San Diego State University Foundation

NSF Awards · FY 2024 · 2024-09

Recent years have seen a remarkable surge in interest and use of unmanned aerial systems (UAS), also known as drones, leading to rapid advancements in UAS technologies. Despite this progress, much remains to be explored to fully harness the potential of UAS. This underscores the critical demand for skilled researchers specializing in UAS. However, shaping the future UAS research workforce encounters multiple challenges, including system complexity, lack of open platforms, and shortage of training materials. To tackle these challenges, this project develops a new training program that will equip students with the essential skills needed for conducting and potentially transforming foundational UAS research. This project involves a team of educators with complementary expertise in various aspects of UAS. The training program includes four modules, each focusing on a fundamental aspect of UAS: control, communication and networking, computing, and artificial intelligence (AI) applications. This modular design ensures scalability and facilitates integration into existing curricula. Additionally, by leveraging an open UAS Cyber Infrastructure (UAS-CI) developed by the project team, each training module includes numerous hands-on projects to equip trainees with practical skills in operating and advancing UAS-CI. Implemented through a month-long summer program and tutorials at relevant international conferences, this project develops at least 50 skilled UAS professionals annually, who are expected to become the future UAS-CI research workforce and drive transformative advancements in foundational UAS research. 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 · 2024-09

This project addresses the issue of homelessness in a border region by developing advanced artificial intelligence (AI) and geospatial techniques to map and analyze the encampments of the unhoused. By integrating data from street view images, remote sensing imagery, and geographic information systems, the research identifies patterns of migration by unhoused populations over the past decade. The study focuses on understanding the socio-environmental factors that influence the distribution and migration patterns, with the ultimate goal of informing policy and interventions to improve community welfare and health. Additionally, this project creates the San Diego Homeless and Health EquAlity Research Team (SDHEART) consortium to promote collaboration among researchers, stakeholders, and the community through workshops, hackathons, and educational initiatives. This project aligns with NSF’s mission to promote the progress of science and advance national health, prosperity, and welfare by addressing a critical societal challenge and fostering STEM education and diversity. The interdisciplinary research project leverage deep learning models and geospatial artificial intelligence (GeoAI) to detect and analyze the spatiotemporal patterns of encampments of unhoused persons. By integrating multiple data sources, including street views, remote sensing imagery, and GIS databases, the project will employ innovative GeoAI and Big Data Fusion methods to study migration patterns. The research aims to understand the socio-environmental determinants and impacts of homelessness on local neighborhoods through mapping, surveys, and interviews. The project also establishes the SDHEART consortium to facilitate sustainable research and policy development by hosting data hackathons, workshops, webinars, and an exhibition. This initiative enhances the research capacity at SDSU, contribute to computational social science, and provide educational opportunities for students in geography, sociology, urban studies, and related fields. The project's outcomes will offer valuable insights into the dynamics of homelessness and serve as a model for similar research in other communities. 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 · 2024-09

Vortices are persistent circulating flow patterns that arise in diverse physical contexts, ranging from classical hydrodynamics and superfluids to condensed matter physics and nonlinear optics. They are ubiquitous physical phenomena in the world around us and can be observed at very different scales, from microscopic vortex lines in superfluid liquid helium, to dust devils and tornadoes, and even to Jupiter's Great Red Spot. Bose-Einstein condensates of ultracold atoms (BECs) provide a pristine and controllable environment where numerous aspects of the fascinating realm of nonlinear vortex dynamics can be explored not just in theory but also through direct experiments. In addition to their intrinsic fundamental interest, these systems also exhibit localized solutions with potential practical applications: for example, it has been suggested that solitary waves could be used for unprecedented, improved sensitivity in interferometric and force-sensing devices. On the other hand, vortical structures, which are the focus of this proposal, also hold promise for other intriguing applications. For instance, they can provide an instance of 'analogue gravity' as a proxy to study the behavior of spinning black holes. It has also been proposed that BEC vortices could collapse in a manner akin to supermassive black holes and that supersonic expansion in BECs can replicate properties of an expanding universe in laboratory settings. Through a bijective collaboration with experiments, this proposal aims to advance the current understanding of topological structures in BECs. Being based on universal models of modulated waves in nonlinear media, the underlying physical setting represents a fundamental playground to study topologically charged excitations that are, in turn, at the heart of an extremely wide variety of physical contexts in atomic, optical, wave physics, and beyond. The project will address the existence, stability, manipulation, and dynamics of vortex configurations in 2D and 3D settings from a novel and broad perspective. The PIs' plan is to develop effective lower dimensional, reduced evolution equations to gain novel insights on the properties of these coherent structures in the original, high-dimensional, models and to compare the theoretical results therefrom with numerical computations and circling all the way back to direct observations from atomic and polariton BEC experiments. The main goals of this proposal are multi-fold and include the following themes: the creation, removal, and interactions of vortices and soliton filaments and experimentally tailored external potentials by leveraging effective lower-dimensional dynamical models for the evolution of soliton filaments coupled with point-vortex models including the relevant case of open quantum systems in the presence of driving and damping for polariton condensates. Also, in close synergy with experimental collaborators, the study of the timely theme of synthetic magnetic monopoles and the elusive so-called Alice ring in spinor (chiefly F=2) BECs. The project aims to shed light on this highly complex, topological pattern forming system and, in particular, on the recent collaborator experiments where they observed that monopole instabilities give rise to topological patterns reminiscent of Alice rings. 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 · 2024-08

