Current Research Projects (by faculty)

The funded projects listed below are active projects and the funded running total for the active projects is on the left navigational menu.


Analysis of a Simple, Low-cost Intervention's Impact on Retention of Women in Computer Science
Bita Akram ; Tiffany Barnes ; Thomason Price ; Tzvetelina Battestilli

$174,938 by National Science Foundation (NSF)
07/ 1/2020 - 06/30/2022

Existing research suggests that institutions may be able to increase the persistence of women in STEM by increasing their self-assessed STEM ability. We propose conducting both a longitudinal field experiment (in Computer Science [CS] classes) and a lab experiment (with novice programmers) to assess the impact of unambiguous, direct performance feedback on women’s and men’s self-assessed CS ability and CS persistence. Beyond the support for our research provided by social-psychological theory, mediation analysis of pilot data from a field experiment found the predicted causal chain: the intervention increased women’s self-assessed CS ability, which then increased women’s CS persistence intentions.

Collaborative Research: Beyond CS Principles:Engaging Female High School Students in New Frontiers of Computing
Tiffany Barnes

$555,000 by National Science Foundation (NSF)
05/ 1/2020 - 04/30/2023

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.

REU Site: Socially Relevant Computing and Analytics
Tiffany Barnes

$405,000 by National Science Foundation (NSF)
03/ 1/2020 - 02/28/2023

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.

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.

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.

Collaborative Research: Scaling the Early Research Scholars Program
Veronica Catete ; Bita Akram ; Sarah Heckman ; Tiffany Barnes ; Tzvetelina Battestilli

$20,000 by University of California - San Diego
09/21/2020 - 08/31/2023

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.

CAREER: Improving Adaptive Decision Making in Interactive Learning Environments
Min Chi

$547,810 by National Science Foundation
03/ 1/2017 - 02/28/2022

For many forms of interactive environments, the system's behaviors can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for selecting the next action to take from a set of alternatives. The objective of this CAREER proposal is to learn robust interaction strategies that will lead to desirable outcomes in complex interactive environments. The central idea of this project is that strategies should not only be effective in complex interactive environments but they should also be efficient, focusing solely on the key features of the domain and the crucial decision points. These are the features and decisions that are not only associated with desirable outcomes, but without which the desirable outcomes are unlikely to occur.

LAS DO2 Chi-Machine Learning Integrity (MLI)
Min Chi

$95,334 by Laboratory for Analytic Sciences
01/ 1/2021 - 12/31/2021

Machine Learning Integrity (MLI). Operationalizing Machine Learning for Analysts. The IC is increasingly using artificial intelligence (AI) and Machine Learning as a means of coping with the vast, disparate, and dynamic data that it collects and processes. The operational environment and domains in which the techniques are being applied create specific challenges.

DIP: Integrated Data-driven Technologies for Individualized Instruction in STEM Learning Environments
Min Chi ; Tiffany Barnes

$1,999,438 by National Science Foundation
08/15/2017 - 07/30/2022

In this proposed work, our goal is to automatically design effective personalized ITSs directly from log data. We will combine Co-PI Dr. Barnes data-driven approach on learning what to teach with PI Dr. Chi’s data-driven work on learning how to teach. More specifically, we will explore across three important undergraduate stem domains: discrete math, probability, and programming.

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.

IUCRC Phase I NC State University: Center for Accelerated Real Time Analytics (CARTA)
Rada Chirkova

$747,647 by National Science Foundation (NSF)
06/ 1/2018 - 05/31/2023

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.

Phase I IUCRC NC State University: Center for Accelerated Real Time Analytics (CARTA)
Rada Chirkova

$32,000 by National Science Foundation (NSF)
06/ 1/2018 - 05/31/2023

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.

LAS DO2 Chirkova-MLI
Rada Chirkova

$97,212 by Laboratory for Analytic Sciences
01/ 1/2021 - 12/31/2021

Machine Learning Integrity (MLI): Operationalizing Machine Learning for Analysts. The IC is increasingly using artificial intelligence (AI) and Machine Learning as a means of coping with the vast, disparate, and dynamic data that it collects and processes. The operational environment and domains in which the techniques are being applied create specific challenges.

Challenges and Opportunities in Noise-Aware Implementations of Quantum Field Theories on Near-Term Quantum Computing Hardware
Patrick Dreher ; Alexander Kemper

$385,000 by Oak Ridge National Laboratories - UT-Battelle LLC
10/21/2019 - 10/31/2022

Quantum computing (QC) offers the potential to explore how recent advances in lattice field theories (LFT) can potentially explore aspects of HEP that have been inaccessible using digital computers. Unfortunately these quantum computers have noise and systematic errors that can complicate the performance of basic quantum field theory (QFT) implementations. Very little effort has been directed to understand how these factors impact LFT simulations on QC platforms. This proposal will explore how noise and systematic errors may be identified and mitigated to extend the coherence lifetimes of the qubits and capabilities of HEP LFT simulations.

