Columbia University

NSF Awards · FY 2024 · 2024-10

Cities are at a technological crossroads. While the rise of generative Artificial Intelligence (AI) promises to reshape how urban residents inhabit, study, work, and conduct their daily lives, adopting cutting-edge technology into socially complex and high-stakes scenarios carries enormous risks. It provides fertile ground for a crisis of public trust in institutions, experts, and technology. Because AI is a particularly abstract and inscrutable “black box,” we offer an approach that fundamentally reimagines what a responsible co-design process for urban AI could be. At the center of this work is the creation of a new “Citizen AI,” built from the bottom up and as the culmination of a plurality of voices, experiences, and forms of expertise. The project team, The Trust Collaboratory (TC), and the Gen-4 NSF Center for Smart Streetscapes (CS3) at Columbia University, together with over ten community-based organizations in Harlem, will create a process toward local use cases of urban AI based on community-driven privacy, safety, reliability, and transparency parameters. At the center of this process will be the co-creation of a community-based conversational engagement tool (teLLMe) that redefines how, when, by whom, and under what conditions AI should be integrated into New York City and its social fabric. AI can play an integral role in how urban residents will inhabit and navigate future cities. This requires that AI designers prioritize their intended users and their needs. To achieve this vision of an urban AI serving the common good, this project presents a complete and self-sustained implementation lifecycle to create a “Citizen AI.” At the center of this process will be the co-creation of a community-based conversational engagement tool (teLLMe) that redefines how, when, by whom, and under what conditions AI should be integrated into our city and its social fabric. This LLM-based system will elevate the principle that responsibly designed urban AI requires modes of technology co-production that bring civic organizations, advocacy groups, small businesses, domain experts, and residents under one umbrella. The team's approach draws on a recent “participatory turn” that goes beyond mere assurances of data security and efforts toward explainability. This co-design sequence will proceed side by side with research on the social dynamics of trusting behavior as well as contributions from engineers and data scientists with expertise in accessibility, data privacy, machine learning, and computer vision to make AI accountable, fair, safe, transparent, and trusted. 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

Traffic congestion is a growing issue in cities around the world. Congestion is costly for travelers, forcing them to sit unproductively in traffic, sometimes for hours. Moreover, excessive car idling results in excess greenhouse gas emissions that contribute to environmental change. Given these social and environmental costs, mitigating traffic congestion is an essential challenge for decisionmakers and scientists alike. Congestion pricing has been demonstrated to be an effective tool for reducing traffic and emissions. However, many cities that have attempted to introduce congestion pricing have failed to do so, often because of strong local voter opposition. This study examines why voters support or oppose this action and how they adjust their beliefs and commuting behaviors in response. This is an important question for the future of green urban transportation decisions. The findings from this research could inform decisionmakers on how to communicate to voters to increase the support for and efficacy of green rules. The study investigates how individuals perceive and integrate information into their decision-making process in response to new rules in the context of traffic congestion mitigation strategies. The study consists of a two-wave experimental survey timed around the introduction of a congestion pricing rule in a large city. The study includes an information intervention that tests whether correcting ex ante misconceptions about congestion pricing influences respondents’ support for the rule. The study also follows respondents after the implementation of the rule to see the effect of information updating on their commuting behavior. Combined, the findings from these experiments advance understanding of how individuals update their beliefs about an issue of public concern and whether an intervention affects their perception of a new rule to remediate the issue, and their behavior. 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 Institute of Electrical and Electronics Engineers (IEEE) Symposium on Foundations of Computer Science (FOCS) is one of the premier annual research conferences that cover the breadth of theoretical computer science. It is a conference of very long standing that has continued to play a formative role in the field; it is also at the leading edge of connections made to other areas. This project aims to increase the impact of this conference on students and postdoctoral researchers, particularly those from under-represented groups, by encouraging and enabling their participation, especially in cases where travel expenses would otherwise preclude their attendance. Concretely, this project assists US-based students and postdoctoral fellows in attending the 2024 Annual FOCS conference, sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing (TCMF). The coming FOCS will take place in Chicago, IL, October 27-30, 2024. As computing becomes ubiquitous, it is crucial to expand the participation of young scholars in cutting-edge research. FOCS has served as one of the most important venues for groundbreaking research in theoretical computer science – and, increasingly, as a key ambassador to other areas within and outside computer science. We anticipate the conference presentations and discussions will expose students and postdoctoral researchers to a broad set of fundamental questions and ideas. We expect the community to benefit in turn, as such junior researchers have contributed substantially to the growth of theoretical computer science over the years. With an emphasis on junior scholars in need, supporting the participation of junior researchers from underrepresented populations in the stimulating exchange of ideas benefits all conference attendees. As the tools and techniques from theoretical computer science (such as novel models, algorithms, impossibility results, and unexpected connections in data science and machine learning) are becoming vital to several domains inside and outside computer science, it is anticipated that such broader participation of junior researchers will benefit society at large. 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

