Tulane University

NSF Awards · FY 2024 · 2024-10

NONTECHNICAL ABSRACT This award supports theoretical and computational research and education to advance Kohn-Sham density functional theory. This theory provides a computationally efficient and usefully accurate description of the electronic properties of materials enabling molecules, chemicals, and materials to be modeled. The aim of this project is to develop more accurate computer models of materials. To do this, the PI will focus on the “glue” that binds one atom to another to form molecules and materials: the exchange-correlation energy. In this research, the PI will develop even more accurate approximations for this “glue” that still permit efficient simulation on computers. Kohn-Sham density functional theory is widely used in physics, chemistry, and materials science to predict what atoms, molecules, and materials can exist and with what properties. Starting from the first principles of quantum mechanics, this theory constructs the ground-state energy and electron density of a many-electron system from an auxiliary system of non-interacting electrons including the contribution from the "glue", facilitating practical computation. The exact exchange-correlation energy must be approximated. Widely predictive approximations should themselves be based upon first principles and be accurate enough to predict the small energy differences between competing states in complex materials and systems. The strategy of this project is to achieve more accurate but computable general-purpose approximations by incorporating more of the mathematical properties of the exact universal density functional for the exchange-correlation energy. Exact constraints and appropriate norms, non-bonded systems for which the approximations can be exact or very accurate, guide the construction of functionals that reliably predict bonds without being fitted to bonds. Kohn-Sham density functional theory also provides guidance and input to machine-learning approaches to materials theory. Progress in the improvement of the functionals has been relentless but slow, requiring rigorous theory, intuition, and persistence. Since 1965, there has been great progress from the original local spin density approximation to generalized gradient approximations (GGAs), meta-GGAs, and their exact-exchange hybrid or self-interaction corrections. It is hard to get everything right, but worth the effort. The goals of the proposed research are to advance understanding of this theory, and to improve its useful approximations in order to better understand and predict interesting materials. Broader impacts will include more reliable predictions for the existence and properties of new materials the education of graduate students and postdoctoral fellows, and research experiences for undergraduates, high-school students, and middle-school students. TECHNICAL ABSTRACT This award supports theoretical and computational research and education to advance Kohn-Sham density functional theory. While the appropriate norms used to construct non-empirical density functionals for the exchange-correlation energy are normally correlated, the approximations that employ only occupied orbitals, especially the higher-level ones, can often describe strong-correlation effects on the energy through symmetry breaking. The principal investigator and his research group have recently found a proper and possibly general-purpose Perdew-Zunger self-interaction to the local spin density approximation, locally scaled down in many-electron regions and called LSIC alpha. They are investigating an interpolation between LSIC alpha and the advanced r2SCAN meta-generalized gradient approximation (meta-GGA), each dominating in its appropriate region of space. They plan to use these improved functionals to explore how reliably symmetry breaking can simulate strong correlation. They will also investigate the degeneracies at the non-interacting-electron level that create strong correlation, and the physical interpretation of excited-state solutions to the Kohn-Sham equations. Evaluating a non-self-interaction-corrected functional on the too-localized Hartree-Fock density instead of its own self-consistent density often leads to higher accuracy, due to an unconventional but understandable error cancellation. The principal investigator and collaborators will test this idea on materials problems such as the formation and reaction energies of transition-metal oxides that are currently treated with a +U correction. They will search for violations of their conjectured tight lower bound on the exchange energy. They will use an accurate inversion of an accurate electron density to investigate how reliably the exact Kohn-Sham orbital energies approximate all the vertical ionization energies. Since the r2SCAN meta-GGA is already too non-local for metals, especially magnetic ones, they will develop a constraint-satisfying GGA for metals, and a local indicator of metallicity that will permit an interpolation between it and r2SCAN or functionals beyond r2SCAN. They will investigate how the derivative discontinuity evolves from a smooth energy surface as an open system moves away from its electron reservoir. They will revisit the sd transfer errors of approximate functionals, including modern ones. They will generalize r2SCAN to ground states with non-zero current densities, study the distribution of dimensionless meta-GGA ingredients over real systems, investigate the exchange-correlation-corrected screening by a uniform electron gas in position space and time, delve into an atom-cluster phase transition in jellium spheres, find and test an improved self-interaction correction to the random phase