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.

SaTC: CORE: Small: Detecting Vulnerabilities and Remediations in Software Dependencies
William Enck ; Bradley Reaves

$499,928 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2023

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.

Defining Security Policy in Distributed Environments using Network Views
William Enck ; Bradley Reaves

$1,033,306 by US Navy-Office Of Naval Research
12/ 1/2019 - 11/30/2022

Existing networking technologies are primarily focused on functionality, not security. Consequently, requirements of these technologies, such as fixed network topologies, lead to rigid architectures that fail to enable the network access control requirements of current and future computing environments. We propose the creation of a novel primitive called network views that allows a physical or virtual host to have a different set of accessible peers,regardless of network address or topological placement of those peers. We seek to explore and characterize the utility and practicality of network views in different network environments, ranging from traditional LANs to multi-site, multi-tenant networks such as those emerging in cloud and cellular networks. Our proposed design combines concepts from software-defined networking (SDN),operating systems access control, and distributed consensus protocols. Through these efforts, we seek to provide a new security foundation for the growing security needs of both public and private sector network operations.

Machine Learning for IT Service Assurances
Xiaohui Gu

$155,032 by Cisco Systems, Inc.
08/16/2021 - 08/15/2022

Production computing infrastructures, particularly multi-tenant cloud infrastructures, have become increasingly complex and require constant monitoring and maintenance. Cloud service providers are faced with the challenge of both high operation cost and daunting service downtime penalty. Existing monitoring tools continuously collect a large amount of metric and log data but still fail to answer the key operation questions about when and why a cloud infrastructure experiences a problem. In this project, we propose to develop a set of new machine learning technology to automatically detect and diagnose performance and security bugs in production cloud environments.

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: Transforming Computer Science Education Research Through Use of Appropriate Empirical Research Methods: Mentoring and Tutorials
Sarah Heckman

$406,557 by National Science Foundation
09/ 1/2015 - 08/31/2022

The computer science education (CSEd) research community consists of a large group of passionate CS educators who often contribute to other disciplines of CS research. There has been a trend in other disciplines toward more rigorous and empirical evaluation of various hypotheses. However, many of the practices that we apply to demonstrate rigor in our discipline research are ignored or actively avoided when performing research in CSEd. This suggests that CSEd is “theory scarce” because most publications are not research and do not provide the evidence or replication required for meta-analysis and theory building . An increase in empiricism in CSEd research will move the field from “scholarly teaching” to the “scholarship of teaching and learning” (SoTL) providing the foundation for meta-analysis and the generation of theories about teaching and learning in computer science. We propose the creation of training workshops and tutorials to educate the educators about appropriate research design and evaluation of educational interventions. The creation of laboratory packages, “research-in-a-box,” will support sound evaluation and replication leading to meta-analysis and theory building in the CSEd community.

CUE: Collaborative Research: Effective Peer Teaching Across Computing Pathways
Sarah Heckman ; Tzvetelina Battestilli ; Anna Howard

$98,987 by National Science Foundation (NSF)
01/ 1/2020 - 06/30/2022

Demand for computing is increasing across pathways; majors, minors, and computing in discipline. Peer teachers are critically needed to support student learning outcomes. This proposal builds on an existing NIC in peer teaching to expand support across computing pathways. We will assess the impact of peer teaching, particularly related to support with debugging, on 1) departmental and non-major course culture, student learning and support and 2) broadening participation of underrepresented groups in computing courses and as peer teachers. We will share our results and build a larger community through a 2nd offering of the Peer Teaching Summit.

transVRse: Automatic Viewpoint Computation and Navigation support in Virtual Reality
Arnav Jhala

$50,000 by Epic Games, Inc.
01/ 1/2021 - 12/31/2021

transVRse is an automatic viewpoint computation and navigation support toolkit in VR. The current version is being used for the analysis of para-hydrogen pathways in dynamic molecular simulations by the Ab Inito Materials Simulations Group at Duke and Hyperpolarization Lab at NCSU.

SHF: Small: Inter­-Request Workflow and Dataflow in Web Applications: a Modeling Framework and its Applications
Guoliang Jin

$350,000 by National Science Foundation (NSF)
08/15/2020 - 07/31/2023

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.

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.

