnostic way.

Modern distributed systems and Internet services require authentication between their components to protect their services from unauthorized access and ensure appropriate billing. In practice, this authentication is performed by presenting a static secret, such as an “API key” or password. These are difficult for developers to manage and deploy securely, and credentials are accidentally or intentionally stored in widely readable software repositories. This threatens not just the security of the leaker, but also the authenticating service. The ultimate root cause of this issue is the adaptation of user authentication methods (e.g., passwords) to software in ways that are inappropriate and ultimately unsafe. This proposal will fund research to more reliably and consistently identify these leaked software credentials, triage them according to the risk they present, conduct developer interventions to train them to properly manage this risk, and finally develop more secure yet manageable alternative solutions to software authentication.

Modern web applications are the cornerstone of much of our online life. Unfortunately, web appli- cations are a complex mix of different technology stacks (e.g., HTML, JavaScript, and PHP), and this complexity breeds security vulnerabilities that allow an adversary to launch successful attacks. Thus, we require new approaches and techniques to tame the complexity that seems inherent to web applications. Building on the success and impact of our existing XS-SHREDDER efforts, the project proposed herein will research and develop novel, complementary, and synergistic capabilities that will improve the result and applicability of debloating to all layers of the web-application stack. These results will be demonstrated with proof-of-concept prototypes that we will quantitatively evaluate based on the reduction of code and known vulnerabilities. At the same time these prototypes should facilitate easy transition to customers within the Navy and beyond.

We propose to develop learning-engineering methods to efficiently build an effective online STEM learning environment, in the form of adaptive online courseware called CyberBook, to promote robust mathematics learning with understanding. The proposed CyberBook is a combination of traditional online courseware (that promotes conceptual understanding) and intelligent tutoring systems (that support guided learning-by-doing). We hypothesize that these two well-established technologies can be combined by identifying the shared latent learning constructs, i.e., skills and concepts to be learned. We further hypothesize that the resulting cyberlearning space will promote synergetic learning that, by definition, will fertilize the desired proficiency.

Web applications play an important role in the current software ecosystem, and these web applications are usually built with certain supporting frameworks. While these frameworks ease the development of web applications, they bring several challenges to the analysis of web applications. Existing techniques analyze each request independently leading to suboptimal results. In this project, we propose inter-request analysis to go beyond the boundaries of individual requests, design a framework that can capture and express inter-request data and control dependencies, and develop several program analyses leveraging the framework for performance bug diagnosis, performance optimization, and flow integrity monitoring.

Knowledge graphs have emerged in many domains of science and technology as a powerful means of integrating, structuring, and mining information to extract new knowledge. Recognizing the importance of this paradigm, the Proto-OKN project will create FRINK: Fabric Integrating Networked Knowledge. FRINK will create capabilities that allow for the uniform deployment, integration, and harmonization of knowledge graphs created under the Proto-OKN program into a unified Open Knowledge Network for query and analysis. FRINK will be organized around three objectives, detailing three types of fabric that will bind together the initially disparate graphs developed by Theme 1 teams.

Recent policy documents for graduate STEM education note that engineering programs do not adequately help students develop abilities to work in collaborative and team settings, to communicate to diverse audiences, and to deal with diverse opinions, ideas, and backgrounds. Additionally, the emergence of new fields at the interface of two or more disciplines requires a workforce with the ability to work collaboratively with people from different disciplines. Moreover, most engineering problems in the field involve multiple heterogeneous teams working on subsystems that need to be integrated as a working system. Students need to learn how to work within and across teams - and disciplines. In this project we seek to improve graduate engineering education by studying students’ interactions and learning within and across collaborative groups, when integrating into professional engineering endeavors, and when engaged in interdisciplinary projects, in order to identify promising approaches, identify obstacles, and generate theory for the effective preparation for the workforce of graduate engineering students. We are guided by the theoretical framework of communities of practice (CoP), which has a strong emphasis on collaboration, diverse groups and audiences, and the need to communicate across disciplinary and cultural backgrounds. The CoP framework also provides mechanisms for the enculturation of novices into disciplinary groups, as well as for the dissemination of ideas across such groups. We have selected three courses from three different departments to foster and study this CoP approach. The selected classes afford CoP-guided studies of different grain sizes, using diverse concepts from the CoP framework, and in a variety of disciplines. Through this approach that involves various settings and granularities, we seek to develop a broader view of CoPs in engineering that can build theory for this field and guide implementation across subfields of engineering education.