approximation (RPA), and extract an RPA-based long-range van der Waals correction to improved functionals that need one. Broader impacts will include more reliable predictions for the existence and properties of new materials, the education of graduate students and postdoctoral fellows, and research experiences for undergraduates, high-school students, and middle-school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The transformation of the news ecosystem from traditional print media to online platforms has fundamentally changed how people engage with current events. This decentralization of media has arguably resulted in greater access to diverse and timely information, but it has also led to growing concerns about unreliable information, polarization, and the role that computer-mediated communication plays in fostering these phenomena. This project advances the understanding of how people interact with news online and how their behaviors evolve over time. By analyzing how people share, support, or criticize news on social media, this research identifies different stages of news engagement, in terms of the types and tone of news people interact with. Understanding these progression stages and the factors that influence them will provide insights into designing online platforms with healthy news ecosystems, reducing the spread of unreliable information while maintaining information diversity. The project will integrate findings into university courses and community workshops, ultimately fostering a more informed public. To meet these goals, this project advances computational models of online news engagement through three main research thrusts. First, it develops models to identify various types of news engagement behaviors and their progression stages, innovating advanced language and user modeling techniques to predict future behavior patterns. Second, it establishes a technical framework for estimating causal relationships between different news engagement behaviors, combining natural language processing with causal inference methods to estimate treatment effects from observational data. Third, the project tests socio-technical hypotheses regarding strong positions on issues, trust, and information reliability using this framework. The research employs multi-year, publicly available data from online social media platforms, enriched with databases of political news sources. Evaluation methods include machine learning metrics, semi-synthetic experiments, and validation and verification through surveys and focus groups. This comprehensive approach will produce more accurate predictive models and robust causal estimation methods applicable across various domains to study human behavior from online data. 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 research in this project is in the area of analytic number theory, a field that uses analytic functions to study arithmetic structure. The main objects of study in this project are modular forms, complex analytic functions that encode a wide variety of arithmetic information in various ways and play a major role in modern number theory, with connections to combinatorics, algebraic geometry, representation theory, topology, and mathematical physics. While the most classical modular forms are holomorphic, real-analytic modular forms have also been studied for decades and become essential tools in analytic number theory. More recently, harmonic Maass forms have appeared in many applications, for example, to indefinite theta functions, combinatorics, and elliptic curves. This project will explore the arithmetic information encoded by the harmonic Maass forms and their closely related generalizations, and ways of extending classical methods from analytic number theory to study them. The PI will also use the grant to support the dissemination of the research ideas by the PI and her PhD students at conferences and to organize number theory seminars. The PI plans to explore the connections between real-analytic modular forms and L-functions. This project will elucidate connections between values of L-functions and harmonic and polyharmonic Maass forms, and will use these connections to develop new methods of constructing modular forms and summation formulas for mock modular forms. The methods will utilize differential operators on modular forms, the spectral theory of automorphic forms, and techniques from the analytic theory of L-functions such as converse theorems. Applications to the study of Hurwitz class numbers and quadratic number fields will also be explored. This project is jointly funded by Algebra and Number Theory program, and the Established Program to Stimulate Competitive Research (EPSCoR). 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

In today’s software-centric world, ultra-large-scale software repositories, e.g. GitHub, with hundreds of thousands of projects each, are the new library of Alexandria. They contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important research hypotheses. However, the current barrier to entry is prohibitive and only a few with well-established infrastructure and deep expertise can attempt such ultra-large-scale analysis. Necessary expertise includes: programmatically accessing version control systems, data storage and retrieval, data mining, and parallelization. The need to have expertise in these four different areas significantly increases the cost of scientific research that attempts to answer research questions involving ultra-large-scale software repositories. As a result, experiments are often not replicable, and reusability of experimental infrastructure low. Furthermore, data associated and produced by such experiments is often lost and