University of Pittsburgh

NSF Awards · FY 2025 · 2025-08

The ocean plays a vital role in regulating Earth’s temperature and weather systems by transporting heat and nutrients throughout its depths. While winds and waves are known to stir the ocean, new research suggests that tiny sea creatures may also play a significant role, collectively generating enough energy to drive substantial mixing. When swimming together in large groups, they can create swirls that are much larger than their individual sizes, potentially strong enough to influence the movement of nutrients up and down in the ocean. This project investigates how swarms of tiny marine animals generate significant water movement, contributing to ocean mixing. By uncovering the mechanisms behind their collective motion, the study aims to improve predictions of ocean behavior and enhance our understanding of marine dynamics. The outcome of the proposed research could potentially benefit engineering applications such as optimizing airflow around drone swarms and improving bubble-induced air-water mixing in wastewater treatment. The project offers training for students and engages the public through accessible educational videos and hands-on workshops. This award will investigate how the collective active migration of small marine organisms can generate large-scale flow structures, referred to as aggregation-scale flows. These flows may contribute meaningfully to vertical mixing in the ocean. The objective of the proposed research is to employ well-controlled laboratory experiments to gain a deeper understanding of aggregation scale flow generation during collective active migrations of particles, an inherently many-body and multi-scale fluid-structure interaction problem. The research is focused on three aims: (1) Identifying the limiting individual and group properties required for generating aggregation scale flows; (2) Understanding the physical mechanisms behind these aggregation scale flows using spatially resolved spectral energy flux analysis; and (3) Directly measuring mixing efficiency using a specialized vertical migration tank designed to mimic relevant ocean stratification scales. This research will test theoretical predictions of mixing efficiency and advance our understanding of collective biological-fluid interactions and their broader effects on ocean mixing and coastal dynamics. Ultimately, the findings will inform the development of improved long-term ocean forecasting models and have broader applications in natural and engineering systems with many-body, fluid-structure interactions. 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 2025 · 2025-08

This project seeks a travel grant to support student attendance at the 2025 Embedded Systems Week (ESWEEK) in Taipei, Taiwan. ESWEEK is a premier international event that unites leading conferences in embedded systems and software, covering critical advancements in compilers, architectures, hardware/software codesign, and embedded software development. Supporting student participation in ESWEEK directly serves the national interest by fostering the next generation of innovators in a field crucial for advancing technology, prosperity, and welfare. Increased student attendance will enrich the conference and help attract students from all background to embedded systems and STEM research. Students will gain unique opportunities to interact with global experts and industry leaders, discuss their research, and establish connections vital for their professional careers. This initiative aims to address financial barriers that often prevent students from attending, thereby ensuring a more skilled workforce for the future of embedded systems. The project's goal is to provide travel support for approximately 30 eligible students to attend ESWEEK 2025. Eligibility is restricted to undergraduate or graduate students currently enrolled at a U.S. university, including U.S. citizens traveling from other countries. Priority for funding will be given to students presenting their research work at ESWEEK, its symposia, and workshops, with a special preference for U.S. citizens and permanent residents. The grant will cover a portion of reimbursable expenses, including airfare (on U.S.-flag carriers or ticketed through U.S. carriers), ground transportation, lodging, registration fees, and meals. Outreach for travel support will be widely advertised through the ESWEEK website, mailing lists, and technical society newsletters. Student applications will be collected via email, requiring a resume, a cover letter detailing reasons for attending and estimated expenses, and an advisor's letter supporting the application and stating other funding availability. A committee will select awardees based on eligibility, participation level, research alignment, advisor recommendation, and prioritizing U.S. students. After the event, each awarded student must submit a one-page report on their impressions and outcomes. 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 2025 · 2025-08

This research project aims to develop new mathematical methods and techniques to analyze some nonlinear partial differential equations (PDEs) that govern fluid flows and related phenomena. Fluid flows such as gases are important in nature. Their study is crucial for understanding the dynamics in a wide range of scientific and engineering applications, including gas dynamics, material science, geometry, turbulence, and shell theory. While one-dimensional problems in this field are relatively well understood, the theory for multi-dimensional cases remains mathematically underdeveloped. This project seeks to advance the mathematical understanding of multi-dimensional conservation laws and their applications in both fluid dynamics and geometry, and will integrate research and education, therefore also contributing to the development of the future STEM workforce. The research will focus on four core problems: (1) the existence and stability of the transonic contact discontinuity in the three-dimensional axisymmetric nozzle in gas dynamics: this is a free boundary and mixed-type problem, the free boundary is characteristic, and this study will shed light and provide new methods on the general multi-dimensional theory of conservation laws; (2) the existence of a global solution to the transonic flow past a three-dimensional axisymmetric cone in gas dynamics: new ideas and techniques will be developed to solve this mixed-type PDE problem; and (3) the global smooth solution to the Gauss-Codazzi equations of isometric immersion of surfaces: a global smooth solution to the underlying hyperbolic system of balance laws yields a smooth isometric immersion of surfaces, and a longstanding open problem is to find such a global smooth solution when the curvature of the surface has the optimal decay rate and oscillations. By developing novel analytic methods for these important problems, the project will deepen understanding of multi-dimensional PDEs in fluid dynamics and geometry. It will advance knowledge in fundamental areas of mathematics and mechanics while also providing valuable training opportunities for students in applied mathematics. 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 2025 · 2025-07