Recent years have seen a surge in popularity in smart home IoT products, and with the ongoing pandemic, people are spending more time interacting with such devices. However, it is unclear whether and how these IoT devices affect the security, privacy, and performance of the home network as well as the access network. In this proposal, we focus on developing privacy-preserving IoT analytics to help network providers allocate network resources accordingly and, at the same time, help consumers identify potential anomalous behavior.

This project aims to develop an integrated lightweight and energy-efficient prosthetic care robot framework. It will enable proactive and user-specific prosthetic control to improve walking function in a variety of walking conditions found ubiquitously in daily living.

Digital twin is emerging as a revolutionary approach to testing and assurance for next-generation (nextG) wireless networks enabling continuous prototyping, optimization, and validation. The primary goal is to lay the foundations of digital network twin (DNT) by exploring innovative technologies to map and optimize nextG wireless networks in twins, thereby facilitating development, testing, and formal evaluation exercises of nextG wireless networks. The research agenda comprises two thrusts. Thrust 1 is focused on novel approaches of building the twin environment to replicate the physical network world. Thrust 2 shall build and optimize the network twins over actual network environments associated with communication, computing, and networking resources. The fundamental research of Thrusts 1 and 2 is then implemented in a developed DNT platform used to demonstrate the behavior and performance of designed twining and optimization approaches.

Software engineering candidates commonly participate in high-pressure technical interviews, or whiteboard interviews. Critics have argued that these types of interviews unnecessarily stress and filter out otherwise qualified candidates, yet it remains a standard hiring practice. This project proposes a series of randomized control trials to understand how these practices influence performance of candidates, identify any bias or confounding factors in the process, and develop interventions to make problem-solving assessment more equitable and inclusive.

Software testing is a critical skill for computer science graduates entering technical positions. Software tests, and in particular unit tests, have several uses in education. The purpose of this proposal is to create pedagogy and tools around writing unit tests for CS3 and Software Engineering (SE) courses. Building on our preliminary work, we develop and evaluate the impact of a lightweight intervention with explicit testing strategies on the test quality of student-written tests. Then, we investigate the impact of the process of writing tests on student outcomes.

Real-time hierarchical scheduling facilitates modular reasoning about the temporal behavior of real-time applications by isolating their potential misbehavior. However, conventional time-partitioning mechanisms fail to achieve strong temporal isolation from a security viewpoint; variations in execution timings can be perceived by others, enabling illegitimate information-flow between applications completely isolated from each other in the utilization of CPU time. This project develops algorithmic solutions that make real-time partitions oblivious of others’ varying temporal behaviors, achieving non-interference-based security among partitions. The proposed work will allow such systems to employ advanced hardware and software technologies to develop high-end, real-time applications in a secure manner.

This project will support implementation and study of the Beauty and Joy of Computing (BJC) curriculum. We aim to increase implementation of BJC in New England states and beyond particularly in high-need districts. We will study the effects of BJC implementation on the participation of girls, Black, Latinx and low-income students.

