Research Projects 2024 (by faculty)

The funded projects listed below are/were active projects in the 2024 calendar year and the funded running total for that year is on the left navigational menu.

Collaborative Research: Transforming Introductory Computer Science Instruction with an AI-Driven Classroom Assistant
Bita Akram ; James Lester, II ; Bradford Mott ; Jessica Vandenberg

$1,723,467 by National Science Foundation (NSF)
05/ 1/2023 - 04/30/2027

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.

Improving Equity in AP Computer Science Principles: Scaling Beauty and Joy of Computing
Tiffany Barnes

$249,997 by Education Development Center, Inc.
09/ 1/2021 - 12/31/2025

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.

Collaborative Research: BPC-AE: STARS: Catalyzing Action-Oriented Academic Communities for Broadening Participation in Computing
Tiffany Barnes ; Veronica Catete

$652,289 by National Science Foundation (NSF)
10/ 1/2021 - 09/30/2024

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.

Collaborative Research: Conference: 2022 CISE Education and Workforce PI and Community Meetings
Tiffany Barnes ; Veronica Catete

$17,726 by National Science Foundation (NSF)
08/15/2022 - 01/31/2024

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

RET Site: Socially Relevant Computing and Analytics
Tiffany Barnes ; Collin Lynch ; Veronica Catete

$598,913 by National Science Foundation (NSF)
04/15/2021 - 03/31/2024

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.

Field Experiences in Engineering Education - Rwanda
Veronica Catete ; Laura Bottomley

$34,885 by World Learning
09/ 1/2022 - 08/31/2024

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.

Generalizing Data-Driven Technologies to Improve Individualized STEM Instruction by Intelligent Tutors
Min Chi ; Tiffany Barnes ; Thomason Price

$1,999,578 by National Science Foundation (NSF)
08/15/2020 - 07/31/2025

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.

Proto-OKN Theme 2: FabRic Integrating Networked Knowledge (FRINK)
Rada Chirkova

$342,500 by University of North Carolina at Chapel Hill
10/ 1/2023 - 09/30/2025

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.

Collaborative Research: FMitF: Track II: Cross-Language Support for Runtime Verification
Marcelo D'Amorim

$50,000 by National Science Foundation (NSF)
09/ 1/2023 - 02/28/2025

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.

Collaborative Research: IMR: MM-1C: Privacy-preserving IoT Analytics and Behavior Prediction on Network Edge
Anupam Das

$300,004 by National Science Foundation (NSF)
10/ 1/2022 - 09/30/2025

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.

Collaborative Research: SaTC: CORE: Medium: Enabling Practically Secure Cellular Infrastructure
William Enck

$601,966 by National Science Foundation (NSF)
01/ 1/2022 - 12/31/2024

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.

Collaborative Research: FRR: An Integrated, Proactive, and Ubiquitous Prosthetic Care Robot for People with Lower Limb Amputation: Sensing, Device Designing, and Control
Zhishan Guo

$300,000 by National Science Foundation (NSF)
01/ 1/2023 - 12/31/2026

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.

Collaborative Research: Building High-Quality K-12 CS Education Research Capacity Across an Outcome Framework of Equitable Capacity, Access, Participation, and Experience
Sarah Heckman

$202,645 by National Science Foundation (NSF)
09/ 1/2021 - 08/31/2024

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.

Collaborative Research: Characterizing and empowering student success when traversing the academic help landscape
Sarah Heckman ; Lina Battestilli

$374,120 by National Science Foundation (NSF)
05/ 1/2024 - 04/30/2027

In this research, we plan to characterize the help resources available to students in Computer Science (CS) courses and analyze the order and frequency of use by the students. We will also study why students choose specific help patterns and what help they perceive to be effective for their learning. Our goals are to explicitly teach students about the help resource landscape, guide them to identify CS topics where they may need help and to empower them to be more effective in traversing the complex landscape of help resources.