This project explores fundamental problems in Geometric Function Theory, focusing on mappings and functions with limited differentiability, such as convex functions, Sobolev functions, and Lipschitz and Hölder continuous mappings. Geometric Function Theory has its roots in classical complex analysis and quasiconformal mappings, but over the past decades it has evolved into a rich and modern field with deep connections to other areas, including convex analysis, analysis on metric spaces and Heisenberg groups, contact and symplectic geometry, and geometric measure theory. This broadening has been driven by the need to understand nonlinear phenomena and low-regularity structures in both pure mathematics and applied sciences. These types of maps are essential in modeling irregular behavior, where classical smooth tools fail. By studying their analytic, geometric, and topological behavior, the project seeks to uncover new mathematical principles that improve our understanding of irregular structures. The research is structured around 21 well-defined objectives, most of which are formulated as precise mathematical conjectures with definitive yes-or-no answers. These investigations aim to generate new directions in geometric analysis and topology while contributing to the broader mathematical community. The research is expected to support the national interest by advancing mathematical knowledge, training students, and providing tools applicable to areas that depend on the analysis of non-smooth structures. The investigator studies several interconnected areas of research. These include: (1) Lusin approximation and rectifiability questions in convex analysis; (2) regularity of homeomorphisms in Euclidean spaces, including the study of the sign of the Jacobian; (3) Guth's conjecture about the homotopy theory of continuously differentiable maps whose derivatives have low rank; (4) Gromov's conjecture about Hölder continuous embeddings into the Heisenberg group and related questions about the topology of Lipschitz and Hölder continuous maps in the Heisenberg group; (5) Sobolev extension domains; (6) analysis on metric spaces and the geometric measure theory of Lipschitz mappings into metric spaces. The project combines analytic, geometric, and topological methods to address both longstanding open problems and newly formulated questions. The anticipated outcomes include theoretical advances, publication of results in leading journals, and training of graduate students in cutting-edge mathematics. 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 2025 · 2025-07

This collaboration between researchers at the University of Michigan and the University of Pittsburgh will study the mechanisms of animal migration termination using a novel logger and analytics platform (mSAIL). Migratory species such as monarch butterflies are uniquely threatened, and understanding how and why they choose different habitats will be important for helping us mediate threats. This research will provide novel insight into how social and environmental information are integrated to guide decisions and shape migratory ecosystems. Broader impacts of this work include raising scientific literacy through public engagement and broad cross-disciplinary training opportunities. Community volunteers directly contribute data that enable machine learning algorithm development for mSAIL. This work will provide cross-disciplinary training opportunities for multiple student participants in biology and engineering. The project contributes to the bioeconomy and to biotechnology through the development and honing of a data logger small enough to be carried by an insect that will be of interest to other scientists and engineers outside of this project. Migratory animals often terminate their migrations in specific places. How and why specific wintering/estivating habitats are chosen is not well understood yet is important to know given the unique threat that migratory species face. This research will use mSAIL, a multi-modal integrated biologger and analytics platform, to monitor monarch butterflies as they terminate their iconic annual migrations at their overwintering sites. The recently developed mSAIL technology will be modified to capture multi-modal environmental data with higher spatial and temporal resolution, allowing behavioral inference. This work will contribute to our broader understanding of how and why migratory species are distributed as they are and how different sources of information (environment and social cues) are integrated to determine these patterns. 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 2025 · 2025-06

There is a growing demand for technologies like semi-autonomous unmanned craft (drones) to augment human capabilities for infrastructure inspection, disaster management, and search and rescue operations. To realize their potential, such technologies must integrate seamlessly into teams of multiple people interacting with multiple drones. Effective collaboration between people and semi-autonomous drones requires team members to be aware of surroundings, the placement and activity of other team members, anticipating what may happen next, assessing potential risks and hazards. In short, team members must be able to accurately perceive the situation in order to make informed decision about actions --- so-called situational awareness. This project aims to understand team situational awareness and develop computer-implemented tools that support real-time team situational awareness for tasks that involve both people and semi-autonomous drones. By fostering a comprehensive understanding of team dynamics, the project will significantly benefit practitioners by enhancing team collaboration and coordination, thereby improving efficiency and safety. The project will address education and workforce development by collaborating with government, industry, and community stakeholders to create new course modules, K-12 workshops, public safety training programs, and initiatives promoting broad participation, This project will develop an integrated framework to understand, predict, and support team situational awareness in multi-human-multi-agent teams operating in uncertain and dynamic environments. The project has three thrusts. Thrust 1 focuses on identifying optimal information for operators in specialized roles across diverse team structures and environmental settings. Participatory design and simulation studies will determine what information to display and how to present it, offering insights into how information types and team structures influence team situational awareness. Thrust 2 will establish novel metrics and computational models for team situational awareness. It will integrate cognitive theory and machine learning models, incorporate neural response data, and employ uncertainty-guided training to improve performance. Thrust 3 will design adaptive interfaces to guide operators to critical but overlooked information for individual situational awareness and visualize real-time shared situational awareness. These interfaces will improve situational awareness, enhance team collaboration and effectiveness, and help realize the promise of semi-autonomous agent technologies. Comprehensive evaluations in simulator and field studies will assess interface usability, team situational awareness, and performance impacts. 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 2025 · 2025-06