Tools and Techniques to Improve the Granularity and Usability of Web Application Debloating
Alexandros Kapravelos

$389,312 by Arizona State University
02/11/2021 - 02/10/2023

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.

CHECRS: Cognitive Human Enhancements for Cyber Reasoning Systems
Alexandros Kapravelos

$884,817 by Arizona State University/DARPA
11/29/2018 - 05/29/2022

The recent Cyber Grand Challenge (CGC) showed progress in the ability of computers to discover and patch vulnerabilities, but these programs are still far from being able to compete against human players. In order to cope with the state-explosion problem that is now limiting our ability to automatically analyze binary programs, we need to design a new class of solutions inspired by expert humans behavior. In this project, instead of blindly analyzing as many nodes as possible trying to explore the search space exhaustively, we are going to develop new techniques to explore it more intelligently.

LAS DO2 Kapravelos - AUC
Alexandros Kapravelos

$93,854 by Laboratory for Analytic Sciences
01/ 1/2021 - 12/31/2021

Dr. Alexandros Kapravelos will explore a machine learning error avoidance system that occurs in parallel with a fuzzing campaign. He will leverage an existing JavaScript fuzzer to add an additional capability to learn and predict common errors with the goal of identifying code blocks that work together and that are incompatible to develop a more effective fuzzing campaign.

Real-time Cyber Knowledge Platform for Web Threats, CARTA Core Project
Alexandros Kapravelos

$60,000 by Center for Accelerated Real Time Analytics (CARTA) - NCSU Research Site
01/ 1/2021 - 12/31/2021

The web evolves continuously and we currently lack the tools to monitor how it is changing and how this affects the security of internet users. Characterizing website behavior will help both users and organizations to understand the website they visit/operate. Our goal in this project is to identify at real-time websites that their behavior diverges from their expected behavior and thus indicate that they have been compromised. We are going to develop a publicly available system that performs continuous website behavior analysis and reports of changes in behavior that occur over time.

REFLECT: Improving Science Problem Solving with Adaptive Game-Based Reflection Tools
James Lester ; Roger Azevedo (Psychology)

$1,300,000 by National Science Foundation
04/15/2017 - 03/31/2022

Reflection has long been recognized as a central component of effective learning. With the overarching goal of improving middle school students' science problem solving and learning outcomes, the REFLECT project has the objective of investigating a suite of theoretically grounded, adaptive game-based reflection tools to scaffold students' cognitive and metacognitive processes. The project will center on the design, development, and investigation of game-based learning tools for science education that adaptively scaffold students’ reflection through both embedded and retrospective support. It will culminate in a classroom experiment to study the impact of the adaptive reflection tools on both problem solving and learning. The results from this project will contribute significantly to theoretical and computational models of reflection, and produce both design principles and learning technologies that support the creation of effective learning environments.

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).

Health Quest: Engaging Adolescents in Health Careers with Technology-Rich Personalized Learning
James Lester, II

$1,378,755 by National Institutes of Health (NIH)
08/ 1/2017 - 07/31/2022

Leveraging intelligent game-based learning technologies, the Health Quest project focuses on developing and disseminating technology-rich resources to broaden the interests of adolescents in biomedical, behavioral and clinical research careers. The project centers on the development of technology-rich learning resources. These include a game-based learning environment featuring health careers as well as an online community that includes a speaker series featuring a broad range of health professionals. The final year of the project will see a full evaluation of the Health Quest program and its impact on students’ interest in biomedical, behavioral and clinical research careers.

Supporting Student Planning with Open Learner Models in Middle Grades Science
James Lester, II

$1,499,183 by National Science Foundation (NSF)
08/15/2018 - 07/31/2022

The ability to plan is a key element of learning. With the objective of improving middle school students' science learning, the project will investigate open learner models to scaffold student planning. The project will see the design, development, and investigation of an open learner model for student goal setting and planning. In contrast to the "classic" student models of intelligent tutoring systems, which are opaque, open learner models are inspectable: they enable students to inspect a learning environment's representation of their knowledge and competencies. Using the Future Worlds learning environment, the project will feature classroom studies that will investigate the impact of open learner models on both problem solving and learning in middle grades science.

EAGER: Collaborative Research: Building Capacity for K-12 Artificial Intelligence Education Research
James Lester, II

$99,976 by National Science Foundation (NSF)
08/15/2019 - 01/31/2022

This goal of this project is to build capacity for education research for K-12 artificial intelligence education. In particular, it will bring together experts in AI and learning sciences to develop a shared understanding and create a research agenda to bring evidence-based AI education to K-12 classrooms. We will organize and facilitate a series of two workshops focused on answering what and how to teach AI for K-12, with findings from the first workshop informing the second. We will also conduct broad analyses of how K-12 AI education research should respond to AI’s far-reaching societal impact.