Computer Science Pathways: A Diagnostic Grant to Support Retention and Persistence
Sarah Heckman ; Tzvetelina Battestilli ; Veronica Catete

$59,917 by Northeastern University
01/25/2022 - 01/24/2024

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.

Collaborative Research: SaTC: Core: Medium: Securing Continuous Integration Workflows
Alexandros Kapravelos

$400,000 by National Science Foundation (NSF)
07/ 1/2023 - 06/30/2027

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.

CAREER: Web Evolution and Emerging Threats
Alexandros Kapravelos

$561,188 by National Science Foundation (NSF)
07/ 1/2021 - 06/30/2026

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.

Collaborative Research: SaTC: CORE: Medium: Defending against Emerging Stateless Web Tracking
Alexandros Kapravelos ; Anupam Das

$799,081 by National Science Foundation (NSF)
06/15/2022 - 05/31/2026

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.

CompGen: Competency-based Generation of Synthetic Training Scenarios for the Schoolhouse
James Lester ; Wookhee Min

$1,449,415 by Combat Capabilities Development Command Soldier Center (DEVCOM)
04/ 1/2023 - 03/31/2026

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.

AI Institute for Engaged Learning
James Lester, II

$19,996,290 by National Science Foundation (NSF)
10/ 1/2021 - 09/30/2026

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).

Collaborative Research: ExplainIt: Improving Student Learning with Explanation-based Classroom Response Systems
James Lester, II ; Wookhee Min

$1,599,645 by National Science Foundation (NSF)
10/15/2021 - 09/30/2025

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.

Improving Conceptual Knowledge in Upper Elementary Science with Scaffolded Sketch-Based Modeling
James Lester, II ; Bradford Mott

$1,999,050 by US Dept. of Education (DED)
08/ 1/2021 - 07/31/2025

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.

TCAT and TeamCoach: Tools for Natural Language-Based Team Communication Assessment and Team Feedback in Collective Synthetic Training Environments
James Lester, II ; Bradford Mott ; Jonathan Rowe ; Randall Spain

$2,018,810 by US Army - Army Research Laboratory
09/ 5/2019 - 01/ 4/2024

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.

Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
Jiajia Li

$3,033,782 by National Science Foundation (NSF)
10/ 1/2023 - 09/30/2028

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.

QuSeC-TAQS: Sensing-Intelligence on The Move: Quantum-Enhanced Optical Diagnosis of Crop Diseases
Jianqing Liu

$1,075,000 by National Science Foundation (NSF)
09/ 1/2023 - 08/31/2027

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.

CAREER: Taming Wireless Devices Cross-Layer Errors with Assistive Networked Edges
Jianqing Liu

$447,106 by National Science Foundation (NSF)
01/ 1/2023 - 07/31/2026

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.

ExpandQISE: Track 1: Virtual Quantum Networks: From Foundations to Field Tests
Jianqing Liu

$800,000 by National Science Foundation (NSF)
11/ 1/2022 - 08/31/2025

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.

Collaborative Research: CNS Core: SMALL: DrGPU: Optimizing GPU Programs via Novel Profiling Techniques
Xu Liu

$249,473 by National Science Foundation (NSF)
10/ 1/2021 - 09/30/2024

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.

Collaborative Research: NeTS: Small: Digital Network Twins: Mapping Next Generation Wireless into Digital Reality
Yuchen Liu

$300,000 by National Science Foundation (NSF)
10/ 1/2023 - 09/30/2026

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.

Formative Feedback for Writing
Collin Lynch

$499,973 by Education Testing Service
07/ 1/2021 - 06/30/2025

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.

CAREER: Explorable Formal Models of Privacy Policies and Regulations
Christopher Martens

$555,000 by National Science Foundation (NSF)
06/15/2019 - 05/31/2024

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

CAREER: Explorable Formal Models of Privacy Policies and Regulations (Supplement)
Christopher Martens

$16,000 by National Science Foundation (NSF)
06/15/2019 - 05/31/2024

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

Engaging Rural Students in Artificial Intelligence to Develop Pathways for Innovative Computing Careers
Bradford Mott ; Wookhee Min ; Veronica Catete

$1,166,886 by National Science Foundation (NSF)
05/ 1/2022 - 04/30/2025

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.