This grant provides support for participant costs to develop two new initiatives during the 2025 American Society of Biomechanics Annual Meeting to be held in Pittsburgh, Pennsylvania, 13-16 August 2025. 1) Develop a travel award called the “Biomechanics Career Kickstarter Award.” This award will prioritize first-time attendees and undergraduate students. 2) Speakers will be recruited to provide pre-conference webinars and workshops. Students who are typically unable to attend professional conferences will primarily benefit. The scientific meeting will contain biomechanics programming including keynote talks, oral presentations, and poster presentations. The meeting will also include activities to enable informal scientific discussions. The expected outcomes for the awardees will be career development through stronger scientific and networking skills. This meeting will enable workforce development that serves the biomechanics and health research community. The new “Biomechanics Career Kickstarter Award” will be awarded to 10 applicants so that they can attend the 2025 American Society of Biomechanics Annual Meeting. The conference highlights and disseminates research across engineering biomechanics and mechanobiology, about the development and evaluation of rehabilitation engineering technologies, and research that integrates across intent, motor output, and technology. An advertising strategy will reach potential applicants beyond the society’s current community. Sites with “research experience for undergraduate” programs and regional meetings associated with the American Society of Biomechanics will expand the applicant pool beyond regular attendees. Programming (mentorship, webinars, and workshops) will prepare awardees for attending a scientific conference and ease their path into the society community. A workshop will focus on networking skills and pre-conference webinars will enable awardees to plan for the meeting. Evaluations will measure the impact of programming on attendees and on the awards on awardees. Dissemination will include a scientific abstract reporting the impacts of our evaluations. This meeting will provide awardees with a first step towards a career in biomechanics and enrich our understanding on how travel awards and professional development programming influences students’ ability to benefit from scientific meetings. 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 2025 · 2025-06

This integrated research-education program will engage 18 high school and middle school science teachers over 3 years in authentic microbiology research to understand how bacteria adaptively evolve when producing biofilm, and how biofilm helps bacteria to resist predation by amoebae. This research training will prepare the teachers to lead these experiments in their classrooms, where students will observe and study bacteria evolving in biofilms over a week using our innovative curriculum. Both research and teaching will be supported by mentors in our laboratory and in their classrooms. We will measure whether our program improves teacher confidence in using life science experiments in their classrooms, as well as student attitudes and self-efficacy towards science topics relevant to future careers in biotechnology. This program will empower greater understanding by teachers and students of how microbes evolve, a crucial topic today. The dynamics of microbial biofilm adaptation, particularly its rapid pace and extent of diversification, present both a scientific puzzle and an educational opportunity. Through training the teachers, our program enables their students (age 12+) to observe evolutionary adaptation in just one week using a safe non-pathogenic Pseudomonas model. Through genome sequencing, we can identify mutations causing distinct colony morphologies. Student and teacher research revealed that biofilm adaptations protect against predation by Dictyostelium discoideum, uncovering an important but overlooked survival benefit. Building on 20 years of biofilm evolution research, our program creates a community where teachers are mentored by researchers with varied skills and by BIORETS alumni. Teachers integrate their research questions into an established curriculum that has engaged over 5,000 students across 19 states. With support from our research team, district coordinators, and community partners, teachers develop confidence in teaching experimental methods and data analysis. This empowers their students to overcome misconceptions in evolution and genetics through active learning, and provides authentic laboratory experiences for today’s careers in biotechnology. 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 2025 · 2025-06

This REU Site award to the University of Pittsburgh, located in Pittsburgh, PA, will support the training of 10 students for 10 weeks during the Summers of 2025-2027. The program, entitled Training and Experimentation in Computational Biology (TECBio), will immerse students in a mentored research project in interdisciplinary areas of computational, quantitative, and systems biology. Students will gain independence and confidence as researchers working on real-world research projects, with some students having the opportunity to do research with a local biotechnology company. Student contributions will advance the ongoing research of faculty at the host institution and help drive innovation at the forefront of discovery. Students will also learn the essential skills needed to pursue additional training and careers in these emerging fields in both the academic and biotechnology sectors. Students will present their results at a regional symposium and will be encouraged to also present at other scientific conferences. Program assessment will be done using a Qualtrics version of the Undergraduate Research Student Self-Assessment (URSSA) Survey. Students should apply to the REU site using the NSF ETAP (Education and Training Application: https://etap.nsf.gov). The TECBio REU has the scientific theme of “Multiscale Modeling, Simulation, and Systems-level Analyses for Biological Discovery” and leverages the expertise of the host institution’s Computational and Systems Biology Department as well as faculty from the Biological Sciences, Cell Biology, Chemistry, Mathematics, and Structural Biology Departments, among others. TECBio trains students in current state-of-the-art methodologies that prepare them to address rapidly evolving challenges in computational and systems biology by immersing them in mentored, independent research projects in our interdisciplinary scientific focus areas. Example student projects include investigating the dynamics of biomolecular systems using molecular dynamics simulations, using generative modeling for pharmacophore elucidation, modeling cellular phenotype transitions, understanding molecular signals controlling cell fate decisions, and decoding the spatial language of developmental signaling. The research experience will be complemented by a curriculum of professional development and enrichment activities that include training in the responsible conduct of research, a journal club, a tailored research and career seminar series, graduate school, internship, and job prep, training in scientific communication, and opportunities to serve as a mentor and role model for other developing scientists. Formative and summative assessments of the program and student gains will be performed using a modified version of the URSSA developed by the program. Additionally, interviews with participants (students and mentors) will be conducted during and after the 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 2025 · 2025-06