becomes inaccessible and obsolete, because there is no systematic curation. Last but not least, building analysis infrastructure to process ultra-large-scale data efficiently can be very hard. This project will continue to enhance the CISE research infrastructure called Boa to aid and assist with such research. This next version of Boa will be called Boa 2.0 and it will continue to be globally disseminated. The project will further develop the programming language also called Boa, that can hide the details of programmatically accessing version control systems, data storage and retrieval, data mining, and parallelization from the scientists and engineers and allow them to focus on the program logic. The project will also enhance the data mining infrastructure for Boa, and a BIGDATA repository containing millions of open source project for analyzing ultra-large-scale software repositories to help with such experiments. The project will integrate Boa 2.0 with the Center for Open Science Open Science Framework (OSF) to improve reproducibility and with the national computing resource XSEDE to improve scalability. The broader impacts of Boa 2.0 stem from its potential to enable developers, designers and researchers to build intuitive, multi-modal, user-centric, scientific applications that can aid and enable scientific research on individual, social, legal, policy, and technical aspects of open source software development. This advance will primarily be achieved by significantly lowering the barrier to entry and thus enabling a larger and more ambitious line of data-intensive scientific discovery in this area. 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

This project will study a class of machine learning algorithms known as deep learning that has received much attention in academia and industry. Deep learning has a large number of important societal applications, from self-driving cars to question-answering systems such as Siri and Alexa. A deep learning algorithm uses multiple layers of transformation functions to convert inputs to outputs, each layer learning higher-level of abstractions in the data successively. The availability of large datasets has made it feasible to train deep learning models. Since the layers are organized in the form of a network, such models are also referred to as deep neural networks (DNN). While the jury is still out on the impact of deep learning on the overall understanding of software's behavior, a significant uptick in its usage and applications in wide-ranging areas and safety-critical systems, e.g., autonomous driving, aviation system, medical analysis, etc., combine to warrant research on software engineering practices in the presence of deep learning. One challenge is to enable the reuse and replacement of the parts of a DNN that has the potential to make DNN development more reliable. This project will investigate a comprehensive approach to systematically investigate the decomposition of deep neural networks into modules to enable reuse, replacement, and independent evolution of those modules. A module is an independent part of a software system that can be tested, validated, or utilized without a major change to the rest of the system. Allowing the reuse of DNN modules is expected to reduce energy- and data-intensive training efforts to construct DNN models. Allowing replacement is expected to help replace faulty functionality in DNN models without needing costly retraining steps. The preliminary work of the investigator has shown that it is possible to decompose fully connected neural networks and CNN models into modules and conceptualize the notion of modules. The main goals and the intellectual merits of this project are to further expand this decomposition approach along three dimensions: (1) Does the decomposition approach generalize to large Natural Language Processing (NLP) models, where a huge reduction in CO2e emission is expected? (2) What criteria should be used for decomposing a DNN into modules? A better understanding of the decomposition criteria can help inform the design and implementation of DNNs and reduce the impact of changes. (3) While coarse-grained decomposition has worked well for FCNNs and CNNs, does a finer-grained decomposition of DNNs into modules connected using AND-OR-NOT primitives a la structured decomposition has the potential to both enable more reuse (especially for larger DNNs) and provide deeper insights into the behavior of DNNs? The project also incorporates a rigorous evaluation plan using widely studied datasets. The project is expected to broadly impact society by informing the science and practice of deep learning. A serious problem facing the current software development workforce is that deep learning is widely utilized in our software systems, but scientists and practitioners do not yet have a clear handle on critical problems such as explainability of DNN models, DNN reuse, replacement, independent testing, and independent development. There was no apparent need to investigate the notions of modularity as neural network models trained before the deep learning era were mostly small, trained on small datasets, and were mostly used as experimental features. The notion of DNN modules developed by this project, if successful, could help make significant advances on a number of open challenges in this area. DNN modules could enable the reuse of already trained DNN modules in another context. Viewing a DNN as a composition of DNN modules instead of a black box could enhance the explainability of a DNN's behavior. This project, if successful, will thus have a large positive impact on the productivity of these programmers, the understandability and maintainability of the DNN models that they deploy, and the scalability and correctness of software systems that they produce. Other impacts will include: research-based advanced training as well as enhancement in experimental and system-building expertise of future computer scientists, incorporation of research results into courses at Iowa State University as well as facilitating the integration of modularity research-related topics, and increased opportunities for the participation of underrepresented groups in research-based training. 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