This project investigates changes in prehistoric human foraging, mobility patterns, and population dynamics in response to environmental change during the later Pleistocene. The research provides broader context to occupations in a coastal region by comparing the results of new archaeological data analyses to detailed models of the paleo-environment and human foraging patterns on the landscape. This project represents a further step towards understanding how foraging human populations use entire landscapes to extract resources and leave material traces of their behavior, even if only small portions of that material culture are ever recovered through excavation. Agent-based computer simulation modeling bridges the conceptual gap between a robust understanding of foraging decisions, from optimal foraging theory and human behavioral ecology, to the long-term accumulation of stone, plant, and animal remains in the archaeological record. Through a collaboration a multi-institutional collaboration the project expands a partnership with an institution that services mostly students from previously disadvantaged groups. Researchers plan to recruit students to join field teams to work at an archaeological site and to work as research assistants to learn faunal analysis of excavated assemblages. The project will also improve public engagement with science by helping to design an exhibit at a world cultural heritage site. 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 · 2024-08

This NSF/FDA Scholars-in-Residence project is focused on ventricular assist devices (VADs), which are heart pumps used to support patients with severe heart failure. Many patients die each day while waiting for a heart transplant, and VADs can be a life-saving alternative. However, evaluating their safety and effectiveness is complex due to the dynamic interaction with each patient’s unique heart condition, posing significant challenges for the United States Food and Drug Administration (FDA). The goal of this project is to create a new computational model to predict the complex interactions of the VAD with the beating native heart. This model will simulate real-life conditions by integrating data from clinical studies, benchtop experiments, and computer simulations, offering a more precise method for predicting VAD performance. This model will enable the FDA to make better-informed regulatory decisions and support the development of safer, more effective VADs tailored to individual patient needs. This tool will drive improvements in VAD technology, ultimately enhancing patient outcomes and reducing deaths among those waiting for transplants. As part of the project, a graduate course on medical devices will be updated to include the use of computational models in regulatory science, and an FDA internship for an engineering student will be offered. These activities ensure that future engineers and regulators are equipped to advance VAD technology. This research has the potential to transform heart failure treatment by improving VAD performance and tailoring their performance to individual patient needs. It could reduce the number of deaths among those waiting for a heart transplant and lead to better patient outcomes. This research will result in a lumped parameter model (LPM) for predicting dynamic pressure and flow for a ventricular assist device (VAD) when coupled with the native human heart and circulation. The LPM will be a digital twin of an experimental mock circulatory loop (MCL) used for testing VAD safety and efficacy. A system of differential equations derived from an electrical circuit representation of the MCL are solved for a range of conditions representing heart failure patients and combined with the VAD performance curves to generate dynamic pressure and flow waveforms. These waveforms can be used to identify safety concerns such as pressure spikes and turbulent flow early in the design or review process. Verification, validation and uncertainty quantification of the LPM will be performed following FDA guidelines and published standards in preparation for use in VAD regulatory review and design. The LPM resulting from this project will aid industry and FDA via the science-based and transparent results and contribute to the integration of computational models into the regulatory review process. 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 · 2024-08