Artificial intelligence (AI) works by learning from patterns in data. Building AI technologies depends on acquiring data for training models. Responsible development of AI as part of public interest technology (PIT) requires building AI that benefits the public interest while safeguarding data used to power AI systems. Safeguarding data require tradeoffs between the level of protection provided and the usefulness of the models created with the data. These tradeoffs create a tension that PIT organizations must resolve. This project engages a multi-disciplinary team across sectors in a combination of ethnographic and computational research to develop novel approaches that can support PIT organizations in deploying data safeguards to build AI. The project uses disclosure limitation techniques to protect the privacy of sensitive information in AI training data. Deploying these techniques, including newer techniques like differential privacy, require making tradeoffs that affect stakeholders in the AI lifecycle. For example, strong privacy protection reduces statistical accuracy, which may ultimately reduce the model usefulness. The project will develop novel methods and best practices for navigating these aspects for PIT organizations. The project will: (1) use ethnographic approaches and qualitative inquiry to identify socio-technical decision points and challenges at PIT organizations; (2) create and evaluate novel approaches to participatory engagement of stakeholders in the deployment process; and (3) build software and communication tools for evaluation and transparency of AI systems that use differential privacy. 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

Many physical simulation applications can be viewed as building “digital twins” of real systems, i.e., computer models that enable studying physical phenomena computationally, avoiding the costs and risks associated with physical experiments. Differentiable simulation allows automation of two critical aspects of digital twin creation and use, improving the quality of the result and democratizing digital twin use: integration of real-world data, and in the case of engineering systems, optimization of system parameters to achieve a particular goal. Examples include identifying realistic material parameters of a patient-specific biomechanical digital twin or discovering the optimal shape of a shoe sole for uniform load distribution. This project will develop open-source software for differentiable simulation for systems involving elastic deformations with contact. These tools will be evaluated in three major application areas: (computational fabrication, biomechanics, and robotics). The deliverables of this project will be open-source software packages accessible to a broad user base. The project plans to utilize dPolyFEM, a modular software framework for design, control, system parameter inference, and learning problems for physical phenomena in material design, biomechanics, and robotics, based on differentiable simulation. The focus is on developing robust, efficient, and scalable software blocks for differentiable simulation that can handle input data satisfying only weak assumptions (e.g., on mesh quality, shape, or boundary conditions) and require no parameter tuning while providing users sufficient control over performance-accuracy trade-offs. The project will support the most common class of physical problems in the target domains: elastodynamic problems involving complex geometry, large deformations, contact, and friction. For scalability, dPolyFEM will provide shared-memory parallelization. This system will consist of several modules that can be used independently or in an integrated way, enabling easy integration of its components into existing general-purpose and domain-specific software. From a technical standpoint, this system will build on three innovations: (1) considering differentiable simulation as a single end-to-end problem including meshing, FE solution, and adjoint formulation, (2) casting the time-integration of physical systems as an energy minimization, for which robust solvers can be developed, and (3) systematically testing the system on large-scale benchmarks The resulting open-source differentiable simulation framework will enable applications in many fields of interest to NSF. The project team includes computer scientists (CISE), applied mathematicians (DMS), and engineers (ENG), and it is expected that the contributions will have an impact on all three communities. Individual modules can and will be integrated into major open-source projects, likely benefitting tens of thousands of users. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil Mechanical and Manufacturing Innovation within the Directorate for 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 · 2024-09