This project constructs a new dataset from county land-use records. The dataset includes information on land records from 20 counties spread across the United States over 40 years. It includes data on the Federal Housing Administration (FHA) insured and Veteran’s Administration (VA) guaranteed mortgages in these counties. The data will be publicly available to academics, decision makers, community organizations, and individuals who want to evaluate the impact of these two important federal programs. The team is also providing an initial analysis of these data to describe and analyze the spatial and demographic patterns of federal mortgage insurance. This analysis leverages individual-level borrower data and address-level property information to examine how these programs affected homeownership, wealth, and neighborhood outcomes in counties with different characteristics. The research findings have the potential to improve the functioning of mortgage markets, thereby enhancing the well-being of U.S. households. The award is jointly funded by the NSF programs in Economics, Sociology, and Human-Environment and Geographical Sciences (HEGS). The project creates a new data resource that provides the most extensive and granular data on the demographic and spatial distributions of FHA-insured and VA-guaranteed mortgages to date. Because the data include information on issued mortgages, the data allow scientists to consider the results of enacted policy rather than simply examining government agency reports and correspondence. The team is collecting and geocoding data on roughly 280,000 mortgages. They are using the data and econometric methods to provide detailed descriptive statistics on the recipients of government-insured mortgages. They also use the data to test hypotheses about the effects of FHA and VA loans on neighborhood characteristics The broader impacts include access to data and enabling science-informed discussions about issues that affect wealth accumulation, neighborhood outcomes, and intergenerational mobility. The project also involves students and early career researchers in data collection and analysis. 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 2025 · 2025-04

Immunotherapy is a cancer treatment that uses the patient's own immune system to control tumor growth. Adoptive cell transfer of chimeric antigen receptor (CAR) T cells into cancer patients has revolutionized the treatment of blood cancers, demonstrating the power of synthetic signaling receptors for immunotherapy. Despite this tremendous promise, there is a need for better, highly diverse receptor systems for effective therapies to apply immunotherapy to solid tumors. There is a wide range of potential receptor design directions to engineer more potent immunotherapeutic cells. To address the combinatorial complexity of possible design solutions, computational tools and methods are vital for the continuous innovation and improvement of these therapies to make them safer, more effective, and tailored to individual patient needs. This project aims to develop a computational framework with a novel methodology that integrates knowledge from published scientific papers and databases with experimental data using large language models (LLMs) and graph neural networks (GNNs), to provide a tool that will enable transformative cancer immunotherapy treatment designs. This project includes outreach programs and educational storytelling videos to introduce future generations of engineers and scientists and the broader community to the field of synthetic biology, as well as curriculum development and student mentoring. In this project, a fully automated framework will be created that integrates knowledge-driven and data-driven artificial intelligence approaches to recommend the most effective immunotherapeutic cell designs. Specifically, prompting methods will be developed as part of the framework to enable efficient use of LLMs for reliably extracting knowledge facts and symbolic rules from scientific literature. These facts will be represented in the form of knowledge graphs that capture the knowledge about signaling networks within immunotherapeutic cells, connecting newly designed receptors or pathways with markers of important processes, such as cytotoxicity and stemness. Further, GNN architectures will be utilized when exploring and improving the knowledge graphs, as well as when explaining the dynamic behavior of signaling networks within the tumor microenvironment. This project will also develop methods for reliably identifying and resolving inconsistencies in knowledge graphs when studying intracellular signaling. This work will provide new AI algorithmic approaches that incorporate scientific facts and principles within learning and reasoning to ensure explainable and trustworthy predictions. The developed computational methods will be generalizable to other T cells and immune cells, thus equipping synthetic biologists with a framework to quickly identify and explain receptor or pathway designs that could lead to potent cellular behaviors. 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 2025 · 2025-04

Biological research across scales from genes to ecosystems increasingly relies on distributed sensors and other physical infrastructure to measure and sample biological variables of interest. Such measurements are informing analyses related to fundamental biological questions, species conservation, ecosystem response to extreme events, and related topics. This trend towards sensor-based biology is exemplified by NSF’s National Ecological Observatory Network (NEON), which is collecting and making freely available massive quantities of biological data. Combined with satellite observations, biologists are presented with the unique challenge of synthesizing these data while advancing basic and applied research. Advances in Artificial Intelligence (AI) have the potential to greatly accelerate this work, but the community still faces pressing questions related to short- and long-term priorities and the most appropriate applications of cyberinfrastructure. This award will fund an interdisciplinary workshop, bringing together scientists to discuss priorities and chart a path forward. This workshop will specifically foster new cross-disciplinary collaborations across ecological research, AI-enabled cyberinfrastructure, and physical infrastructure. By identifying short- and long-term research priorities with both ecologists and infrastructure developers, as well as related next steps, the workshop will lead to new knowledge, research roadmaps, interdisciplinary teams, pilot projects, and frontier ecological science research proposals. These will address critical knowledge gaps across the ecological and environmental sciences, as well as help to define community-wide needs for next-generation infrastructure to support transformative research breakthroughs. This workshop will help to accelerate innovation and translate discoveries into scalable, real-world applications to solve societal challenges such as ecological resilience, biodiversity loss, and sustainability. Cross-training researchers in AI and infrastructure will help to build a broad AI-capable national workforce across applied fields such as agriculture, forestry, land management, and environmental science. 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 2025 · 2025-04