Biomanufacturing differs from chemical manufacturing as the process operations are significantly different in deference to the lability of biomolecules and cells. Biomanufacturing also differs in the expertise needed for designing, developing and implementing bioprocesses as well as the nature of safety and ethical issues that must be addressed. In the nascent industrial biotechnology sector, the pace of change and innovation, along with societal impacts, must be part and parcel of workforce training and education. Rather than develop separate educational programs for molecular biotechnology, bioprocessing and the ethical issues related to the field, we propose to provide an integrated platform, based on the best pedagogical practices and educational technologies (e.g., including the use of augmented reality for remote laboratory training) that brings workers up-to-speed and helps them maintain the needed expertise to be effective in this emerging sector. BIT (https://biotech.ncsu.edu/), BTEC (www.btec.ncsu.edu) and GES (https://research.ncsu.edu/ges/) at NC State have considerable experience in this type of education for our campus and beyond, and propose to leverage this experience to contribute to the BioMADE initiative. This integrated educational training will help build a sustainable, domestic, end-to-end bioindustrial manufacturing ecosystem that will enable domestic bioindustrial manufacturing at all scales, develop technologies to enhance U.S. bioindustrial competitiveness, de-risk investment in relevant infrastructure, and expand the biomanufacturing workforce to realize the economic promise of industrial biotechnology. Recent attention to issues of Diversity, Equity, and Inclusion (DEI), and broader societal awakenings of academic and corporate responsibility have raised important questions that reach well beyond our laboratories, classrooms, manufacturing facilities, and into society. The current and future biomanufacturing workforce, need to be prepared for these complexities. The workforce training and education package developed here will be sensitive to student/worker time commitment and be maintained such that emerging developments and innovations can be readily incorporated.

Cloud environments employ various microservices and serverless functions to handle web or database requests. Although cloud provides a uniformed infrastructure for resource management, it can easily suffer from performance inefficiency in the entire cloud software stack. To address this issue, we will develop CloudProf. This project has the following goals. First, it will break the abstraction introduced by the runtime systems of managed languages for intra-application optimization. Second, it will identify problematic interactions across microservices for inter-service optimization. Third, it will break the abstraction introduced by virtual machines and containers for the optimization of the entire cloud software stack.

GPUs have become common in today’s computing systems. However, it is challenging to efficiently map software applications to GPU architectures. Performance inefficiencies can hide deep in heterogeneous code bases, impeding applications from obtaining bare-metal performance. In this project, we will develop DrGPU to systematically study the performance inefficiencies in heterogeneous CPU-GPU systems with novel measurement, analysis, and optimization techniques.

Children encounter artificial intelligence (AI) on a daily basis and may have limited recognition that they have interacted with an AI-driven system or misunderstandings around what AI can do. Understanding the technologies behind these systems is essential for all students, especially young children who are coming of age in a highly evolving technological landscape. This project will create story-centric plugged and unplugged activities to support upper elementary student learning of AI concepts as well as develop a set of self-report and multiple-choice instruments for assessing student attitudes and understanding around AI.

We attack a key software challenge in quantum computing, the programmability of quantum computers. Writing quantum programs is vastly more difficult than writing classical programs, with relatively few of the skills required by the latter translating to the former. Furthermore, the two dominant forms of quantum computation—circuit-model quantum computing and quantum annealing—are programmed fundamentally differently from each other, and each requires substantial effort to master. The goal of this research is to both unify and facilitate the exploitation of circuit-model quantum computers and quantum annealers. As a result, productivity of computational scientists is increased in all scientific disciplines.

The expansion of K-12 Computing Education Research (CER) is quickly following the expansion of computing education in primary and secondary schools, yet much remains to be learned about the effectiveness of the implementation and the quality of evidence produced by research. While the integration of computing education into K-12 in the United States is still in its infancy, so is the research necessary for identifying promising practices for educational outcomes across a variety of populations, including those historically underserved and marginalized by education. The proposal seeks to 1) summarize and frame prior equitable K-12 CS education research against the areas of capacity, access, participation, and experience as defined by the CAPE framework; 2) develop publicly-available recommendations and resources along the CAPE framework for expanding coverage of equitable K-12 computing education research; and 3) design and pilot workshops to train K-12 education research in methods and practice to support robust evidence-based research results that can inform practice.

Configuration scripts are used to manage system configurations and provision infrastructure at scale. Configuration scripts are susceptible of including security weaknesses such as hard-coded passwords, which can facilitate large-scale data breaches, as well as provisioned systems being compromised. We propose an automated technique to identify security weaknesses so that configuration scripts do not cause large-scale security attacks and data breaches. We will build upon our recent research and construct eSLIC, which will overcome previous limitations of our initial prototype and facilitate wide-spread security static analysis of infrastructure. We will make eSLIC available for OSS and practitioners in industry.