This award will fund a research project to investigate a broad theoretical question: How and to what extent can participatory science counteract the crisis of trust in public health and medical expertise? In recent decades, emergent diseases have caused profound frictions in democratic governance across the Atlantic. Sluggish institutional responses and inadequate treatments have escalated disputes between advocacy groups, patients, medical experts, and scientists in regulatory agencies over the speed, direction, and implications of scientific research. Patient communities in the US and other countries have successfully pushed for more responsive public health policies, and some medical groups have opened themselves to participatory science formats to regain public credibility. Although such cooperatively co-produced expertise holds the potential for counteracting the decline of trust in experts, it is far from clear what formats are best suited to democratize scientific knowledge in ways that do not erode scientific authority and delegitimize expert knowledge. Insights from this project will contribute to deepening and strengthening the dialogue between patients and experts, thereby putting trust in experts on a surer footing. This research will also offer important explanations of how to fortify democratic resilience across the Atlantic in the face of future health crises. This project is a comparative study that seeks to document the dynamics of disease advocacy, contestation, and cross-country collaboration. The researchers will also compare across conditions and diseases by adding “control cases.” This comparative framework will allow us to study the co-production of expertise about emergent diseases through archival methods, participant observations, and semi-structured interviews with key stakeholders. In each case, there is a rich tapestry of factors, some of which are local, contextual, and time-dependent, which determine the social character of the parties involved in inclusionary arrangements and the nature of these arrangements. This project contributes to two areas of research in Sociology as well as the field of Science and Technology Studies: (1) a Sociology of Trust in Experts, which will generate innovative research into what makes expertise credible and trustworthy, or on the contrary, mistrusted; and (2) a Sociology of Contested Illnesses, where we will advance critical insights into the dynamics of activism and knowledge in contested illnesses. One of the key deliverables of this project will be to study modes of inclusion in the case studies and to develop an analytical framework that identifies the relations between the various factors, including (1) the organizational format of inclusion; (2) factors shaping the formation of disease identity; (3) the inherited repertoires available to patients; and (4) the political and legal environment. 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

It is critically important to resolve systemic challenges that have hindered the transfer of fundamental research discoveries at academic labs out to the marketplace to achieve lasting and positive societal benefits. Despite many efforts by public, academic, and private stakeholders, many promising innovations based on Federally funded scientific research fail to become products and services that benefit society due to a variety of reasons. Some of these failure points are inherent to the risks all startups face. However, other failure points relate to process, information sharing, or infrastructure challenges unique to deep tech academic innovations that should be avoidable. The Workship for Enhancing America's Deep Tech Commercialization Pipeline aims to highlight and address failure points specific to university researchers, startups, and spinouts. Commercialization experts will be invited to participate in discussions focused on scoping and prioritizing these challenges and identifying processes, platforms, and partners to include in the solution design process. The workshop's central goal is to better support America’s deep tech commercialization enterprise by identifying missed opportunities, making actionable suggestions for adjusting poorly envisioned components, expanding commercialization programs in relevant adjacencies, and removing roadblocks that limit success. The Workshop for Enhancing America's Deep Tech Commercialization Pipeline aims to establish a robust framework for building solutions to systemic challenges in deep tech commercialization, accelerating the transition of science-based innovations from the lab to the market. Breakout discussions will focus on identifying gaps and formulating new goals, resources, metrics, research, strategies, or other approaches to address systemic challenges or unmet needs. Findings and opportunities will be shared in a report distributed through the Columbia Tech Ventures website. 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

