Arizona State University

NSF Awards · FY 2024 · 2024-07

Recent cyber-attacks that exploit multiple vulnerabilities plague even the most protected companies. This has led to the solutions that ubiquitously monitor system activities as a series of system events, and apply causality analysis to reveal the attack steps through reconstructing the events and their dependencies on the attack as dependency graphs. Nevertheless, existing techniques mainly exploit event time to identify dependencies. This will include many less-important dependencies brought by irrelevant system activities. Moreover, these techniques cannot easily incorporate expert knowledge from security analysts due to limited extensibility, and provide little support to engage security analysts to actively explore the dependencies. The project is expected to make a major positive impact on system security by enhancing attack investigation using system audit logs, and provide contextual information to help intrusion detection systems better prioritize alerts. The project actively involves students from minority and underrepresented groups for research and training experiences. The goal of this proposal is to develop a general query framework to express and extract contextual attack information, by constructing small graphs of attack-relevant events from system audit logs. The project is focused on the following research tasks: (1) build a general infrastructure that computes discriminative weights for dependencies based on various properties of system events to identify attack-relevant events and entry points for attacks; (2) develop a declarative graph query language that provides specialized language constructs to express various formats of causality analysis; (3) devise a scalable interrogative analysis framework that can automatically clarify causality analysis by tracking expressive causal structures for both verified and hypothetical scenarios, enabled by new ``Why'' and ``What-if'' semantics. This project will advance the state of the art in revealing attack provenance from complex systems, on engaging security analysts to interactive and explainable security analytical pipelines, and to gain better understanding of the fundamental and practical challenges for building an effective attack investigation system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This project aims to serve the national interest by significantly enhancing the spatial communication abilities of students on virtual field trips. Field trips are one of the most common methods used to deliver real life situated learning experiences to students. They represent a form of active learning, enriching traditional lessons by better engaging students and strengthening spatial, verbal, and math skills for many students. However, field trips also present major logistical, financial, and accessibility challenges for many educational institutions. In this IUSE level 3 project, Arizona State University in collaboration with the University of Missouri plans to engage in a 54-month project, the goal of which is to investigate the use of virtual field trips to improve student learning through a virtual field trip platform. The approach leverages web-based digital environments to deliver multi-user, synchronous, situated learning experiences offering students in-depth spatial communication practice. The project features a transformative change in the ways that spatial communication learning is conducted in STEM, democratizing it by making field trip experiences fully accessible anywhere, anytime. Broad dissemination of findings is assured through extensive faculty professional development and the implementation of iVisit in courses, workshops, webinars, and outreach. Intentional efforts will be made to reach low-income and students from groups underrepresented in STEM using iVisit, further improving STEM inclusion. Research questions posited to guide the investigations include (1) What learning affordances in virtual field trips foster active spatial communication and student group engagement? and (2) How do virtual field trips improve student group spatial communication learning? A design-based research approach is to be employed to obtain answers since this offers the opportunity to improve educational practice through iterative analysis, design, and implementation. Researchers and practitioners will collaborate in real-world settings to create and test design principles and solutions for educational core areas in the current curriculum of Construction Management programs in the United States. The iVisit field trip contents and technology will be informed by the conceptual frameworks for situated learning, including Problem-Based Learning (PBL), Computer-Supported Collaborative Learning (CSCL), and spatial communication in construction. Focus group interviews for instructors recruited to provide the field trip experience for their courses, will be recorded, transcribed, and analyzed via thematic analysis. By leveraging the results from the focus groups, the field trip contents and the PBL Student Activities will be created. In order to support the created contents and activities, multiple media representations of spatial data will be embedded into iVisit to digitally embody the required knowledge for learning spatial communication. These field trip contents and PBL student activities will then be used for the iVisit platform development. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project is an ExpandAI Partnership between Arizona State University (ASU) and the AI Institute for Foundations in Machine Learning (IFML). In this project, ASU (a Hispanic-Serving Institution) leads a new collaboration with an AI Institute to pursue shared, complementary goals to unlock untapped talent at ASU for artificial intelligence (AI) education and use-inspired research. The collaboration focuses on joint research between researchers at ASU and IFML on projects that address fundamental challenges of robust/interactive/embedded machine learning in pervasive systems. Pervasive systems are those that integrate computational capability into objects such as wearable technology, mobile devices, and assistive robots as well as built environments such as homes, cars, and workspaces. These technologies are poised to have broader impact in health and wellness by addressing the challenges associated with automation of cost-effective, objective, continuous, and real-time monitoring, intervention, and decision making in pervasive systems in areas such as health monitoring, health assessment, outcome prediction, and intervention automation in. The project also promises broader impacts in AI education for demographics underserved in this area (including underrepresented minorities and women) by integrating the research activities into new interdisciplinary courses. Broader educational outreach involves graduate, undergraduate, and high school students. This mutually beneficial partnership in research, education/workforce development, and infrastructure will be centered on addressing challenges in deploying AI-enabled pervasive systems in real-world settings. Because these systems are deployed in highly dynamic environments and in direct interaction with humans, the project will (i) design robust machine learning algorithms that address distribution shifts in the data due to dynamic changes in the system status over time; (ii) design interactive machine learning techniques that incorporate human input and prior domain knowledge for improved model performance and personalized decision making; and (iii) develop embedded machine learning methods for deploying the models on embedded devices with stringent constrained resources. Leveraging the existing AI capacity at ASU and prior research of the collaborators, this partnership between ASU and AI Institute for Foundations of Machine Learning (IFML) also increase participation in multidisciplinary research, forging new interdisciplinary collaborative opportunities with the newly founded ASU School of Medicine and Advanced Medical Engineering. The collaboration also features educational programs, research oriented interdisciplinary course development, ExpandAI workshops, and the development of new courses, certificates, and course modules in pervasive AI systems to increase access to AI education and career pathways for minority students. The project will leverage these research and education efforts for impact at the secondary school level, delivering instructional materials for use by high school teachers and students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-05