Combining Real-time Tasking and Data Parallelism within OpenMP under Linux, CARTA Core Project
Frank Mueller

$50,000 by Center for Accelerated Real Time Analytics (CARTA) - NCSU Research Site
01/ 1/2023 - 05/15/2024

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.

FFATA: QLCI-CI: NSF Quantum Leap Challenge Institute for Robust Quantum Simulation
Frank Mueller ; Gregory Byrd ; Huiyang Zhou

$1,125,000 by University of Maryland, College Park
09/ 1/2021 - 08/31/2026

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.

CAREER:Robust and lightweight formal methods for mobile robot system development
John-Paul Ore

$594,739 by National Science Foundation (NSF)
08/ 1/2024 - 07/31/2029

Open-source robot software aims to enable rapid system development but comes with little or no tooling for automated testing and analysis. This work utilizes model checking of behavior trees and abstract type inference of physical units to automatically suggest system tests and to help ensure the absence of certain classes of software defects. This CAREER proposal examines whole system representation and tooling across interdisciplinary boundaries. We aim to substantially reduce the cost and improve the scalability of lightweight formal methods for robotic software systems, thus laying the foundation for the next generation of automated testing and analysis of robotic systems.

CAREER: Understanding and Supporting Programmer Cognition
Christopher Parnin

$555,882 by National Science Foundation (NSF)
07/ 1/2021 - 06/30/2026

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.

CAREER: Improving Machine Learning Education through Data-driven Support for Pipeline Design and Implementation
Thomas Price

$644,883 by National Science Foundation (NSF)
08/15/2023 - 08/14/2028

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.

CAREER: Improving Machine Learning Education through Data-driven Support for Pipeline Design and Implementation
Thomas Price

$644,883 by National Science Foundation (NSF)
08/15/2023 - 08/14/2028

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.

Collaborative Research: Using Fine-Grained Programming Trace Data to Inform Disciplinary Models of Self-Regulated Learning in Computing Education
Thomason Price

$525,284 by National Science Foundation (NSF)
07/ 1/2023 - 06/30/2026

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.

Collaborative Research: CCRI: NEW: An Infrastructure for Sustainable Innovation and Research in Computer Science Education
Thomason Price ; Tiffany Barnes

$460,757 by National Science Foundation (NSF)
08/ 1/2022 - 07/31/2025

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.

CAREER: Increasing Trust and Reducing Abuse in Telephone Networks
Bradley Reaves

$606,848 by National Science Foundation (NSF)
07/ 1/2022 - 06/30/2027

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.

SFS: A Cybersecurity Educational Partnership for the Government Workforce
Douglas Reeves ; Sarah Heckman

$2,748,558 by National Science Foundation (NSF)
01/ 1/2020 - 12/31/2024

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.

PIPP Phase I: Real-time Analytics to Monitor and Predict Emerging Plant Disease
Jean Ristaino ; Ignazio Carbone ; Peter Ojiambo ; Christopher Jones ; Raju Vatsavai

$1,000,000 by National Science Foundation (NSF)
08/ 1/2022 - 01/31/2024

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.

DSFAS-AI: Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER)
David Roberts ; Michael Kudenov ; Cranos Williams ; Daniela Jones ; Sarah Barnhill

$648,722 by US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)
06/15/2022 - 06/14/2025

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.

NSF Computer and Information Science and Engineering Graduate Fellowship Award (CSGrad4US)- Jordan Esiason
Georgios Rouskas

$159,000 by Computing Research Association (CRA)
09/ 1/2023 - 08/31/2028

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

Cross-Site Function Chain Scheduling in Serverless Edge
Muhammad Shahzad

$174,777 by Cisco Systems, Inc.
02/ 1/2023 - 04/30/2024

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.