Neutron stars (NS), one end product of massive star evolution, are among the densest objects in the universe. Many NS show evidence of extremely strong magnetic fields that can be generated during their birth or in mergers of NS binaries. A theoretical investigation at Columbia University aims to understand the origin of ultrastrong magnetic fields and the diffusive processes that regulate the onset of turbulence and magnetic field generation. Numerical experiments will be carried out in the relevant physical regime. The proposed systematic revision of dynamo mechanisms can have a significant impact on the field and pave the way for realistic magnetohydrodynamic simulations of collapsing stars and mergers. In addition, public outreach efforts will be undertaken to present the research in a more accessible form to larger audiences, including high-school students at two existing educational programs. The project will involve training of university students and postdoctoral researchers. The research team will investigate mechanisms generating ultra-strong large-scale magnetic fields in magnetars and NS mergers. A turbulent dynamo inside a nascent rotating NS requires an instability to create turbulence, and the canonical candidates include the Tayler instability and convection. The proposed research has two parts: (1) A crucial revision of the linear stability analysis to correctly capture the interplay of rotation and microscopic diffusive processes (in particular, viscosity and thermal conductivity due to neutrino diffusion). Preliminary results suggest a new picture of marginally stable proto-neutron stars, potentially explaining two classes: ordinary pulsars and magnetars. A similar analysis of mergers will give necessary conditions for a successful dynamo in the merger remnants. (2) State-of-the-art simulations of nonlinear turbulence triggered in nascent remnants. The simulations will model a rotating, stratified, magnetized fluid sphere using a pseudo-spectral code. They will allow direct control of diffusivities and implementation of the correct physical regime of turbulence development. The numerical tools will also be used to investigate a very different origin of magnetic fields: the chiral magnetic effect. 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

Understanding the impact of place-based innovations on socio-economic aspects is crucial for accurately measuring recent adaptation and mitigation efforts. Current methods and tools used to address the impact of such activities on communities are still not properly capturing the nuances associated with biases towards specific ethnic and racial communities or simply including socio-vulnerable populations. This project is focused on assessing the impact of the U.S. National Science Foundation's Regional Innovation Engines (NSF Engines) in New York and Louisiana. We will utilize advanced ML tools to analyze and quantify these impacts, ultimately applying, for the first time to our knowledge, AI tools to such socio-economic problems related to climate change and creating scalable models that can be applied to other regions and areas. ML tools can be used to discover patterns and relationships among datasets and build new inference models that can connect changes among variables. In the case of this specific project, the project will use ML to discover relationships among socio-economic, climate and environmental datasets and model such relationships with a specific emphasis in identifying biases of historical models on socially-vulnerable populations. However, to counterbalance the potential “black-box” effects of ML-based approaches, the project will make use of Explanatory Artificial Intelligence (XAI). XAI tools help characterize the accuracy, transparency, fairness, and outcomes of AI-powered decision-making. Specifically, the project will make use of an XAI technique based on Shapley coefficients, which quantifies the relative role of each predictor on the model performances. This will allow us not only to understand the drivers of potential changes - eg. due to NSF investements in those areas - but also to better understand the “quality” of the ML outputs that will be required to fulfill basic rules based on the knowledge of the processes under study from a qualitative point of view. Convening workshops with experts will help identify the specific datasets and optimal approaches for creating a database that will unveil the impact of new activities on communities through ML models. 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