This work seeks to investigate the potential of non-volatile memories (NVM) with its novel properties of byte addressability, persistency, larger capacity, yet higher latency, and addresses the its applicability within the next generation edge cloud infrastructures. The objective of this work is to assess the efficacy of providing services in the edge cloud as opposed to conventional cloud-hosted infrastructure.

Our objective is to enable stateful applications on the serverless architecture. A stateful application is characterized by a function-chain. The need for maintaining the state arises because the platform that is executing the function-chain must take the output from any given function in the chain, hold it until the next function in the chain starts execution, and provide it to that function when the execution starts. Thus, towards achieving the objective of enabling stateful applications on the serverless architecture, we envision two major tasks that we plan to undertake. First, we will extend our existing serverless platform to support function-chains in addition to standalone stateless functions.Second, we will demonstrate the feasibility of the framework resulting from the first task by executing selected existing stateful applications on this framework.

Existing blockchains support a limited set of first-class transactions and rely on user-defined code for extended programmability. This creates challenges with robustness, performance and usability for applications using blockchains. To address these problems, we will develop the conceptual and implementation foundations for “declarative blockchain transactions” that will enable the explicit capture as first-class blockchain transactions, various transactional behaviors common to many blockchain applications. These first-class primitives will serve as building blocks that can be composed by users into more complex blockchain transaction workflows and can be supported by automated reasoning in the blockchain to avoid aforementioned challenges.

NSF Computer and Information Science and Engineering Graduate Fellowship Award (CSGrad4US) for incoming CSC PhD student Jordan Esiason.

The potential of modern real-time applications, while enabled by advances in wireless communication technologies, is limited by the poor and unpredictable performance of the cloud backend as an Internet-based service. Edge computing is believed to be the magic bullet to this problem, but after years of research, we have yet witnessed the first large-scale deployment and utilization of edge computing. We believe the barrier is the lack of SLA-based performance guarantee, due to the inevitable risk of SLA violation. This project aims to take the first step in modeling and optimization of SLA violation risks in mobile edge computing.

This project proposes to explore solutions to both the workflow scheduling problem and the fault awareness requirements. Its primary aim is to prototype a novel software scheduling technology to manage dynamically changing heterogeneous resource pools on one side and a fault propagation mechanism within Flux instances on the other side. This reflects emerging trends in combing HPC and cloud computing while taking full advantage of characteristics for heterogeneous resource requirements of modern applications workflows under challenges to address faults in a transparent manner.

We aim to devise a general framework for anonymity and traceability in blockchain applications which fulfills efficiency and trustless requirements of current blockchain ecosystems.

This project investigates a form of active cyberdefense based on defensive deception against an attacker. It applies a form of game theory called hypergame theory that enables a natural representation of situations where an attacker and a defender can be understood as playing a different game. This project will develop computational models of hypergames that reflect cyber attack and defense strategies to support the investigation of tradeoffs such as between defense effectiveness and cost. If successful, the project will yield representations and algorithms that defenders could apply to disrupt an attacker's beliefs and thus cause attacks to fail.

This project will work on developing efficient sparse tensor algorithms for hypergraphs on diverse computer platforms. Dr. Li’s group will accelerate sparse tensor algorithms from two perspectives, sparse tensor memory representation and effective performance tuning methods. Dr. Li’s group will develop novel data structures taking symmetry, hyper-sparsity, and high dimensionality features into consideration, by extending Li’s prior work.

The DoD is making available scholarships for N.C. State students specializing in cybersecurity. Students (junior and senior) must be US citizens and majoring in any engineering field at the bachelor, master's, and PhD level with a specialization in cybersecurity. In addition to full tuition, this scholarship can last up to five years and provide a generous stipend, tuition, health insurance, and an allowance for other professional expenses. In return, the student agrees to work after graduation with a federal, executive-branch government agency for an equal period of time. The program includes mentoring, professional opportunities while in school, and assistance finding internships and post-graduation full-time employment in government.

