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: The STARS Aligned: How the STARS Computing Corps Broadens Participation in Computing
Tiffany Barnes

$199,390 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2021

The goal of this STARS proposal is to demonstrate the impact of the STARS Computing Corps on broadening the participation of women and underrepresented minorities in computing, at a national scale. To do so, STARS will research its cohort-based model that has engaged departments, faculty, and students in an inclusive community of practice, continue its thriving RESPECT BPC research and STARS Celebration BPC leadership conferences and improve recognition and the research base for BPC and STARS efforts. Collectively, these efforts will demonstrate how STARS has served as a national resource that catalyzes, develops, and celebrates BPC leadership for computing students, alumni, and faculty.

EAGER: Collaborative Research: Enhancing Impact of Broadening Participation in Computing Efforts through the STARS Cohort Conference Attendance Program
Tiffany Barnes

$47,432 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2020

In a recent Dear Colleagues Letter [cite], NSF outlined their commitment to “enhance the community's awareness of and barriers to broadening participation in computing (BPC) as well as to provide information and resources to principal investigators (PIs) so that they can develop interest, skills, and activities in support of BPC at all levels”. As part of this new initiative, NSF has launched a small pilot of a new requirement that PIs include plans for meaningful BPC activities in their proposals. In March 2018, NSF sponsored the BPCNet workshop to bring together BPC researchers and practitioners, including those from the NSF-funded BPC Alliances program, to identify approaches to support new PIs to engage in BPC efforts in meaningful ways. Our experience in leading the STARS Computing Corps demonstrates the value of cohort-based efforts to broaden participation in computing for individual students and computing departments. In response to the call for action presented to attendees at the NSF BPCNet workshop, the goal of this proposal is to provide a scaffolded approach to engaging faculty in cohort-based BPC efforts. As a starting point, the STARS Computing Corps proposes to prepare faculty to support diverse cohorts of students to attend diversity-oriented conferences through scholarships and targeted activities that build community and a sense of belonging for the attending students and ignite efforts to broaden participation within their own computing depart

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.

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.

PFI: BIC: FEEED: Flexible, Equitable, Efficient and Effective Distribution
Min Chi ; Julie Ivy

$400,000 by UNC - NC A & T State University
08/ 1/2017 - 07/31/2021

Nonprofit hunger relief organizations operate in a complex environment consisting of a large and diverse donor base and a dynamic distribution network. These organizations generate a large amount of unstructured and complex data on food collection, inventory management, and distribution activities. However, existing information systems lack the infrastructure to interpret this large-scale data to provide real-time policy recommendations and support operational and strategic decision-making. The proposed smart service system will synthesize data from disparate sources to create a real-time perspective of the environment and learn from the actions of the decision maker. Specifically, this system will automatically predict, visualize, and recommend decisions that will advance operational effectiveness of food collection, distribution, and resource management in a way that is efficient and equitable and will identify opportunities to improve a food bank’s capability to satisfy hunger need.

Development of a Nearly Autonomous Management and Control System for Advanced Reactors
Min Chi - (Co-PI) ; Maria Avramova ; Abhinav Gupta ; Nam Dinh

$2,686,834 by US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)
10/ 1/2018 - 03/31/2021

The proposed project seeks to establish a technical basis for, and preliminary development of, a Nearly Autonomous Management and Control (NAMAC) system in advanced reactors. The system is intended to provide recommendations to operators during all modes of plant operation except shutdown operations: plant evolutions ranging from normal operation to accident management. These recommendations are to be derived within a modern, artificial-intelligence (AI) guided system, making use of continuous extensive monitoring of plant status, knowledge of current component status, and plant parameter trends; the system will continuously predict near-term evolution of the plant state, and recommend a course of action to plant personnel.

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.

LAS DO1 Chirkova - 3.3 Computational Social Science
Rada Chirkova

$186,023 by Laboratory for Analytic Sciences
01/ 1/2019 - 12/31/2020

LAS DO1 Chirkova - 3.3 Computational Social Science

CRII: SaTC: Analyzing Information Leak in Smart Homes
Anupam Das

$174,995 by National Science Foundation (NSF)
06/ 1/2019 - 05/31/2021

We live in an increasingly connected world where we spend a large part of our time interacting with a wide range of Internet of Things (IoT) devices. While all these IoT devices provide convenience through automation of appliances, such conveniences often come at the cost of sharing very personal data about our lifestyles. This personal data can then not only be used to serve targeted ads, but can also be misused by repressive governments and cybercriminals. In this project, the PI proposes to analyze the extent to which IoT devices, commonly found in smart homes, leak sensitive information about ourselves.