Ice sheets lose ice mass through gravity-driven flow to the ocean where ice breaks into icebergs and melts, contributing to global sea level rise. Water commonly found at the base of ice sheets facilitates this process by lubricating the ice-rock interface. The recent discovery of vast, kilometer-thick groundwater reservoirs beneath the Antarctic Ice Sheet thus raises important questions about the potential impact of groundwater on ice flow. It has been hypothesized that groundwater flow to the ice-sheet bed may accelerate ice flow as the ice sheet shrinks in response to global warming. Evaluating this hypothesis is challenging due to poorly understood interactions between water, ice, and rock, but is crucial for anticipating the response of ice sheets and sea level to climate change. Understanding how groundwater responds to a changing ice sheet also has important implications for the heat, chemical elements, and microorganisms it stores and transports. To assess the impact of groundwater processes on ice dynamics, a new idealized modeling framework will be developed, incorporating several novel hydromechanical couplings between ice sheets, subglacial drainage systems, and groundwater aquifers. This framework will enable testing the hypotheses that (1) aquifers decelerate ice mass loss in the absence of a well-developed subglacial drainage system, but that (2) an efficient, channelized drainage system can reduce and even reverse this decelerating effect, and that (3) the impact of these phenomena is most pronounced for steep ice flowing rapidly over thick sedimentary basins and depends in a complex way on aquifer permeability. Existing geodetic, seismic, and other geophysical datasets at well-studied Thwaites Glacier and Whillans Ice Stream will be used to constrain model parameters and investigate the impact of groundwater processes in contrasting glaciologic settings. This work will help rule out or highlight subglacial groundwater as one of the next major challenges for efforts to predict the future of the Antarctic Ice Sheet and sea-level rise on decadal to millennial timescales. The project will contribute to educating the next generation of scientists by supporting an early-career PI and a graduate student, as well as participation in a field and research educational program in Alaska and the production of chapters for an online, open-source, free interactive textbook. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project will investigate conversations between the vast number of persons in our world who speak multiple languages and who frequently interleave those languages in their speech in what is known as code-switching. It is important to not only be able to identify when, why, and to what effect code-switching occurs but also to correctly interpret what is said and to be able to generate similarly code-switched responses from voice assistants, which can improve their ability to interact with such users. Recent advances in speech technology in recent years have resulted in widespread use of voice assistants such as Siri, Google Assistant, and Alexa. These interfaces enable vast improvement in information access by voice for languages such as English, French, German, Cantonese, Mandarin, and Spanish. However, such access is limited to monolingual speech, which for many multilingual speakers is not the most natural form of speech production. Thus, code-switched speech is rarely understood correctly by voice assistants and is never produced in their responses. To enable efficient and natural communication for these people, it is important to develop speech technology that can not only understand code-switched input but also produce similar human-like output. This project examines how spoken and written code-switching interacts with other paralinguistic aspects of communication to improve code-switched text and speech understanding and production. It will explore research questions not yet studied in code-switching research including (1) whether speakers entrain, speak more similarly, on strategies of code-switching in speech; (2) whether there is a quantifiable relationship between code-switching and empathy in speech, where empathy is a speaker’s intention to convey that they understand another’s problems and want to help address them; (3) whether the presence of named entities, such as names or geographical locations, primes code-switching; (4) which dialogue acts, such as questions or statements or backchannels, tend to be produced most often in code-switched speech; and (5) how speakers produce intonational contours when they code-switch: does their intonation production match either of the languages they are producing or is it different from both? Statistical and machine-learning techniques will be used to address these questions in spoken and lexical features of code-switched speech in Standard American English with Spanish, Mandarin Chinese, and Hindi. The goal is to highlight aspects of code-switching that can be further explored by the research community. 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