The design of aerospace propulsion systems is significantly streamlined by employing computational fluid dynamics. Leveraged by high-performance computing, numerical simulation helps reduce development cost and explore the design parameter space, contributing to finding an optimal design and eventually lowering fuel consumption and carbon footprints on our environment. Modeling turbulent flows is a key to achieving dramatic reduction in computational cost. However, such task is challenged by the physio-chemical complexities of turbulent reacting flows, in which conventional turbulence modeling approaches are usually limited. This study proposes to address the modeling challenges by discovering turbulence models for reacting flows in a way that is physically consistent and data driven. The developed modeling framework is expected to benefit other applications in science and engineering where interactions between turbulence and multiphysics play central roles, such as particle suspension, atmospheric science, high-speed aerodynamics, and nuclear fusion. As a part of educational activities, video materials on technical formulations and outcomes will be developed and shared publicly on online platforms for training current and future workforces. An outreach project on fluid mechanics is planned targeting local K-6 or under to promote interests in fluid mechanics and engage undergraduate students. A modeling framework is proposed for the large-eddy simulation of turbulent premixed flame to discover closure terms that describe combustion-induced energy backscatter. Despite the compelling evidence of energy backscatter in turbulent combustion, scientific questions remain as to how such effects should be modeled in subgrid-scale stresses. The traditional eddy-viscosity concept is limited in describing the true two-way interactions between turbulence and premixed flame. A modeling framework based on wavelet multiresolution analysis will be developed so that subgrid-scale models that optimally describe spectral energy transfer are discovered from high-fidelity numerical database. The optimization formulation is consistent to the fundamental concept of large-eddy simulation. A tensor representation theory will be utilized to expand analytically the true subgrid-scale stresses in terms of thermo-physio-chemical states into a complete and minimal form. For statistically one-dimensional turbulent premixed flames, the modeling framework will be tested for different regimes of turbulent premixed combustion. The proposed study is expected to provide a first-of-its-kind discovery of subgrid-scale models that allow energy backscatter in turbulent premixed flame, accelerating the advent of accurate and efficient prediction-based design of aerospace propulsion system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2023 · 2023-10

Engineering the Elimination of End-of-life Plastics (E3P) requires technological advances to maximize recycling and recovery, behavioral understanding to influence consumer attitudes, and economic approaches to incentivize extension of product life. Each alternative involves trade-offs in its social acceptability, economic feasibility, environmental sustainability, and circularity. For example, biodegradable plastics may seem to be most desirable if they decompose to become biological nutrients. However, if these materials have a large life cycle environmental impact, their adoption will not eliminate the end-of-life, but simply shift the environmental burden along the life cycle. Solutions for E3P need to be sustainable by being environmentally benign, economically feasible, and socially desirable. The overall goal of this project is to develop holistic and systematic methods and tools for assessment, design, and innovation toward Sustainable and Circular E3P (SCE3P). The research team will conduct synergistic research in polymer chemistry, reaction engineering, and molecular simulation to determine properties of depolymerization and valorization processes under practical conditions of contamination; process design to model the cost and physical flows of current and emerging technologies; supply network modeling to determine the effects on the wider chemical industry; behavioral studies to discern and influence the role of consumers; and life cycle and circularity assessment to estimate environmental effects across global value chains. The resulting framework will consider the entire plastics life cycle, including thousands of combinations of alternatives at each step to select the "best" pathway. This framework will be able to assess existing products, design new products and pathways, and encourage innovation toward SCE3P. The framework will be useful for all types of plastics, but the project's experimental focus will be on polystyrene (PS) and poly(ethylene terepthalate) (PET) due to their large market. The SCE3P framework will be applied in the project to plastic products in the food service industry, with case studies done in collaboration with industry consortia and other stakeholders. The project is formulated to contribute to the convergence of chemical engineering, sustainable engineering, and behavioral science, for assessment, design, and innovation toward a sustainable and circular economy of plastics. A target is to develop new knowledge about the chemistry and engineering of various depolymerization and valorization approaches for PS and PET products. The research team will also bring together knowledge about steps in the plastics life cycle to contribute to an innovation roadmap for SCE3P. A spatial model of the U.S. chemical industry will be extended by including the plastics industry and emerging technologies for SCE3P. Behavioral studies will improve the understanding of spillover effects of other decisions on choice of plastic products and their responsible disposal. New data and methods will be developed for assessing and designing circular systems, evaluating their resilience, and identifying hotspots to focus innovation. Application to food services will guide progress toward goals of zero waste and carbon neutrality. The outcome of this project is to be a software prototype of the SCE3P framework, which will be disseminated widely via a university-based website, webinars to industry and other stakeholders, and university courses. Collaborators will provide access to over a hundred companies across the world. The team will develop teaching modules related to the research for inclusion in university courses and high school engineering curricula through the Engineer Your World program which reaches over 10,000 diverse high school students across the U.S. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.