With support of the Chemical Catalysis program in the Division of Chemistry, Professor David Waldeck of the University of Pittsburgh is studying how the spin of electrons affects electrochemical reactions. Although chemists have long known that both the electron spin and charge are important for making and breaking chemical bonds, considerations of the electron charge has dominated past studies in electrochemistry. Recent work on water splitting, as well as a few other reactions, show that the electron spin can significantly impact reaction selectivity and reaction efficiency. Waldeck will develop new methods to deliver spin-filtered electron currents to conventional achiral catalysts and will use them to study how the electron spin affects electrocatalytic reactions. Success of this research program promises to introduce a new tool for examining electrochemical reaction mechanisms and for improving the selectivity of electrochemical reactions. The fundamental scientific outcomes of this project have potential impact beyond electrochemical water splitting, with relevance to other processes involving spin transport and new mechanistic views of catalytic reactions. The graduate, undergraduate, and high school students involved in the project will be trained in unique aspects of electrochemistry and engage in a consortium of research groups involved in chiral induced spin selectivity (CISS) research. With support of the Chemical Catalysis program in the Division of Chemistry, Professor David Waldeck of the University of Pittsburgh will leverage the chiral induced spin selectivity (CISS) effect, which states that the transmission of electrons through a chiral material depends on their spin, to create spin-filtered electron currents for electrochemistry studies. Waldeck’s team will create heterostructured catalyst scaffolds, which possess an intermediate chiral ‘spin transport layer’, to control the population of electron spins at the catalyst surface. In this way, they will examine how the electron spin affects multi-electron redox reactions and enantioselective oxidation reactions. For multielectron reactions, they will examine whether (and how) the orientation of the electron spins with respect to each other affects the distribution of chemical products. In enantioselective chemical reactions – a type of reaction that produces chiral molecules with a particular handedness – they will study how the electron spin can be used to spin-polarize the electron clouds of chemical intermediates and direct the chemical reaction toward one ‘molecular handedness’ over the other. 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 2025 · 2025-03

This project funds research that looks to combine numerical-experimental approach to understand and model the synthesis of large populations of advanced materials that are a hundred thousand times smaller than human hair. The research looks to develop a data science-based framework to enable learning, predicting, and simulating hard-to-model nanoscale fabrication processes, which underpin a variety of emerging applications in electronics, energy storage, and biomedicine. Proposed research resonates with the global quest towards realizing the potential of artificial intelligence and machine learning in boosting American competitiveness in advanced manufacturing. The scientific community can benefit from this research by extending the approach to a broader set of nanoscale material systems including different oxide-supported metal nanoparticles. Research will study the evolution of alumina-supported iron nanoparticles which serve as nanocatalysts for the chemical vapor deposition (CVD) growth of vertically aligned carbon nanotubes (VACNTs) for next generation thermal interfaces and electrical interconnects. Educational impact intends to include upskilling STEM students and junior scientists on timely topics at the nexus of data and manufacturing sciences. Moreover, the project will strive to generate jargon-free outreach materials explaining topics in machine learning and advanced nanomanufacturing to the general audience. The collective behavior and interactions among substrate-bound nanoparticles during the coupled physicochemical processes of oxidation/reduction, dewetting, coarsening, and catalysis are not well understood. This severely constrains the ability to reliably manufacture dense populations (hundreds of billions per square centimeter) of functional nanoparticles or active nanocatalysts. This research project intends to combine probabilistic data science methods with in-situ environmental transmission electron microscopy (E-TEM) to elucidate the dynamics of spatial proximity effects among ensembles of adjacent nanoparticles. The research looks to leverage spatio-temporal point process theory, a branch of probabilistic machine learning, for quantifying, predicting, and simulating the time evolution of location and size distributions and spatial dependencies during the formation and evolution of metal nanoparticles from thin films. In pursuit of these goals, the following tasks will be undertaken: (1) In-situ E-TEM measurements of population behavior of metal oxide reduction, nanoparticle formation by dewetting, coarsening by Ostwald ripening, and catalytic activation; (2) Automated image segmentation of in-situ E-TEM videos to extract salient information about the time evolution of locations, sizes, areal densities, shapes and activation of nanoparticles; (3) Learning from experimental observations: spatio-temporal statistical modeling of segmentation data using point process theory to characterize, predict, and simulate the evolution of interaction potentials; (4) Learning beyond experimental constraints: elucidating the physicochemical dynamics of metal/support interfacial phenomena for larger spatial domains, finer temporal resolutions, and unsampled conditions. 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 2025 · 2025-03

This doctoral dissertation project investigates adaptive processes in an early complex cultural contexts. Bioarchaeology, the study of human skeletal remains from archaeological sites, provides a means to uncover specific details about the lives of past populations, including their health, diet, and how ancient communities adapted to change. Beyond the training of a graduate in scientific methods of data collection and analysis, the project contributes bioarchaeological data to an open-access database, and fosters scientific collaboration. This project uses bioarchaeological, isotopic, biodistance, and paleopathological methods to reconstruct profiles, dietary patterns, and health indicators from skeletal remains. Isotopic analyses provide data about mobility and diet, while comparative analyses with collections from other sites provide a robust dataset for contextualizing the findings within regional patterns. The use of photogrammetry during fieldwork ensures the preservation of burial data, supporting viable archival practices and creating long-term digital records for future research. By integrating new datasets with existing collections, this project underscores the importance of preserving archaeological heritage while advancing understanding of culture. 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 2025 · 2025-03

NON-TECHNICAL AND TECHNICAL: This award will provide partial support toward travel and registration expenses for graduate students, postdoctoral associates, and early career researchers to participate in the Short Course on "Physics of Charged and Ion-Containing Polymers" at the 2025 March Meeting of the American Physical Society (APS). The course will introduce attendees to the physics of charged and ion-containing polymers, including (1) the fundamental physics of charge interactions in polymeric systems, (2) simulating the structure and dynamics of charged and ion-containing polymers, (3) major classes of charged polymers including ionomers, polyelectrolytes, and charged biopolymers, with special attention on methods for characterizing these systems, and (4) industrial applications of charged polymers. It will be taught by a group of speakers from academic institutions and national labs and will include a panel discussion with industrial R&D experts in these topics. Ionic and charged polymeric materials are at the forefront of research and applications in energy-related technologies, biomedicine, pharmaceutical and cosmetic industries, and others. The short course will offer an introduction across the forefront of this growing scientific area. It will prepare graduate students and early-career researchers for advances and potentially careers in these areas. The conference will aim for broad participation and extensive opportunities for participants to network with other early-career researchers and industrial scientists. The organizers also plan to work with the invited speakers and instructors to prepare a tutorial review on charged and ion-containing polymers based on the material presented in the short course and submit it for publication in a polymer journal. This could broaden the impact of the short course far beyond those able to attend in person at the APS Meeting. 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 2025 · 2025-01