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.

Collaborative Research: FW-HTF: Augmented Cognition for Teaching: Transforming Teacher Work with Intelligent Cognitive Assistants
James Lester, II ; Bradford Mott

$1,499,736 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2022

Effective teaching is the cornerstone of K-12 education. However, effective teaching occurs in complex workplaces that require teachers to cope with the real-time demands of providing effective learning experiences for large classrooms of students by skillfully bringing to bear their expertise in pedagogy and classroom management. Although there is enormous potential for enhancing teaching with technology-rich support that leverages artificial intelligence (AI), limited work has been done to investigate how emerging AI technologies can bring about fundamental improvements to the teaching profession. With recent advances in AI technologies for natural language processing, machine learning, and user-adaptive support, the time is ripe for transforming the professional lives of teachers. The objective of the proposed research is to design, develop, and evaluate the Intelligent-Augmented Cognition for Teaching (I-ACT) framework featuring intelligent cognitive assistants for K-12 teachers. A unique feature of I-ACT afforded by recent advances in machine learning will be its ability to optimize teacher support for collaborative learning at the individual student, group, and classroom levels

Collaborative Research: PrimaryAI: Integrating Artificial Intelligence into Upper Elementary Science with Immersive Problem-Based Learning
James Lester, II ; Bradford Mott

$985,585 by National Science Foundation (NSF)
09/ 1/2019 - 08/31/2022

Artificial intelligence has emerged as a technology that promises to have unprecedented societal impact. Integrating AI into the science curriculum holds significant potential for introducing students to deep science inquiry while simultaneously providing them with an experiential understanding of the role that AI can play in science problem solving. The proposed project will center on the design, development, and investigation of PrimaryAI, a curricular framework that integrates science and AI for upper elementary science education. Featuring an immersive game-based learning environment, PrimaryAI will use problem-based learning as the foundation for science inquiry in which students grades 3-5 will utilize AI tools to solve complex ecosystem problems within an immersive science adventure. Students will engage in scientific problem solving tightly integrating AI and science to learn about ecosystems phenomena, mechanisms, and components that comprise a system, and make inferences about change over time for biological systems. The project will use design-based research to understand how best to integrate AI and science in upper-elementary science classrooms.

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 - 09/ 4/2022

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.

Investigating Emergency Response Performance with VR-Based Intelligent User Interfaces
James Lester, II ; Bradford Mott ; Randall Spain

$1,112,175 by National Institute of Standards & Technology
06/ 1/2018 - 12/31/2021

First responders are seeing a significant increase in the amount and types of data available when responding to emergencies. To maximize the value of these data, user interfaces need to be designed that provide first responders with critical real-time information. Intelligent user interface design, in which the data and information presented to the user is adapted and tailored to the needs of individual users based on analytic information (e.g., expertise, task state, location), offers significant potential for improving performance, reducing mental workload, and facilitating effective decision-making. This project builds on a decade of research by the project team in developing intelligent game-based virtual learning environments. The goal of the project is to develop a virtual reality emergency response scenario that will serve as a test bed for evaluating the impact of intelligent user interfaces on first responder performance. In addition, the project will investigate the impact of providing adaptive support on task proficiency and whether alternative interaction methods (gesture-based vs. voice-based) reduce cognitive load and improve system interaction.

SCH: ChangeGradients: Promoting Adolescent Health Behavior Change with Clinically Integrated Sample-Efficient Policy Gradient Methods
James Lester, II ; Jonathan Rowe

$224,021 by University of California - San Francisco
12/13/2019 - 11/30/2021

The objective of the proposed research is to design, implement, and investigate ChangeGradients, a clinically integrated health behavior change system for adolescents. In a partnership with the University of California, San Francisco School of Medicine, we will create a computational behavior change framework based on sample-efficient policy gradient methods for reinforcement learning. The project will investigate a critical research question in health behavior change: how can a computational framework produce dynamically tailored interactive narratives that promote health behavior change for adolescents? ChangeGradients will support behavior change by generating personalized interactive narratives and delivering analytics to healthcare providers in a data-driven clinical intervention. ChangeGradients’ impact on health behavior change will be evaluated in a clinical study at the UCSF Benioff Children's Hospital.