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 - 10/31/2021

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.

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.

AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless
Ismail Guvenc ; Rudra Dutta ; Mihail Sichitiu ; Brian Floyd ; Thomas Zajkowski

$6,000,290 by PAWR, LLC.
12/16/2019 - 09/30/2020

We propose AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless, a first-of-its-kind aerial wireless experimentation platform to be developed in close partnership between NCSU, Wireless Research Center of North Carolina (WRCNC), Mississippi State University (MSU), University of South Carolina (USC), City of Raleigh, Town of Cary, Town of Holly Springs, North Carolina Department of Transportation (NCDOT), and numerous other project partners. With a major focus being on aerial communications within low altitude airspace, AERPAW will develop a software defined, reproducible, and open-access advanced wireless platform with experimentation features spanning 5G technologies and beyond. NCSU, USC, and MSU researchers have existing UAS experimentation capabilities and ongoing experimental research activities involving wireless technologies spanning software defined radios (SDRs), LTE, WiFi, ultra-wideband (UWB), IoT, and millimeter wave (mmWave), which will form the initial baseline framework for the AERPAW platform. To deploy AERPAW, NCSU will work closely with NCDOT’s Integration Pilot Program, a three-year FAA project that allows BVLOS UAS experimentation for medical supply delivery in North Carolina, in close collaboration with NCSU, several UAS companies, municipalities, and a medical institution. Initial flight tests have already started within the Raleigh area, and will be expanding to other parts of the state in 2019 and beyond. Any additional FAA permits, as necessary, will be secured by AERPAW team in close collaboration with NCDOT.

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 - 08/31/2021

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 - 06/30/2021

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.

SHF:Small: Enabling Scalable and Expressive Program Analysis Notifications
Sarah Heckman ; Emerson Murphy-Hill

$265,853 by National Science Foundation (NSF)
08/15/2017 - 07/31/2021

Previous research shows that existing notifications produced by integrated development environments are poorly understood and overwhelming to programmers. We propose building on our prior work to create a new architecture for program analysis notifications that enables toolsmiths to create scalable and understandable notifications for a variety of program analysis tools.

LAS DO1 OY- Jhala 4.0 Triage
Arnav Jhala

$64,712 by Laboratory for Analytic Sciences
05/ 1/2020 - 12/31/2020

We are interested in projects that will develop and demonstrate methods and tools to identify data relevant to a given analytic question within diverse and potentially large data sets. The process of finding the “right” data to support an analysis could potentially introduce significant errors and biases, and so methods and tools for data triage must be characterized in a way that makes clear their appropriate use in rigorous analytic processes. Finally, data increasingly must be discovered within less traditional formats, such as audio or speech recordings or video, that challenge more common triage and search techniques.

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.

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.

SaTC: CORE: Medium: Collaborative: Taming Web Content Through Automated Reduction in Browser Functionality
Alexandros Kapravelos

$406,609 by National Science Foundation
09/ 1/2017 - 08/31/2021

The browser is constantly evolving to meet the demands of Web applications. Although this evolution supports the innovation that we see on the internet, there are security implications that we need to consider, such as attacks against the browser that leverage bugs that occur from the rapid development. In this project, we plan to examine how certain web applications work and associate their behavior directly with the corresponding browser functionality. Our goal is to be able to characterize what functionality is need from the browser when rendering a page and certain components. By building a system like this we will be able to identify for example what is needed from the browser to render a web advertisement. To better protect the internet users, we are going to leverage that information so that we can identify when web applications diverge from their expected behavior and attack the users' browser. We will use this information to limit the exposed functionality to the web applications and eliminate this way multiple classes of attacks, such as browser fingerprinting and drive-by downloads.

Collaborative Research: Big Data from Small Groups: Learning Analytics and Adaptive Support in Game-based Collaborative Learning
James Lester

$1,249,611 by National Science Foundation
10/ 1/2016 - 09/30/2021

The proposed project focuses on integrating models of game-based and problem-based learning in a computer-supported collaborative learning environment (CSCL). As groups of students solve problems in these environments, their actions generate rich and dynamic streams of fine-grained multi-channel data that can be instrumented for investigating students' learning processes and outcomes. Using the big data generated by small groups, we will leverage learning analytics to provide adaptive support for collaboration that will allow these models to be used at larger scales in real classrooms. The project will study CSCL in the context of an environmental-science-based digital game that will employ specific strategies to support the problem-based learning goals of helping students construct explanations, reason effectively, and become self-directed learners. In problem-based learning, students are active, intentional learners who collaboratively negotiate meaning. The project will embed models induced using learning analytic techniques inside of a digital game environment to enable students to cultivate collaborative learning competencies that translate to non-digital classroom settings.