This project aims to serve the national interest by creating an efficient, scalable method through which a wide range of undergraduate students can find, analyze, and understand relevant biology research in research journals. Such experiences give students an understanding of recent advances in the field, allow them to make stronger connections between research and real-world applications, improve their professional skills, and support them in becoming lifelong learners. By creating an innovative model called Peer-Reviewed Presentation Exchange (Perepex), the project aims to provide students in many different biology courses with opportunities to engage in low-stress, structured discussions and presentations of biology research papers. This approach will allow a much broader range of students to have access to powerful learning experiences that traditionally have been available only in smaller, advanced courses in selective institutions. The project's goals include adapting and refining the Perepex model for large-enrollment courses and less selective institutions, while focusing on inclusivity and effective peer feedback mechanisms. The project will leverage five years of successful outcomes from an advanced biology course and the expertise of biology educators and a learning scientist. Methodologically, it will employ easy-to-use, low-cost technologies to facilitate seamless integration into existing courses. The project team plans to study the effectiveness of this model in improving students' engagement and self-efficacy, as well as the quality of peer feedback in various educational contexts. Dissemination of results will be conducted through conferences, journal publications, and a dedicated website. 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 creating, exploring, and implementing 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 2025 · 2025-01

Laser powder bed fusion is increasingly being used to produce metallic parts in a variety of high-value industries, like aerospace, biomedical, and automotive. However, parts manufactured are prone to shape distortion and excessive heat or stress build up due to uneven temperatures across the part during the printing process, leading to cracks or other defects. Prior research has shown that scan sequence (i.e., the order in which geometric features on the part are scanned by the laser) can help in homogenizing temperature distribution across a part, thus reducing distortion, overheating and excessive stress. However, scan sequence is currently determined based on trial-and-error or heuristics, leading to inconsistent and suboptimal results. This project supports a scientific investigation into an approach for optimally determining scan sequence using models of the printing process. The knowledge created through this investigation will enable 3D printing of complex metallic parts with fewer failed or defective prints, thus improving the economic viability of laser powder bed fusion. The research will enrich an outreach program to excite middle school students in Detroit and inspire them to pursue careers in STEM fields. The objective of the project is to mathematically, numerically, and experimentally uncover the relationships between optimal scan sequences, temperature distribution, distortion, and residual stress in laser powder bed fusion using physics-based and data-driven thermal or thermomechanical models. The impacts of optimal scan sequences on microstructure and other part quality metrics will also be investigated. This objective will be achieved by: (1) incorporating advanced thermal effects into the determination of optimal scan sequences using data-driven models; (2) numerically investigating when optimal scan sequences generated using only thermal models do not adequately reduce distortion or residual stress; and (3) introducing thermomechanical effects into the determination of optimal scan sequences in cases where thermal models alone are deficient. The methods will be validated experimentally. Translation of knowledge from this project to application may accelerate the adoption of additive manufacturing in broader industries. 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 · 2024-10

The unprecedented growth of wireless connectivity and increasing demand for high data rates necessitate a transition to the FR3 frequency band (7-24 GHz), which offers the combined benefits of extensive coverage, high capacity, and ultra-fast data rates. This project addresses the critical need for enhanced Spectrum Access Systems (SAS) within the FR3 band to ensure efficient coexistence among its diverse applications, such as mobile satellite services, radio astronomy, and various federal and commercial operations. The project undertakes the development of a precise wireless digital twin to accurately model signal propagation and enable spectrum access and management systems to improve spectrum sharing requests for general users, as well as identify optimal bands or channels at any given time and location, allowing more effective channel requests from the access systems. Additionally, it facilitates more efficient spectrum allocation in densely populated areas, minimizing interference and maximizing spectrum efficiency. The PIs will develop both individual and group-based research projects related to this proposal, actively recruit female and underrepresented students for research, and provide research opportunities for K-12 students through their institutions' outreach programs. This research project is organized into three thrusts. The first thrust focuses on building a wireless digital twin to capture the physically-largest, static features of the environment using 3D scene representation and ray tracing tools to simulate signal propagation. This validates the core capability of accurately identifying signal transmission paths and channel quality. The second thrust aims to develop a dynamic wireless digital twin that accounts for environmental dynamics, such as human movement and changes in object positions, by optimizing real-time channel updates. The third thrust ensures the digital twin adheres to spectrum sharing regulations, prioritizing incumbent users while integrating real-time spectrum usage data to enhance predictive capabilities. This research provides a comprehensive framework for managed spectrum sharing in the FR3 band, fostering the deployment and performance of next-generation wireless networks. 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 · 2024-10