Multimodal Visitor Analytics: Investigating Naturalistic Engagement with Interactive Tabletop Science Exhibits
James Lester, II ; Jonathan Rowe ; James Minogue

$1,951,956 by National Science Foundation (NSF)
03/ 1/2018 - 02/28/2022

Recent advances in multimodal learning analytics present new opportunities for investigating learning and engagement in informal education settings. In this project, we will investigate visitors’ learning experiences in science museums using multimodal visitor analytics, which marry the rich multi-channel data streams produced by fully-instrumented exhibit spaces and the data-driven modeling functionalities afforded by recent advances in machine learning. The project will leverage Future Worlds, a fully-instrumented prototype digital interactive exhibit about sustainability, which was developed and piloted by the project team in a previously funded NSF Informal Science Education proof-of-concept project. The research team will conduct a series of museum studies to investigate how learners interact with Future Worlds and other exhibits in a science museum, and will utilize learning analytic techniques to model visitors’ cognitive, affective, and behavioral components of learning and engagement. The project will produce a detailed empirical account of visitors’ learning experiences in a science museum, as well as an open-source software platform for conducting multimodal visitor analytics, which will help other informal education researchers utilize learning analytics with their own datasets

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:CNS Core:Small:Towards Efficient Cloud Services
Xu Liu

$249,840 by National Science Foundation (NSF)
08/19/2020 - 09/30/2023

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.

IGE: Learning the Entire Pipeline: Analyzing and Improving Graduate Engineering Education through Communities of Practice
Collin Lynch Co-PI ; Cesar Delgado ; Kook Han

$332,184 by National Science Foundation (NSF)
08/15/2021 - 07/31/2023

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.

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

Simulating Social Influence Based on Real-World Geographic Data: Emergent Narratives and Interactive Hypothesis Testing
Christopher Martens

$577,574 by US Air Force - Office of Scientific Research (AFOSR)
09/15/2020 - 09/14/2023

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.

CRII: SHF: Supporting Domain-Specific Inquiry with Rule-Based Modeling (Supplement)
Christopher Martens

$16,000 by National Science Foundation (NSF)
03/ 1/2018 - 02/28/2022

An increasingly common method for communicating and critiquing the emergent behavior of complex systems is interactive simulation, which can teach interactors about the way a system works by revealing system-level properties like feedback loops and tension between objectives. The Ceptre programming language provides a way to author interactive simulations in a rule-based way, amenable to both intuitive understanding and analysis. We propose to expand Ceptre’s audience by implementing a user interface that enforces syntax-level and type-level correctness of programs, which can be run and deployed in the browser for rapid prototyping.

Collaborative Research: Cyberinfrastructure for Robust Learning of Interconnected Knowledge
Noboru Matsuda

$386,884 by National Science Foundation (NSF)
07/15/2020 - 06/30/2023

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.

Collaborative Research: Cyberinfrastructure for Robust Learning of Interconnected Knowledge
Noboru Matsuda

$16,000 by National Science Foundation (NSF)
07/15/2020 - 06/30/2023

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.

SHF:Small: Mega Transfer: On the Value of Learning from 10,000+ Software Projects
Timothy Menzies

$472,024 by National Science Foundation (NSF)
10/ 1/2019 - 09/30/2022

Software analytics is a workflow that distills large amounts of low-value data into small chunks of very high-value data. A typical research paper in software analytics studies less than a few dozen projects. Such small samples can never be representative of something as diverse as software engineering. Perhaps it is time to stop making limited conclusions from tiny sets of software projects. To that end, we 3ill apply innovative transfer learning methods (based on very fast clustering and transfer learners based on very fast stream mining algorithms that use incremental hyperparameter optimizers) to the 10,000+ projects currently in Github.

Elements: Can Empirical SE be Adapted to Computational Science?
Timothy Menzies

$592,129 by National Science Foundation (NSF)
10/ 1/2019 - 09/30/2022

Standard methods in empirical software engineering (SE) needs to be adapted before it can be safely deployed in other domains like computational science. But what adaption methods are useful/useless? Are they cost effective? Do they work effectively across multiple data sets? We have some preliminary results suggesting that the work for (a) defect prediction but can we also adapt other tasks such as (b) test case prioritization, (c) effort estimation, (d) learning to avoid spurious false negatives from static code analysis, etc. Why is this important? Well, building software is hard. Building good software is even harder when developers have not formally studied SE (i.e. as in the case of many developers of computational science software developers). How can we capture and maintain expertise about software development, then make that expertise more widely available?

LAS DO2 Menzies- MLI
Timothy Menzies

$106,101 by Laboratory for Analytic Sciences
01/ 1/2021 - 12/31/2021

Machine Learning Integrity (MLI): Operationalizing Machine Learning for Analysts. The IC is increasingly using artificial intelligence (AI) and Machine Learning as a means of coping with the vast, disparate, and dynamic data that it collects and processes. The operational environment and domains in which the techniques are being applied create specific challenges.