Informed consent is a cornerstone of research ethics. However, standard consent procedures often fail to ensure participants understand the content and consequences of their participation in research. This project will extend scientific understanding of informed consent in the social and behavioral sciences by probing participant comprehension of data sharing options and preferences for confidentiality. These issue areas have grown in importance as researchers aim to conduct work that is both ethical and transparent. This project involves three related activities. First, the development of a scoping review of past research on the topic. Second, an expert workshop to discuss current practices that aim to enhance ethical consent and research trade-offs in consent design decisions. And third, the collection of original data through focus groups with researchers who seek to obtain consent for surveys, as well as interviews with research participants. Findings from this project will inform researcher decision-making with respect to informed consent design choices and contribute to evidence-based recommendations about how to improve standard practices for obtaining consent. Participation in research is essential for the progress of the social and behavioral sciences, and ensuring genuinely informed consent is key to the ethical treatment of participants. This project seeks to advance scientific understanding of informed consent, focusing on two key issues. First, the project assesses how participants understand confidentiality and data sharing in the informed consent process. Second, it evaluates how participants behave when introduced to various types of content included in the consent script and different modes of obtaining consent. The project begins with a scoping review of prior research on this topic and convening a workshop of experts to identify which issues are most pressing. These activities will inform subsequent original data collection in the United States and abroad. Focus groups will collect enumerator perspectives and recommendations on informed consent processes and gather specific feedback on the informed consent scripts used in cognitive probing interviews. The cognitive interviews will examine how research participants understand and react to content, language, or delivery variations in the informed consent process. This project is funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences. 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

Much of the anthropogenic CO2 released into the atmosphere has been absorbed by the ocean, which acts as a buffer against global warming and rapid climate change. But this makes seawater more acidic and corrosive to CaCO3 minerals (known as ocean acidification: OA), which is expected to harm marine organisms that build skeletons or shells from CaCO3 (marine calcifiers). Marine calcifiers typically grow their CaCO3 hard parts in a micro-scale “calcifying fluid (CF)” by modifying its chemistry from seawater to elevate the degrees of CaCO3 saturation (Ω). This process potentially enables calcifiers to cope with OA, at least to some extent. This research aims to establish the use of Raman Spectroscopy (RS) to constrain Ω of CF. Compared to other methods, this approach will be simple, rapid, and non-destructive to organisms and CaCO3 samples. This work will be a cornerstone for an important methodology that can advance knowledge on calcification mechanisms and resilience of marine calcifiers against OA. Rapid precipitation of CaCO3 at higher Ω levels leads to greater structural disorder in the mineral lattice due to increased defects and kinetically-driven uptake of minor/trace elements (e.g., Mg). Thus, the degree of lattice disorder determined from the positional shift in and width of Raman peaks should be a function of Ω. This concept has been validated for aragonites and RS has been extensively used to constrain Ω of CF in aragonitic calcifiers. But in calcites, Mg inclusion by substitution of Ca also contributes to structural disorder significantly. This necessitates a correction for Mg-driven lattice disorder for effective use of RS on calcitic organisms, which is currently lacking. This research will produce an extensive set of abiogenic calcite samples that vary significantly in Mg contents and solution Ω for precipitation based on laboratory experiments using artificial seawater. These samples will disentangle the overlapping effect of Mg inclusion and Ω on calcite structural disorder. This investigation will for the first time generate Raman calibrations for Mg contents and Ω that are applicable to marine biogenic calcites. This project is jointly funded by the Geobiology and Low-Temperature Geochemistry Program (GG), the Established Program to Stimulate Competitive Research (EPSCoR), and the Marine Geology and Geochemistry Program (MGG). 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