This work aims to study sparsity in widely-used tensor networks by introducing constraints, regularization, dictionary, and/or domain knowledge for better data compression, faster computation, lower memory storage, along with better interpretability by: 1) Proposing memory hierarchy and microarchitecture-aware representations and effective yet efficient data (re-)arranging; 2) Designing memory hierarchy-aware and balanced algorithm with smart page arrangement; 3) Erasing the curse of dimensionality through memoization and intelligent data allocation; 4) Exploring specialized architecture on GPU and FPGA. We will accelerate six application scenarios by leveraging the scalable and highly optimized sparse tensor network on distributed heterogeneous systems.

This proposal targets performance optimization for sparse tensor networks on heterogeneous architectures. Our optimization crosses high-performance computing, algorithms, runtime, compiler, and computer architecture areas by proposing compressed representations and organization, load-balanced and memory heterogeneity-aware algorithms with memorization and intelligent data allocation, and designing specialized accelerators. The whole infrastructure will be applied to scale diverse application scenarios.

In recent years, we have seen a surge in the popularity of smart home Internet of Things (IoT) products, and recent reports show that during the pandemic, many consumers have furnished their homes with more smart devices. Furthermore, with the ongoing pandemic, working from home has increasingly become the norm. We are therefore not only seeing people bring their corporate devices to their home offices but also spending more time interacting with such devices. However, it is unclear whether and how these IoT devices affect the security and privacy of the home network. In this proposal, we focus on developing privacy-preserving IoT analytics to identify potential anomalous device behavior.

Edge/cloud programs (workloads such as machine learning and data analytics) of high performance play an important role for boosting productivity and reducing energy consumption. However, it is increasingly difficult to identify performance bottlenecks and optimize them because the workloads, written in different languages, need to be adapted to complex architectures with increasing parallelism and heterogeneity. In this project, we will developed a performance tool, which analyzes various workloads and pinpoints performance inefficiencies due to redundant arithmetic computation, unnecessary data movement, and useless memory accesses. Our framework will work on CPUs and GPUs that widely exist in various edges and clouds.

The NC State Computer Science Department had doubled undergraduate enrollments and the percentage of women in our program between 2010 and 2020. With this growth, we are challenged with understanding persistence and retention of our students, particularly women. The goal of the diagnostic grant program is to collect and analyze demographic data to better understand where our students are coming from, and if they leave the program, where do they go. The results of the data analysis will provide insights into how we can better support our students to increase persistence, retention, and success.

The North Carolina Department of Transportation (NCDOT) created a new knowledge repository called Communicate Lessons, Exchange Advice, Record (CLEAR) as an official platform to store and retrieve knowledge. We will transfer a construction domain language model to improve the search capabilities of CLEAR database. A construction language inference model has already been developed as a prototype that can make meaningful connections between lessons learned and best practices within the construction domain vocabulary. A proof of concept will be validated by project managers on a set of pre-selected projects by the NCDOT Value Management Office.

Runtime Verification (RV) monitors programs against formal specifications and reports violations when executions do not satisfy those specifications. RV can find bugs that tests miss, but it is not yet widely adopted. We address two hindrances to broad RV adoption: (1) writing specifications requires learning domain specific languages, and (2) many programming languages have no mature RV tool. We propose infrastructure for writing specifications in popular user-friendly formats, and for reusing existing RV tooling for Java to monitor programs written in other languages. We will evaluate the improved usability of the proposed infrastructure via case studies and user studies.

Emerging CPS applications increasingly require significant computational power to benefit from machine learning. From lower-end multi-core systems (4-core A72) to embedded GPUs (NVIDIA Drive DGX), highly parallel compute platforms dominate the application space. However, real-time requirements poorly map to such platforms and impose high coding complexity. The objective of this work is to alleviate programmers by extending the OpenMP to real-time tasks and data parallelism. Instead of deploying a real-time operating system with limited compatibility, we propose novel OpenMP extensions, define their semantics and provide an implementation on Linux mapping to privileged priorities under static and dynamic real-time scheduling policies.