SHF:Medium:Scalable Holistic Autotuning for Software Analytics
Timothy Menzies ; Xipeng Shen

$898,349 by National Science Foundation
07/ 1/2017 - 06/30/2022

This research proposes to advance the state of the art to holistic scalable autotuners, which tunes all levels of options for multiple optimization objectives at the same time. It will achieve this ambitious goal through the development of a set of novel techniques that efficiently handles the tremendous tuning space. These techniques take advantage of the synergies between all those options and goals by exploiting relevancy filtering (to quickly dispose of unhelpful options), locality of inference (that enables faster updates to out- dated tunings) and redundancy reduction (that reduces the search space for better tunings). This new autotuner will be a faster method for finding better tunings that satisfy more goals. To test this claim, this research will assess if this new tool can reduce the total computational resources required for effective SE data analytics by orders of magnitude.

Computing Innovation Fellows 2021 Project
Bradford Mott

$232,798 by Computing Research Association (CRA)
09/ 1/2021 - 08/31/2023

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.

Collaborative Research: Building a Computational Thinking Foundation in Upper Elementary Science with Narrative-Centered Maker Environments
Bradford Mott ; James Minogue ; Kevin Oliver

$1,599,339 by National Science Foundation (NSF)
08/ 1/2019 - 07/31/2022

Recent years have seen a growing recognition of the importance of computer science experience for today's K-12 students. Knowledge of computing is essential for students' success throughout their academic and professional careers. Engaging elementary students in computational thinking through the creation of rich interactive computational narratives offers an innovative approach to building students’ computational thinking practices and interest in computing. This project will engage students in a broad range of computing activities centered on creating digital interactive narratives. The project will see the development of a narrative-centered maker environment that introduces computational thinking into upper elementary science education emphasizing connections to the Next Generation Science Standards.

Unifying Circuit-Model Quantum Computing and Quantum Annealing
Frank Mueller

$225,000 by Los Alamos National Laboratory (LANL)
04/13/2021 - 09/30/2023

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.

SaTC: CORE: Small: Enhanced Security and Reliability for Embedded Control Systems
Frank Mueller

$500,000 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2022

CPS and IoT devices are inherently networked, which exposes them to malware attacks. We propose to significantly increase the cyber security specifically of CPS and IoT computing devices by developing real-time monitoring techniques that defeat cyber-attacks.

Large-Scale Automatic Analysis of the OAI Magnetic Resonance Image Dataset
Frank Mueller

$331,603 by UNC - UNC Chapel Hill
08/15/2017 - 07/31/2022

The goal of this proposal is to optimize and to openly provide to the OA community a new technology to rapidly and automatically measure cartilage thickness, appearance and changes on magnetic resonance images (MRI) of the knee for huge image databases. This will allow assessment of trajectories of cartilage loss over time and associations with clinical outcomes on an unprecedented scale; future work will focus on incorporating additional disease markers, ranging from MRI-derived biomarkers for bone and synovial lesions, to biochemical biomarkers, to genetic information.

PFCQC: STAQ: Software-Tailored Architecture for Quantum Co-Design
Frank Mueller ; Huiyang Zhou ; Alexander Kemper

$623,408 by Duke University
08/ 1/2021 - 07/31/2023

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.

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.

SHF: SMALL: Effective and Equitable Technical Interviews in Software Engineering
Christopher Parnin

$300,000 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2023

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.

Collaborative Research: SaTC: TTP: Small: eSLIC: Enhanced Security Static Analysis for Detecting Insecure Configuration Scripts
Christopher Parnin

$199,978 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2023

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.

SHF: SMALL: Automated Discovery of Cross-Language Program Behavior Inconsistency
Christopher Parnin ; Kathryn Stolee

$499,994 by National Science Foundation (NSF)
08/ 1/2020 - 07/31/2023

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.

Intelligent Support for Creative, Open-ended Programming Projects
Thomason Price ; Tiffany Barnes ; Christopher Martens

$749,920 by National Science Foundation (NSF)
08/ 1/2019 - 07/31/2022

We will develop new data-driven methods to support students automatically as they create novel, open-ended and creative, computational artifacts. Specifically, we will develop techniques to adaptively scaffold project design and planning, detect students' programming goals, offer on-demand example-based support and tailor help to students needs through an interactive help interface. We will augment the popular Snap programming environment, which is already used in hundreds of high school and college classrooms, with these features and evaluate their effective in a series of experiments designed to explore how students approach open-ended tasks and how best to support them.