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

$1,301,820 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 - 07/31/2021

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 - 01/31/2021

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: 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.

DeepGen: Dynamic Generation of Training Simulation Scenarios with Deep Reinforcement Learning (W911NF-17-S-0003: CCE-HS-3: Training)
James Lester, II ; Bradford Mott ; Jonathan Rowe

$1,398,004 by US Army - Army Research Laboratory
12/ 8/2017 - 12/ 7/2021

Automated scenario generation offers considerable promise for addressing the needs of simulation-based training. Given recent advances in machine learning, including artificial neural networks and deep learning, data-driven approaches to automatically generating scenarios that are customized to the cognitive and affective characteristics of individual learners hold great potential. This project will investigate a critical research question for adaptive simulation-based training: how can we devise generalizable, data-driven scenario generation models that dynamically adapt training events to achieve target learning objectives in simulation-based virtual training environments? To answer this question, the project will investigate a data-driven framework for dynamic scenario generation that formalizes the task as a deep reinforcement learning problem. We will demonstrate the generalizability of the approach by investigating its implementation in multiple distinct simulation-based virtual training environments.

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.

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

$124,021 by University of California - San Francisco
12/13/2019 - 11/30/2020

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 - 02/28/2021

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

Developing Integrated Teaching Platforms to Enhance Blended Learning in STEM
Collin Lynch ; Tiffany Barnes ; Sarah Heckman

$599,992 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2021

Modern computer science classrooms support student learning by integrating a large number of educational tools and platforms from forums to intelligent tutors and automated tests. This proliferation of tools can overwhelm students who have difficulty navigating across platforms or connecting the information between platforms. We propose to develop an open platform for student and instructor guidance that supports data integration and analysis. The system will be designed to synthesize student interaction data from a rich set of educational tools. The integrated data and will provide the basis for automated analysis of students' work and work habits allowing for automated and instructor-driven interventions to support student learning. Additionally, the integrated system will provide pedagogical guidance on how students can best use classroom learning tools not individually, but in concert.

Collaborative Research: Fostering Collaborative Computer Science Learning with Intelligent Virtual Companions for Upper Elementary Students
Collin Lynch (co-PI) ; Eric Wiebe

$1,399,088 by National Science Foundation
08/15/2017 - 07/31/2021

The University of Florida and North Carolina State University jointly propose FLECKS, a Design and Development proposal for the NSF's Discovery Research PreK-12 (DRK-12) program. FLECKS (Friendly Learning Environment for Kids' Computer Science) addresses the pressing need for the development of fundamental computer science competencies in upper elementary-school children. The goal of the proposed project is to design, develop, and investigate FLECKS, an intelligent learning environment to teach collaborative computer science problem solving. Collaboration is a central academic and professional practice in computational thinking, yet it presents many challenges for elementary school students. Students often struggle to collaborate successfully due to individual differences in academic status; gender; cultural background; personality; attitudes toward collaboration; or attitudes toward learning. In order to address these challenges, FLECKS will provide dyads of students with a rich, scaffolded environment where they use an interactive online coding environment to engage in computer science challenges related to their STEM subject areas. Central to the innovation is the way in which the dyads are supported. FLECKS are animated virtual characters that take a rich set of multimodal features as input, and then adapt to students’ patterns of collaboration, including who has control of the keyboard and mouse; who speaks when; and the problem-solving actions the students take in the online environment.

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

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.

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.

Developing An Online Environment For Learning Algebra By Teaching A Synthetic Peer
Noboru Matsuda

$1,399,947 by US Dept. of Education (DED)
05/ 9/2019 - 08/31/2021

In this project, researchers will develop and evaluate an online game-like environment for middle school students to solve and learn algebra linear equations by teaching a simulated peer student. Learning by teaching is a promising style of instruction, with evidence supporting that when students engage in peer tutoring there is a benefit for both the tutee and tutor.

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 DO1 Menzies - 2.4 Analytics, AI and Machine Learning
Timothy Menzies

$255,684 by Laboratory for Analytic Sciences
01/17/2019 - 12/31/2020

LAS DO1 Menzies - 2.4 Analytics, AI and Machine Learning

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

$898,349 by National Science Foundation
07/ 1/2017 - 06/30/2021

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.

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.

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.

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

$500,000 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2021

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.