Forest ecosystems play a critical role in the Earth system as major carbon sinks, which are essential for carbon neutralization and climate change mitigation. However, significant deforestation and forest degradation could push the Earth to climate change tipping points. As such, there is a growing interest in forest carbon sequestration through afforestation and reforestation initiatives from local to global scales. These developments have led to a strong demand in advancing the scientific understanding of the impact of forest carbon dynamics in the carbon cycle. Specifically, a new generation of models has emerged to connect individual plant level processes with the global carbon cycle. In addition, advancements in remote sensing have generated unprecedented new high-resolution measurements at the global scale. Despite these opportunities, several key challenges persist in understanding forest carbon dynamics, including the lack of understanding of fine-scale but widespread disturbances such as tree mortality in existing remote sensing products, the computational bottlenecks of the theory-based models for global scale analysis, and the limited flexibility of the models in enhancing the prediction quality using new observations. This project aims to develop new capabilities to bridge these research gaps and significantly advance the monitoring and understanding of forest carbon dynamics in the Earth system. The enhanced understanding can provide necessary information for estimating carbon budgets and realizing carbon neuralization goals. The research results will be used to develop materials for both undergraduate and graduate courses in AI and geosciences. The project will also engage students from underrepresented groups in the research activities and partner with K-12 schools to promote education on topics intersecting AI and geosciences. This project will result in several advances of artificial intelligence techniques with the potential to further the understanding of how forest carbon influences the Earth system’s carbon cycle under climate change and what terrestrial ecosystems’ capacity is in climate change mitigation. First, the project team will develop cross-platform and cross-region learning frameworks to enable fine-scale carbon dynamics monitoring at large geographic scales. Second, the team will create high-fidelity fast approximations of the theory-based carbon forecasting model by developing new theory-guided meta-learning and invertible frameworks to enable global-scale capabilities under diverse climate change scenarios. Finally, the team will develop new theory-guided diffusion methods to significantly enhance the ability of theory-based models in improving predictions by leveraging observations enabled by new sensing platforms. This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering. 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 · 2024-10

Haplotype inference and allele-specific transcript expression quantification are two fundamental problems in genetics and genomics. Haplotype inference aligns maternal and paternal alleles of genetic variants along two diploid chromosomes, whereas allele-specific expression quantification obtains the expression levels of transcripts of maternal and paternal origins from RNA-seq reads. These two problems are coupled in that one can affect the accuracy of the other: accurate allele-specific expression quantification requires accurate haplotypes to map RNA-seq reads to and the accuracy of haplotype inference can be enhanced by allele-specific RNA-seq reads. While existing works have considered these two problems separately, this project develops a computational framework to address these two fundamental problems jointly in a single statistical framework to enhance the accuracy of both inferred haplotypes and allele-specific expression quantification. The computational methods to be developed in this research will advance various aspects of biological research that require accurate allele-specific expression estimates and haplotypes, including mapping allele-specific eQTLs, detecting imprinted genes, imputing untyped variants, finding signatures of natural selection, and detecting recombination events. The outcome of the research will be used in outreach activities in minority serving institutions to recruit graduate students. The project develops a computational framework for obtaining accurate allele-specific expression measurements and haplotypes from RNA-seq and genotype data. Two existing frameworks, one for transcript expression quantification and the other for haplotype inference (e.g., Beagle), are combined into a single framework, while keeping the computational efficiency of the original frameworks. Each of these two existing frameworks is modified to address two previously-unmet challenges regarding allele-specific reads: for the RNA-seq quantification, the project develops a mathematically rigorous approach to obtaining identifiable allele-specific expression estimates at gene level, at transcript-set level, or at individual transcript level, whereas for haplotype inference, the project couples the model in Beagle with RNA-seq quantification methods of these investigators to jointly estimate identifiable allele-specific expression levels and haplotypes that are consistent with each other. The computational methods are benchmarked on allele-specific eQTL mapping, using genotypes and RNA-seq reads from human trios and LG/SM intercross mice with known haplotypes. The outcome of the research is available at http://www.cs.cmu.edu/~sssykim. 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 · 2024-10

The American Lung Association stated that air quality, in particular that which contains large amount of particulate matter in urban settings, is a serious health problem for residents. In addition, for many parts of the U.S, government owned and maintained air monitoring equipment and data does not have the spatial resolution to provide communities, especially those in low income heavily polluted areas, with air quality data. This Civic Innovation Challenge (CIVIC) planning process brings together a science team with local community and civic organizations to co-design a science/research-based, implementable, scalable, and sustainable solution that addresses air quality, an important local community resilience problem in one of the Pittsburgh, low income, urban neighborhoods. This CIVIC planning activity supports infrastructure for the collection of local air quality data, a user-friendly platform that provides real or near real time data and its visualization and analysis. It also provides education and training of community members in advocacy that allow them to effectively work with local governments and other entities to improve air quality. The planning process will bring together all relevant stakeholders and community residents to co-design the low-cost air monitoring network and advocacy education and training regimen. Broader impacts include a community-based air monitoring network that will provide hyperlocal, real-time, air quality data to increase community education and awareness of air quality and its impacts on their health and lives and provide essential data to allow community advocacy for interventions and mitigation strategies thereby improving community health and well-being. The project involves installation of low-cost air quality measurement and monitoring infrastructure to support a network of community scientists with online accessible tools to collect community air quality data, share individual and collective narratives about local environmental issues, and support the community in helping them know how to critically analyze data to build a science and data-driven advocacy campaign for improved community air quality. The project team will develop community training and education programs about air quality data, data analysis literacy, and how the data can be used for advocacy. Another objective is to design, with partners, and implement a community-engaged and participatory action approach to improving local air quality. An online data visualization platform will be developed to provide community members access to real-time air quality data that can be used to improve understanding, awareness of the impacts of compromised air quality to help individuals and the community advocate for action. This planning process will improve the understanding of how community-based efforts can be designed to lead to policy changes. It will also foster and strengthen collaboration between researchers and community stakeholders, develop new collaborations and partnerships, refine the research vision to enable submission of a successful follow-on proposal that will implement the community vision and provide data to address research questions and develop evaluation methods and measures for the follow-on project. Through this approach, the project team feels the activities and anticipated outcomes can be replicated in other similar urban communities facing similar challenges. This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. The proposal is co-funded by the NSF Directorate for Geosciences and Directorate for Computer Information Science and Engineering. 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 · 2024-10