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.

CNS Core:Small:On Parallelizing Optical Network Design Problems:Towards Network Optimization as a Service
George Rouskas

$439,148 by National Science Foundation (NSF)
10/ 1/2019 - 09/30/2022

Planning, deploying, and engineering the networks that make up the Internet infrastructure involves complex problems that we will refer to generically as "network design" problems. Effective and efficient solutions to network design problems are crucial to the operation and economics of the Internet and its ability to support critical and reliable communication services. With this research project we aim to make contributions that will lead to new approaches for tackling network design problems in a scalable manner. In particular, we will develop parallel solutions that are applicable to a wide range of problems by exploiting a feature common to all, namely, that the optimization process incorporates both a routing aspect and a resource allocation aspect.

Investigating the Role of Interest in Middle Grade Science with a Multimodal Affect-Sensitive Learning Environment
Jonathan Rowe

$414,761 by National Science Foundation (NSF)
07/15/2020 - 06/30/2023

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.

Serverless Edge
Muhammad Shahzad

$169,646 by Cisco Systems, Inc.
08/ 1/2021 - 07/31/2022

In this project, we propose to develop methods to enable serverless computing at the edge. Our primary objectives include developing methods to cache serverless functions within the edge network and to optimally schedule functions and function-chains while satisfying the latency SLAs. Our secondary objectives include studying the feasibility of enabling stateful applications on serverless platforms and customizing serverless edge for certain targeted applications.

Radio Frequency Based Indoor Mapping and Human Discovery
Muhammad Shahzad

$199,999 by University of South Florida Institute of Applied Engineering
05/ 1/2021 - 04/30/2022

In this project, our objective is to use radio frequency signals to generate indoor maps of any given building without entering it. In generating these maps, our secondary objective is to discover any humans that are present in the building, identify their locations, and determine which of them are stationary and which are mobile.

CAREER: Algorithmic Challenges and Opportunities in Spatial Data Analysis
Donald Sheehy

$277,465 by National Science Foundation (NSF)
08/23/2019 - 01/31/2022

Spatial data takes many forms including configuration spaces of robots or proteins, collections of shapes, and physical models. These data sets often contain intrinsic, nonlinear, low-dimensional structure hidden in complex high-dimensional input representations.To uncover such structure one needs to adapt to local changes in scale, recognize multiscale structure, represent the intrinsic space underlying the data, compute with coarse approximate distances, and integrate heterogeneous data into meaningful distance functions. There is a need for algorithms and data structures that can search, represent, and summarize such data sets efficiently. The PI will develop new data structures, models of computation, sampling theories, and metrics for addressing these challenges.

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.

HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications
Xipeng Shen

$508,977 by US Dept. of Energy (DOE)
09/23/2020 - 09/22/2023

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.

CSR:Small:Supporting Position Independence and Reusability of Data on Byte-Addressable Non-Volatile Memory
Xipeng Shen

$499,998 by National Science Foundation (NSF)
08/16/2017 - 07/31/2022

Byte-Addressable Non-Volatile Memory (NVM) is the upcoming next generation of memory with tremendous potential benefits. This proposal is about offering programming system-level support of persistency on NVM. Particularly, it focuses on effective support of the usage of dynamic data structures on NVM.

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.

Foureye: Cyber Defensive Deception based on Hypergame Theory for Tactical Networks
Munindar Singh

$95,000 by Virginia Polytechnic Institute and State University (aka Virginia Tech)
12/ 1/2020 - 04/30/2023

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.

RI: Small: Principles of Normative Multiagent Systems for Decentralized Applications
Munindar Singh

$450,000 by National Science Foundation (NSF)
10/ 1/2019 - 09/30/2022

This project will investigate theoretical models and programming techniques for decentralized applications. Emerging technologies show great potential in helping bring about a new era of automated contracts that enables flexible transactions among independent parties---with applications in finance, healthcare, pharmaceuticals, among other domains. This project will go beyond current approaches by providing a new declarative model that is able to handle the challenging computing-related aspects of real-life contracts. This model is accompanied with techniques that provide guidance on how to specify and enact contracts in a manner that is precise, flexible, and eliminates unnecessary information sharing.