Software Support for Heterogeneous Memories in HPC
Frank Mueller

$315,429 by TRIAD National Security, LLC Formerly Los Alamos National Laboratory (LANL)
11/ 9/2018 - 09/30/2021

This project proposes to explore different solutions of software support for heterogeneous memory architectures for future supercomputers. Its primary aim will be to seamlessly integrate the new memory technologies in to the existing architecture support while taking full advantage of its characteristics for existing HPC applications and making the programmers job easier for developing new applications for the future systems.

HPC Power Modeling and Active Control
Frank Mueller

$386,290 by Lawrence Livermore National Laboratory via US Department of Energy
10/25/2016 - 09/30/2020

As we approach the exascale era, power has become a primary bottleneck. The US Department of Energy has set a power constraint of 20MW on each exascale machine. To be able achieve one exaflop in 20MW,it is necessary that we use power intelligently to maximize performance under a power constraint. In this work, we propose to alleviate the shortcomings of current HPC systems in addressing power constraints by (1) power-aware machine partitioning, (2) power-constrained job scheduling, (3) systematic provisioning and procurement of hardware under a power cap, (4)modeling of network, deep memories, and storage, as well as (5)investigating the inter-dependence between power and cooling.

SHF: Small: Retrospective and Prospective Studies of the Effects of Gender Bias in Software Engineering
Emerson Murphy-Hill

$498,461 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2021

Gender bias in the workplace has been documented in a variety of studies, and recent work in software engineering has likewise revealed how bias effects the tech industry. This project seeks to better understand the causes and effects of gender bias in software engineering, by studying the people and artifacts who practice it.

CSR:Medium:SmartChainDB - Enabling Smart Marketplaces With A Scalable Semantically-Enhanced Blockchain Platform
Kemafor Ogan ; Alessandra Scafuro ; Binil Starly

$499,773 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2021

This project seeks to develop a platform-SmartChainDB for supporting Smart Marketplaces in trustless environments. Such marketplaces should enable efficient assessment of bids in response to service requests without a-priori trust establishment between parties. In domains like Digital Manufacturing, job bid assessments are very time consuming efforts that take order of months. The platform will be developed by extending a BlockChain database for managing trust, with transaction types necessary to support a protocol for service requests and response bids. Another key extension will be semantic-enablement of the BlockChain database. A proof-of-concept prototype in Smart Manufacturing will be developed using SmartChainDB.

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.

SHF: SMALL: DockerizeME: Automatic Inference and Repair of Computing Environments
Christopher Parnin

$345,875 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2021

Data scientists perform analysis that society increasingly relies on. As data and analysis grow in complexity, so does the computing environments required to run that analysis. A wide variety of tools have been created to help data scientists do their jobs, yet the computing environments used for these analyses are often difficult to reproduce and vary significantly from tool to tool. As a result, data scientists may waste time trying to share, reproduce, and scale these computing environments, instead of making their analysis more thorough and reliable. In this project, we describe how we can infer configurations from existing code snippets and computing environments and automatically create application containers for running and scaling the computations. Further, based on our infrastructure, we can provide the ability to repair code and code environments in order to perform automated maintenance of computing environments.

SHF: SMALL: DockerizeME: Automatic Inference and Repair of Computing Environments (Supplement)
Christopher Parnin

$3,000 by National Science Foundation (NSF)
10/ 1/2018 - 09/30/2021

Data scientists perform analysis that society increasingly relies on. As data and analysis grow in complexity, so does the computing environments required to run that analysis. A wide variety of tools have been created to help data scientists do their jobs, yet the computing environments used for these analyses are often difficult to reproduce and vary significantly from tool to tool. As a result, data scientists may waste time trying to share, reproduce, and scale these computing environments, instead of making their analysis more thorough and reliable. In this project, we describe how we can infer configurations from existing code snippets and computing environments and automatically create application containers for running and scaling the computations. Further, based on our infrastructure, we can provide the ability to repair code and code environments in order to perform automated maintenance of computing environments.

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.

CRII: SaTC: Techniques for Measuring and Characterizing Robocalls
Bradley Reaves

$174,999 by National Science Foundation (NSF)
05/ 1/2019 - 04/30/2021

Robocalls are unwanted spam calls, and consumers are plagued by billions of calls annually. These calls have significantly degraded the usefulness of the global telephone network. The goal of this project is to develop techniques to measure the prevelence and characteristics of robocalls. This work will pave the way for a more trustworthy and useful phone 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.

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.

WiFi based Indoor Mapping and Human Discovery
Muhammad Shahzad

$329,335 by University of North Carolina System Office (formerly UNC - General Administration)
02/15/2019 - 02/14/2021

In this project, our objective is to use WiFi’s wireless channel metrics to generate indoor map 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.