This project aims to understand complex physical processes like pollution transport, virus spread, and wildfire evolution by integrating physics and artificial intelligence. These movements of interest within each of these processes are influenced by uncertain background flow velocities; that is, how fast the pollution or virus moves, making identification of the source of the problem challenging. The proposed research combines equations from physics that govern physical movement with generative machine learning, specifically focusing on stable diffusion models. By incorporating uncertain flow velocity information, we aim to enable more accurate source identification from limited observations. This innovative approach promises to enhance our ability to manage environmental and societal disasters, leading to improved pollution control, risk assessment, and disaster response strategies. The project develops physics-constrained generative stable diffusion models to reverse advection-diffusion processes. It addresses uncertainties in background flow velocities by developing a stable-diffusion formulation to gradually remove the stochasticity in the backward process, and adopt appropriate diffusivity learned through the training data. This approach integrates physical governing equations as guidance, allowing for reliable modeling that can be conditioned on the limited information of background flow fields. The research aims to quantify how these uncertainties affect source identification accuracy, providing a transformative solution in environmental monitoring. By retrospectively interpreting observations, we aim to unravel causal relationships leading to current states. The project also aims to advance interdisciplinary education and support diverse student participation in engineering and environmental science research. 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 is funded by Pathways to Enable Open-Source Ecosystems (POSE) Program which seeks to harness the power of open-source development for the creation of new technology solutions to problems of national and societal importance. In today’s rapidly evolving world, it is important to understand location data as related to complex societal issues. From urban planning and environmental management to public health and economic development, spatial data science stands at the forefront of decision-making processes, enabling the visualization and analyzis of data in ways that reveal relationships, patterns, and trends across various geographies. This project, spearheaded by an interdisciplinary team at the San Diego State University in collaboration with the University of Chicago, the University of Maryland, and the University of North Texas, aims to improve the field of spatial data science through the development and enhancement of the Python Spatial Analysis Library (PySAL) open-source ecosystem. With a focus on expanding accessibility, functionality, and collaborative potential, the initiative is poised to democratize spatial data analysis, making powerful tools available to researchers, policymakers, and the public. The project's dedication to open-source principles fosters innovation and ensures that advancements in spatial data science are shared freely, promoting transparency and inclusivity in research and application. The technical core of this project revolves around the strategic expansion of PySAL and the cultivation of a supportive ecosystem that bridges the gap between scientific inquiry and practical application. By integrating advancements in spatial analysis with the latest computational techniques, the project aims to refine and extend PySAL’s capabilities to meet the growing demands of diverse data-intensive environments. The initiative seeks to build a robust network of users, developers, and educators through participatory learning and targeted outreach. This multi-faceted approach includes enhancing educational resources to train the next generation of spatial data scientists, fostering cross-domain collaborations, and developing user-friendly tools that cater to the specific needs of industry and government sectors. Through these efforts, the project aspires to advance the science of spatial analysis and equip stakeholders with the means to address real-world challenges more effectively and equitably. 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 is an ExpandAI Partnership between the San Diego State University (SDSU) and The Institute for Learning-enabled Optimization at Scale (TILOS). In this project, a minority-serving institution leads a new collaboration with an AI Institute focused on scaling up already-established research and education programs at SDSU and to pursue shared, complementary goals to develop safety-conscious scalable AI and to develop the next generation of AI education and workforce talent. The collaboration focuses on providing cutting-edge AI research and education to the diverse community of innovators and future leaders in the San Diego region through sustainable collaborations in the AI Institutes ecosystem that also leverage and expand AI initiatives at SDSU. The resulting research collaborations will engage faculty and students at SDSU with those in a wide range of TILOS partner institutions, including the University of California San Diego, the Massachusetts Institute of Technology, Yale University, the University of Pennsylvania, and the University of Texas at UT Austin. Through a range of research and education initiatives, the project will build community and new centers of excellence in AI between these institutions, involving outreach to new minority serving organizations and communities. This mutually beneficial partnership in research, education/workforce development, and infrastructure will be centered on investigation of AI techniques to confront the fundamental research challenges in the optimization of autonomous systems, such as robotic systems and intelligent edge networking devices, especially in the presence of uncertainty. The research encompasses both theoretical foundations of AI in learning and optimization and their applications to autonomous systems, building upon and strengthening the research pillars already established under the TILOS AI Institute. Collaborative research in trustworthy AI decision making under uncertainty in the domain of autonomy will addressing reliability challenges for autonomy under uncertainty. In another thrust, AI-driven optimization for distributed autonomy on the edge will achieve scale and tackle the practical AI deployment challenges that exist at the edge of distributed computing systems, including distributed optimization over communication graphs, motion planning, reinforcement learning (RL), and stochastic games. These efforts will directly inform a comprehensive range of collaborative education and workforce development activities, ensuring accessibility and availability of AI, optimization, robotics, and networking education to students from diverse backgrounds. Project goals include the enhancement of underrepresented minority participation in AI education and research while fostering a diverse talent pipeline that encompasses paths to both industry and graduate programs through expansion of AI course offerings with tailored materials for diverse students, programs that enhance student engagement, new undergraduate summer internship programs and graduate research symposiums, and training programs for faculty. The project is partially funded by the Directorate for STEM Education (EDU), under the Louis Stokes Alliances for Minority Participation (LSAMP) program and the IUSE: Hispanic Serving Institution program. 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