Realizing Cyber Inception: Toward a Science of Personalized Deception for Cyber Defense
Munindar Singh

$375,360 by University of Southern California via US Army Research Office
09/ 1/2017 - 12/31/2021

Frequent security breaches have highlighted both the growing importance of cybersecurity and weaknesses of traditional methods such as firewalls, malware detection, intrusion detection, and prevention technologies. To leap ahead of attackers, we must move beyond passive defense strategies toward a new science of interactive personalized deception for cyberdefense. Our proposed approach involves (1) building models of attackers and their propensities and (2) characterizing computers, networks, users, and their relationships and interactions so as to enable realistic deception. We will develop a modular framework for evaluation of the key deception techniques consisting of a pluggable game-based scaffolding.

CAREER: On the Foundations of Semantic Code Search
Kathryn Stolee

$500,000 by National Science Foundation (NSF)
08/ 1/2018 - 07/31/2023

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.

CHS: Small: Adaptive Rendering and Display for Emerging Immersive Experiences
Ben Watson

$497,177 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2023

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.

Secure Software Supply Chain
Laurie Williams

$123,669 by Cisco Systems, Inc.
08/16/2021 - 08/15/2022

A May 2021 White House Executive Order on Cybersecurity contains a specific focus on the role the private sector plays in fostering a more secure cyberspace and an entire section on enhancing the security and integrity of the software supply chain. Organizations, such as Cisco, that supply “critical software” to the US government must comply with secure software supply chain practices. We propose research projects to aid Cisco in supply chain security. The primary goal of this project is to assist security analysts in identifying suspicious behavior during the build and deployment processes through an empirical analysis of build and deployment logs. We also will work with Cisco to develop machine learning models to automatically identify malicious commits to repositories through the development and validation of a commit-anomaly detector. We will also partner to leverage the information contained in a Software Bill of Materials (SBoM) to reduce supply chain security risk.

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 (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.

SHF: Small: Detecting the 1%: Growing the Science of Vulnerability Detection
Laurie Williams ; Timothy Menzies

$499,998 by National Science Foundation (NSF)
10/ 1/2019 - 09/30/2022

Software practitioners need methods to prioritize security verification efforts through the development of practical vulnerability prediction models. The PIs of this project have conducted extensive research of software analytics and vulnerability prediction algorithms. Based on that work, we can assert that vulnerability predictors usually use old data mining technology, some of which dates back several decades. This proposal will explore numerous better ways to build vulnerability predictors.

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.

Science of Security Lablet: Impact through Research, Scientific Methods, and Community Development
Laurie Williams ; Munindar Singh

$467,750 by US Dept. of Defense (DOD)
04/ 4/2018 - 09/14/2022

This project proposes the continuation of the Science of Security Lablet at NC State University. Science of Security refers to the study of cybersecurity from an explicitly scientific perspective. Cybersecurity encompasses elements of technology, human behavior, and policy. Science of Security seeks to identify and apply the appropriate scientific principles on cybersecurity problems, enhancing rigor and reproducibility, thereby improving the transfer of research to practice. This Lablet provides a home for investigations into diverse topics pertaining to a Science of Security. The Lablet will support the three major elements of a Science of Security: research, scientific methods, and community engagement.

Science of Security Lablet: Impact through Research, Scientific Methods, and Community Development - Additional Funding
Laurie Williams ; Munindar Singh

$537,539 by National Security Agency
04/ 4/2018 - 09/14/2022

This project proposes the continuation of the Science of Security Lablet at NC State University. Science of Security refers to the study of cybersecurity from an explicitly scientific perspective. Cybersecurity encompasses elements of technology, human behavior, and policy. Science of Security seeks to identify and apply the appropriate scientific principles on cybersecurity problems, enhancing rigor and reproducibility, thereby improving the transfer of research to practice. This Lablet provides a home for investigations into diverse topics pertaining to a Science of Security. The Lablet will support the three major elements of a Science of Security: research, scientific methods, and community engagement.

Science of Security Lablet: Impact through Research, Scientific Methods, and Community Development - Additional Funding
Laurie Williams ; Munindar Singh

$356,447 by National Security Agency
04/ 4/2018 - 09/14/2022

This project proposes the continuation of the Science of Security Lablet at NC State University. Science of Security refers to the study of cybersecurity from an explicitly scientific perspective. Cybersecurity encompasses elements of technology, human behavior, and policy. Science of Security seeks to identify and apply the appropriate scientific principles on cybersecurity problems, enhancing rigor and reproducibility, thereby improving the transfer of research to practice. This Lablet provides a home for investigations into diverse topics pertaining to a Science of Security. The Lablet will support the three major elements of a Science of Security: research, scientific methods, and community engagement.

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

$1,198,348 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.

Collaborative Research: CNS Core: Small: Robust Resource Planning and Orchestration to Satisfy End-to-End SLA Requirements in Mobile Edge Networks.
Ruozhou Yu

$142,500 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2023

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.