Holding a large PI meeting like the proposed SaTC PIs meeting is a complex undertak-ing, and the time commitment for the participating PIs is considerable. As such, it is important that such meetings only be held when the attendees and community as a whole will benefit substantially.The proposed PI meeting for the NSF SaTC program would provide a valuable venue for SaTC researchers to engage with others in the community and in other areas, as well as to think about the broader issues in cybersecurity research and education. These interactions are unlikely to occur at other research conferences that are organized around narrow research disciplines and focus on presenting recent works.

This study abroad project has been created in collaboration with NC State's Women and Minority Engineering Program (WMEP). The goal is to increase the number of students from underrepresented groups who participate in study abroad programs with a STEM focus. At NCSU, there are only two faculty-led programs that are open to Engineering students and they are both to Western Europe. This program will expand not just the types of students who travel abroad but will also diversify the location as well as the variety of engineering problems to be studied, better preparing students for a global workforce.

Real-time analytics is the leading edge of a smart data revolution, pushed by Internet advances in sensor hardware on one side, and AI/ML streaming acceleration on the other. We propose creation of a Center of Accelerated Real Time Analytics (CARTA) to explore the realm streaming applications of analytics. This center will be lead by University of Maryland, Baltimore County with partners from NCSU, Rutgers, and other affiliated universities. The proposed center will work with next generation hardware technologies, like the IBM Minsky with on board GPU accelerated processors and Flash RAM, a Smart Cyber Physical Sensor Systems to build Cognitive Analytics systems and Active storage devices for real time analytics. This will lead to the automated ingestion and simultaneous analytics of Big Datasets generated in various domains including Cyberspace, Healthcare, Internet of Things (IoT) and the Scientific arena, and the creation of self learning, self correcting “smart” systems. At the core of these technologies are the techniques of data wrangling that enable this end-to-end real-time data processing and the infrastructure of the next generation of high-performance analytics systems.

The Early Research Scholars Program (ERSP) is a group-based, dual-mentored research structure designed to provide a supportive and inclusive research experience using equity-based practices to grow the confidence and foundational skills of early-career students, particularly African Americans, Hispanics, Native Americans and women. For this NSF subaward from UC San Diego, we plan to add ERSP to our course catalog and start implementing it in Fall 2021. As part of their full-year apprenticeship, teams of students will learn about graduate school, be matched to research mentors, observe the mentor's lab, participate in the ERSP course, and propose an independent research project.

Conference Proposal: 2022 CISE EWF PI Meeting and REU Site PI meeting

Regulatory policies, especially those governing data privacy, must satisfy seemingly contradictory requirements of precision and transparency. Prior research on usable privacy has led to annotating policies with information designed to assist user understanding; meanwhile, the desire for provable guarantees generated efforts on encoding policies in formal logic to answer questions about specific scenarios. The PI proposes to unify these approaches through formalizations amenable to analysis, interactive exploration, and question-answering. This work will enable stakeholders to formulate and answer questions about regulations, protocols, and scenarios, to generate counterexamples and recommendations for policy repair, leading to improved understanding and minimized risk.Impact Tabs/Community

We propose to develop learning-engineering methods to efficiently build an effective online STEM learning environment, in the form of adaptive online courseware called CyberBook, to promote robust mathematics learning with understanding. The proposed CyberBook is a combination of traditional online courseware (that promotes conceptual understanding) and intelligent tutoring systems (that support guided learning-by-doing). We hypothesize that these two well-established technologies can be combined by identifying the shared latent learning constructs, i.e., skills and concepts to be learned. We further hypothesize that the resulting cyberlearning space will promote synergetic learning that, by definition, will fertilize the desired proficiency. This supplement is for an REU experience for students.

The 30th Fall Workshop on Computational Geometry will be organized by Professor Don Sheehy of NC State University. The aim of the workshop is to bring together researchers from academia and industry, to stimulate collaboration on problems of common interest arising in all areas of geometric computing. Following the tradition of the previous workshops on Computational Geometry, the format of the workshop will be informal, extending over two days, with several breaks scheduled for discussions. There will be 2 invited speakers. There will also be an open problem session to promote a free exchange of questions and research challenges.