Faced with an ever-changing environment of rich and complex stimuli, the brain needs to flexibly adapt to changes in the outside world to efficiently process relevant information. This project studies how the brain selectively processes relevant information depending on task demands and coordinates across different brain regions to support flexible behavior. This project will develop a comprehensive framework to analyze information flow in large-scale brain network models. Research findings will be incorporated into coursework to expose undergraduate students to cutting edge research. Regular outreach activities, such as summer camps and in-class visits to local middle and high schools, will be conducted to inspire K-12 students' interests in interdisciplinary research. The investigator will actively recruit students from under-represented or disadvantaged backgrounds to participate in computational neuroscience research. The goal of this project is to develop new analytical tools to disentangle information flow across brain regions to understand how sensory information can be flexibly routed in a highly inter-connected brain. The project will integrate existing datasets of large-scale neural activity recordings and anatomical connectivity to provide a mechanistic understanding of communications between brain regions. Complementary approaches, such as spiking neuron network models with biological details, information-theoretic analysis and parameter optimization methods, will be used to accomplish three aims. The first aim compares alternative mechanisms of selective attention in detailed spiking neural network models and tests model predictions with existing datasets. The second aim develops analytical tools to dissect information pathways in multi-population network models. The third aim fits multi-regional models, constrained by connectome data, to widefield calcium activity from mouse cortex under different behavioral contexts. The education components include curriculum development, undergraduate and graduate students research training, summer camps for high school students and regular in-class visits to local middle and high schools. 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 · 2024-10

Understanding the processes that allow new species to establish is central to understanding the diversity of life on earth. A major macromutation where the whole genome is duplicated (known as polyploidy) is the most common form of instantaneous speciation in flowering plants. Global geographic patterns indicate that polyploid species are more frequent in stressful habitats. This research will use population-level experiments with a model aquatic species under varied stresses to elucidate mechanisms of polyploid success. It will contribute to society by expanding knowledge of polyploids which are important tools for improving agricultural crops, and of duckweeds which are emerging systems for wastewater remediation and biofuels. The work will promote broadening participation through student training, creation of new high school curricula and associated materials that link the effects of whole genome duplication to productivity under natural and agricultural settings. It will also create a children’s book aimed at solving community and societal problems using science. The research fills a significant gap in our understanding of this critical biodiversity-generating process by answering the question of what determines when polyploids will go extinct, coexist with, or competitively exclude their non-polyploid ancestors? By combining the powerful analytic framework provided by Modern Coexistence Theory with the rigor of a highly manipulable and replicable experimental system, the work will transform knowledge of the formative early phase of polyploid existence. Specifically, thousands of reciprocal invasion experiments will be conducted with mixed ploidy communities that vary in functional divergence and phylogenetic distance and across abiotic and biotic conditions motivated by global patterns in polyploid prevalence. This population-level approach will test long standing hyptheses concering the role of abiotic/biotic stresses, functional traits and phenotypic plasticity in promoting polyploid persistence. The work will accelerate knowledge of this crucial phase in plant evolutionary history and explain global terrestrial and aquatic patterns of polyploidy. 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

A major challenge in deaf science education is the lack of standard signs in American Sign Language (ASL) for many scientific concepts. For example, one student might fingerspell a term, another might use a sign they created, and a third might use a different sign altogether. This variation can make it difficult for students to engage effectively in class without a shared understanding of scientific terminology. Collaborative problem-solving activities are known to improve the understanding of complex concepts, but traditional support methods mainly benefit hearing students. This makes it more challenging for deaf students who use sign language to participate fully. Additionally, deaf individuals are significantly underrepresented in scientific fields, which adds to their marginalization. To address these issues, the project will develop a new artificial intelligence tool designed to revolutionize collaborative learning for deaf students in science, helping them to better understand and communicate in university biology classes. The tool will use augmented reality, signed animations, and sign recognition to provide real-time information about the signs used in classroom conversations. The primary hypothesis of the research is that artificial intelligence-driven technology can significantly improve the collaborative experience and learning outcomes for deaf students. The project focuses on establishing common ground, which is particularly challenging in science courses where standard ASL signs are lacking. The team uses augmented reality to visualize scientific lexicon representations, including signing avatars and English captions. These aids complement existing learning strategies, such as parallel visual processing and the creation of new terms. This project will assist students in learning new terminology introduced by teachers or emerging from classroom conversations. It caters to the diverse needs of the deaf community in terms of language fluency, hearing ability, and use of assistive technologies by providing flexible, non-invasive learning supports. In support of the project goals, the team will convene co-design sessions, conduct prototype testing, and implement an experimental study to assess the impact of the tool. The project team includes experts in ASL scientific lexicons, learning sciences, human-computer interaction, and artificial intelligence. The goal is to improve inclusive education strategies, focusing on collaborative learning in science. The project contributes to human-computer interaction by identifying design principles for intelligent support to signing learners. It advances artificial intelligence through state-of-the-art sign recognition and generation systems, adaptive to learner variability, and incorporating facial expressions and prosodic features. In learning science, the project explores the relationship between adaptive scaffolds for lexical alignment, collaborative processes, and learning outcomes. In terms of deaf education, the project develops interventions supporting collaborative learning among deaf students. Acknowledging the diverse experiences within North American deaf communities, the initiative works to understand these nuances. If successful, the technology could generalize to other learning scenarios involving collaborating deaf students. This work will also support professional and scientific opportunities for deaf scientists, students, and trainees. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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.