NeTS: Small: Fine-grained Measurement of Performance Metrics in the Internet of Things
Muhammad Shahzad

$449,999 by National Science Foundation
10/ 1/2016 - 09/30/2020

PI proposes to develop a framework for passive and fine-grained measurements of the performance metrics in the Internet of Things, which include both Quality of Service metrics such as latency, loss, and throughput and Resource Utilization metrics such as power consumption, storage utilization, and radio on time etc. Measurements of these performance metrics can be used reactively by network operators to perform tasks such as detecting and localizing offending flows that are responsible for causing delay bursts, throughput deterioration, or even power surges. These measurements can also be used proactively by network operators to locate and preemptively update any potential bottlenecks.

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: PPoSS: Planning: Scaling Secure Serverless Computing on Heterogeneous Datacenters
Xipeng Shen

$76,611 by National Science Foundation (NSF)
10/ 1/2020 - 09/30/2021

The project will try to explore innovative approaches to enhance the scalability, security, and performance of serverless computing on datacenters that are equipped with heterogeneous computing units and memory.

XPlacer: Extensible and Portable Optimization of Data Placement in Memory
Xipeng Shen

$235,398 by Lawrence Livermore National Laboratory
01/29/2018 - 09/30/2020

Modern supercomputers with heterogeneous components (e.g., GPUs) feature complex memory systems to meet the ever growing demands for data by processors. Putting data into the proper part of a memory system is essential for program performance, but is difficult to do. To address this challenge, we propose a new paradigm featuring three interacting components: 1) an extensible memory specification language to describe memory properties, 2) a compiler for analyzing data access patterns and transforming code for runtime adaptation, and 3) a data placement runtime to find and materialize the best data placements on the fly. The result will be a software framework (named XPlacer) that transforms OpenMP code to automatically place its data in memory in a way best suiting the GPU architecture, inputs, and program phases.

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.

LAS DO1 Singh - 2.1 Human- Machine Collaboration
Munindar Singh

$210,406 by Laboratory for Analytic Sciences
01/17/2019 - 12/31/2020

LAS DO1 Singh - 2.1 Human- Machine Collaboration - Workflows

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.

SHF: Small: Supporting Regular Expression Testing, Search, Repair, Comprehension, and Maintenance
Kathryn Stolee

$499,996 by National Science Foundation (NSF)
08/15/2017 - 07/31/2021

Regular expressions (regexes) are responsible for numerous faults in many software products, and yet, static bug finders and automated program repair techniques generally ignore this common language feature. First, I propose to explore and characterize regex-related bugs in bug repositories. From there, I propose to develop approaches for detecting regex-related bugs using static analysis and patching regex-related bugs using automated program repair. The proposed detection and patching techniques both depend on similarity analysis of regexes. The expected research outcomes include a publicly available data set of regex-related faults, new regex-related bug patterns for static bug finders like FindBugs and PMD, and in the best case, an open source tool for automated patch generation for regular expressions.

SHF: Medium: Collaborative Research: Semi and Fully Automated Program Repair and Synthesis via Semantic Code Search
Kathryn Stolee

$387,661 by National Science Foundation
07/ 1/2016 - 06/30/2021

Software plays an integral role in our society. However, software bugs are common, routinely cause security breaches, and cost our economy billions of dollars annually. The software industry struggles to overcome this challenge: Software is so inherently complex, and mistakes so common, that new bugs are typically reported faster than developers can fix them. Recent research has demonstrated the potential of automated program repair techniques to address this challenge. However, these techniques often produce low-quality repairs that break existing functionality. In this research, we develop new techniques to fix bugs and implement new features automatically, producing high-quality code.

LAS DO1 Watson - 2.1 Human-Machine Collaboration
Ben Watson

$175,581 by Laboratory for Analytic Sciences
01/17/2019 - 12/31/2020

LAS DO1 Watson - 2.1 Human-Machine Collaboration

LAS DO1 Option Year Williams- 3.0 Machine Learning Integrity
Laurie Williams

$116,075 by Laboratory for Analytic Sciences
01/17/2019 - 12/31/2020

LAS DO1 Option Year Williams- 3.0 Machine Learning Integrity The goal of the projects examining Machine Learning Integrity is to identify and address issues that impact the timeliness, objectivity, reliability, explainability, and quality of machine learning approaches. These issues may arise from the specifics of an application, from a lack of data, from computational or policy constraints, or from adversarial actions.

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.

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.