Given the persistent challenge of racial inequity in STEM, there is a clear need for new models that spur and sustain racial equity change. Successful departmental team-based change efforts demonstrate that change can be created and sustained at the meso level of an institution (i.e., departments, centers, and units as the focus for change). This project will bring together experts in institutional change and experts in advancing racial equity with the goal of combining existing, well tested change models to produce a new, racial equity focused model of change in higher education—the Equity Departmental Action Team (EDAT) model. This model will focus on shifting departmental cultures in ways that benefit, and are grounded in the experiences of, those with historically marginalized racial and ethnic identities. This project will advance the scholarship of racial equity by developing, testing, and refining the EDAT model with STEM departments at a Minority Serving Institution and disseminating the model through partnership with national higher education associations. This project will take place in two major phases: 1) development of the Equity Departmental Action Team (EDAT) model, and 2) pilot of the EDAT model in STEM departments at a Minority Serving Institution, the University of Colorado Denver (CU Denver). The development of the new EDAT model will draw from existing change programs, including the Departmental Action Team (DAT) model and the Dialogues and Change Agent programs. It will integrate multiple theories from systems change, social justice change, social psychology change agency, and intergroup contact. Research activities will focus on both the process and impact of the EDAT model. The project will use surveys, focus groups, interviews, and participant journaling to explore the following research questions. RQ1: To what extent do Foundational Experiences prepare EDAT members for racial equity work? RQ2: What strategies do EDATs deploy when engaging in racial equity work? RQ3: To what extent do EDATs integrate racial equity into departmental culture? Research and program evaluation will be conducted simultaneously with the EDAT implementation so the model can be iteratively refined throughout the project. Dissemination of the model will take place in collaboration with partners from the American Association of Colleges and Universities and the Coalition of Urban Serving Universities - Association of Public and Land-grant Universities. This collaborative project is funded through the Racial Equity in STEM Education activity (EDU Racial Equity). The activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. This activity aligns with NSF’s core value of supporting outstanding researchers and innovative thinkers from across the Nation's diversity of demographic groups, regions, and types of organizations. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. 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

Corals are animals found throughout the tropical oceans that construct hard skeletons throughout their lifetime. Eventually these skeletons are cemented together to form coral reefs. Coral reefs are important ecosystems that are threatened by climate change. Lab experiments have shown that the coral skeleton-building process is negatively impacted by ocean warming and acidification. Ocean warming and acidification are caused by carbon dioxide (CO2) released to the atmosphere by humans since the mid-19th century. Lab experiments often test these impacts over weeks to months, but climate change affects wild corals in the ocean over decades to centuries. One of the only ways to study how wild corals have been affected by these changes is to measure past growth rates in their skeletons. Corals develop annual bands much like tree rings creating a record of their growth over time. This award will support a combined research and educational program focused on student training, improving coral data access, and public engagement. Specifically, the program will improve methods for coral skeletal analysis and build a virtual coral core repository. The project will also create an interactive app in which students or the public can interact with coral data and contribute to crowdsourced data analysis. Research conducted under this CAREER award will address the drivers of long-term changes in coral calcification rates during the industrial era. The first stages of the project will focus on developing new tools for growth-rate analysis from coral cores and building the virtual core repository to make all existing coral core CT scans publicly accessible. This will enable a big-data approach in which several hundred cores from across the tropics can be processed for past growth rates with optimal analysis and statistical methods, providing the most comprehensive test of whether coral growth rates in the wild have changed under the past century or more of ocean warming and acidification. The measurement of growth rates in these cores will be transparent, with the traceable analysis files saved in the virtual repository alongside each core, enabling multi-observer analysis by researchers across the globe. Subsequent analysis of interannual variability of coral growth, or reconstruction of past climate variability from growth rates, will be enabled by the virtual repository, including continued addition of new cores collected during this project. This award also includes educational activities including the development of K-12 lesson plans that integrate novel 3-dimensional hologram projections of coral skeletons, and college-level STEM lesson plans based on an app that will be developed to visualize coral cores. This project is jointly funded by the Marine Geology and Geophysics Program, the Established Program to Stimulate Competitive Research (EPSCoR), the Biological Oceanography Program, and the Ocean Education 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.