Collaborative Research:CSR:Medium: Scaling Secure Serverless Computing on Heterogeneous Datacenters
Xipeng Shen

$444,070 by National Science Foundation (NSF)
10/ 1/2023 - 09/30/2027

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.

Collaborative Research: CNS Core: Medium: Understanding and Strengthening Memory Security for Non-Volatile Memory
Xipeng Shen

$449,900 by National Science Foundation (NSF)
10/ 1/2021 - 09/30/2024

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.

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage
Xipeng Shen - Co-PI ; Wenyuan Tang

$750,000 by US Dept. of Energy (DOE) - Energy Efficiency & Renewable Energy (EERE)
06/ 1/2021 - 05/31/2024

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.

RI: Small: Foundations of Ethics for Multiagent Systems
Munindar Singh

$500,000 by National Science Foundation (NSF)
10/ 1/2021 - 09/30/2024

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.

Improving Software Testing Education through Lightweight Explicit Testing Strategies and Feedback
Kathryn Stolee ; Thomason Price

$299,998 by National Science Foundation (NSF)
07/ 1/2022 - 06/30/2025

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.

CAREER: Algorithmic Aspects of Pan-genomic Data Modeling, Indexing and Querying
Sharma Valliyil Thankachan

$603,271 by National Science Foundation (NSF)
01/ 1/2023 - 04/30/2027

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.

Department of Defense Cyber Scholarship Program (DoD CySP)
Ketchiozo Wandji

$68,333 by US Dept. of Defense (DOD)
09/ 6/2022 - 09/ 6/2024

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.

CAREER: Dependable and Secure Machine Learning Acceleration from Untrusted Hardware
Wujie Wen

$600,000 by National Science Foundation (NSF)
10/ 1/2023 - 09/30/2028

Fueled by machine learning (ML) model and hardware advancements, intelligence is transforming every walk of life. For critical applications like autonomous vehicles, ensuring inference dependability is essential. Unfortunately, current hardware cannot provide such a promise. This CAREER project aims to create a new paradigm of safeguarding ML execution against both passive hardware faults and active fault attacks. The novelties lie in the new capability development inside ML processing, and the cross-layer exploration of algorithm, architecture, and hardware security. The broader impacts include yielding practical solutions for ensuring the root of trust of accelerated intelligence services and abundant educational opportunities.

NCAE-C-001-2021: North Carolina State University (National Center of Academic Excellence in Cybersecurity - NCSU)
Laurie Williams ; William Enck

$2,981,264 by National Security Agency
09/15/2021 - 12/31/2024

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 ( 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.

Collaborative: SaTC: Frontiers: Enabling a Secure and Trustworthy Software Supply Chain
Laurie Williams ; William Enck ; Alexandros Kapravelos

$6,344,481 by National Science Foundation (NSF)
10/ 1/2022 - 09/30/2027

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.

SaTC: CORE: Small: Risk-based Secure Checked-in Credential Reduction for Software Development
Laurie Williams ; Bradley Reaves

$399,708 by National Science Foundation (NSF)
07/ 1/2021 - 06/30/2024

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.

FARM BILL: NRI: INT: Towards the Development of a Customizable Fleet of Autonomous Co-Robots for Advancing Aquaculture Production
Sierra Young ; Steven Hall ; John-Paul Ore ; Celso Castro Bolinaga ; Natalie Nelson

$499,245 by US Dept. of Agriculture (USDA) - National Institute of Food and Agriculture
11/ 1/2020 - 10/31/2024

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.

CAREER: WolfPack: An Application-Network Co-Design Framework for Performance-Guaranteed Real-time Applications at the Network Edge
Ruozhou Yu

$505,702 by National Science Foundation (NSF)
07/ 1/2021 - 06/30/2026

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.

SCH: A Personalized Wearable Rehabilitation Sensing System for Stroke Survivors
Yong Zhu ; Xiaogang Hu ; Alper Bozkurt ; Xu Liu ; Xipeng Shen

$1,199,998 by National Institutes of Health (NIH)
09/17/2021 - 08/31/2025

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.