Mathematical modeling can be used to understand and predict natural phenomena in science and areas that broadly impact human society, including medicine, geophysics, finance, economics, weather forecasting, reorganization of food production chains, transport of contaminants in oceans and soils, and propagation of diseases. Mathematical tools that can efficiently solve the partial differential equations (PDEs) that describe those phenomena enable predictions through modeling. The Mimetic Operators Library Enhanced (MOLE) is a software repository that makes it easier and faster to solve mathematical models specifically using mimetic differences. Using mimetic difference methods is favorable, as they are highly accurate, fast, easy to implement and replicate within their appropriate realms, and conserve important properties. MOLE is currently being successfully used by a few research groups around the globe. The goal of this effort is to create a detailed plan for transitioning MOLE to a self-sustaining organization so that it can be maintained and grow through ongoing collaborative development of an open-source product, designed to be publicly accessible, modifiable, and distributable by anyone under an open-source licensing model. Currently, MOLE provides MATLAB/OCTAVE and C++ versions and a basic user guide in its repository. Code contributors must adhere to a set of best practices for scientific software development that not only have solid foundations in research and experience and also improve developers' productivity and the reliability of their software. The team plans to create and support a vibrant community of developers and users, at universities, national labs, and industry. By teaching courses and participating in conferences to promote and increase adoption and collaboration and reach out to current MOLE users and potential adopters, the Open-Source Ecosystem (OSE) requirements will be developed. The project will transition MOLE's existing code base into a robust, distributed, well-documented, community-driven, and secure open-source software environment, and build infrastructure and governance for efficient software development and maintenance. In is anticipated that MOLE will be implemented in compiled and scripting programming languages to support a variety of different environments and communities. 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 persistent underrepresentation of Black, Hispanic, and female workers in the engineering workforce threatens the United States' global economic and technological competitiveness. Many factors contribute to this problem, including educational and socioeconomic equity gaps that fail to support aspiring but underprepared students entering college. Statistical analysis has identified engineering majors whose first mathematics course is college algebra at greatest risk of non-retention and seeks to understand and mitigate the challenges faced in foundational math courses. By conducting qualitative research through focus groups with first-year engineering students, the project aims to uncover the social and cultural factors influencing their experiences and attitudes towards continuation in the major. The data collected will inform the design of interventions aimed at improving early mathematics success for underrepresented engineering and STEM students, complementing existing academic support initiatives. This research not only benefits underrepresented students at SDSU but also contributes to broader efforts in STEM education nationwide and aligns with the NSF Broadening Participation in Engineering goals to foster a more diverse and inclusive engineering workforce essential for innovation in the global economy. The project aims to investigate and address the challenges encountered by students from traditionally underrepresented backgrounds including Black, Hispanic, female and indigenous peoples in the engineering workforce, focusing on foundational mathematics courses, particularly college algebra, at San Diego State University (SDSU). Research questions will probe into the factors influencing the success and retention of underrepresented students including Black, Hispanic, female and Pell grant recipients in engineering majors, emphasizing academic, professional, and social strategies to bolster their performance. Utilizing qualitative data collection methods such as surveys and focus groups, the project will delve into the experiences and attitudes of students towards college algebra, thereby informing evidence-based interventions geared towards improving retention rates. Longitudinal data analysis and predictive modeling will deepen insights and facilitate the customization of interventions for diverse student cohorts. Through collaborative partnerships with SDSU, the California State University (CSU) system, and engagement with the NSF INCLUDES Coordination Hub, the project aims to disseminate findings, foster collaboration, and contribute to a more diverse and inclusive engineering workforce by enhancing the retention and graduation rates of underrepresented students in engineering and STEM disciplines. An interdisciplinary team, comprising faculty from mathematics, engineering, sociology, and student success domains, will collaborate to design, implement, and assess these interventions. The outcomes will result in tailored programs and strategies to meet the needs of underrepresented students, ultimately aiming to promote diversity and inclusivity in engineering education and cultivate a more representative engineering workforce. Furthermore, the research findings are anticipated to enrich broader conversations and initiatives in STEM education and diversity efforts nationally, advancing understanding and practices for supporting underrepresented minority students in engineering and other STEM fields. 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.