Estimates of the magnitude of sea-level rise largely come from computer models that predict ice-sheet behavior. For these models to accurately predict sea-level rise, physical ice-sheet processes must be properly represented. Accurate predictions of ice-sheet behavior and the magnitude of sea-level rise are critical for decision makers. This project will simulate the complete disappearance of the Laurentide Ice Sheet that once covered much of North America. The retreat of the Laurentide Ice Sheet that began about 20,000 years ago presents a natural test case to better understand 1) the physical driving mechanisms that result in the complete disappearance of an ice sheet, and 2) the rate at which an ice sheet disappears. Lessons learned from this research will provide key insights into how large-scale ice-sheet retreat occurs which has clear relevance for predicting the future evolution of Earth’s vulnerable extant ice masses, such as the Greenland and West Antarctic ice sheets. A key question in ice-sheet science is determining how the influence of dynamic mass loss changes through time for a retreating ice sheet and what might control this variability. Lessons from prior episodes of ice-sheet retreat in the geologic record can elucidate how the role of dynamic mass loss changes with time in a fluctuating climate and improve ice-sheet model performance. The researchers will use the next-generation state-of-the-art Ice Sheet System and Sea-level Model (ISSM) to explore the disappearance of the Laurentide Ice Sheet over the last 20,000+ years. In a first-of-its-kind application, ISSM will be used at a spatial resolution capable of capturing large-scale ice-streaming and ice discharge through narrow fjords, along with implementation of coupled solid-Earth-sea-level feedbacks to investigate the role of dynamic ice discharge in driving the disappearance of the Laurentide Ice Sheet. To test model performance, the researchers will compare simulations of Laurentide Ice Sheet retreat against both existing and new geologic benchmarks generated over the course of this project. This project will provide the first quantitative estimates of how the percentage of dynamic mass loss versus surface mass balance evolved over the course of a full deglacial sequence and how this evolution influenced the fate of the Laurentide Ice Sheet. The project will develop a new collaboration with California State University, Long Beach, which is a designated minority serving institution, to recruit students from the Los Angeles area for internships at NASA’s Jet Propulsion Laboratory by leveraging an existing program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project utilizes powerful geometric concepts, known as classical surface theories, to unlock the mysteries of spacetime. This research will deepen our understanding of gravity, black holes, and the very fabric of spacetime. The project's findings can complement experimental data on phenomena like gravitational waves, currently heavily reliant on numerical simulations. Novel methods developed during this research might find applications in other areas of mathematics and physics. The research in this project will also promote interests in mathematics among undergraduate and graduate students and young researchers in the mathematical community. This project leverages the power of classical surface theories, including techniques like isometric embedding and the Gauss map, to investigate complex problems in differential geometry and general relativity. By focusing on the non-linear nature of spacetime, the research aims to:1.Evaluate the quasi-local mass of binary black holes: Develop a more precise method for calculating the combined mass and individual masses of these fascinating objects.2.Define angular momentum in general relativity: Establish a rigorous definition of angular momentum applicable to global solutions of Einstein's equations.3.Prove existence and regularity of a geometric flow: Mathematically demonstrate the existence and well-behaved nature of a specific type of geometric evolution. 4. Demonstrate duality in string theory: Reveal an underlying connection between two seemingly disparate equations within string theory, potentially leading to new avenues of exploration. These advancements promise to significantly contribute to our understanding of the universe and the power of mathematics in unraveling its mysteries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award will enhance national welfare by providing a systematic framework to analyze the efficiency and resilience of demand and supply networks. These systems are challenging to analyze due to the complex interdependencies among firms in the network, which adjust their decision-making processes collectively and strategically in response to both idiosyncratic and systemic shocks. The project will develop novel tools and measures for assessing the impact of risk mitigation plans against supply and demand shocks in the network. The framework will elucidate the mechanisms by which supply shortages of specific goods and services during periods of distress can lead to price spikes and increase the fragility of the supply chain network. For instance, the project will help understand how a global semiconductor shortage can cause significant price surges in the U.S. market for second-hand cars, or how an unexpected surge in demand for hand sanitizers during the pandemic led to widespread supply shortages, impacting the industry and its related sectors. This award will also provide research opportunities for graduate students, equipping them with the tools, background, and expertise to advance research in this area. The project will develop a dynamic decision-making framework to quantify the trade-offs between efficiency and resilience within supply chain networks and provide an empirical analysis of supply chain fragility. This analysis aims to assess how diversification strategies can mitigate risks associated with supply chain vulnerabilities. The research will leverage, extend, and specialize tools from dynamic games, risk management, optimization, and network theory to incorporate the incentives of firms facing information and technological constraints in establishing cost-effective demand-supply relationships and managing risks against supply and demand shocks. The framework will explicitly model both preventive actions taken by firms to hedge against potential future shocks and corrective actions implemented in response to significant disruptions. The project will lead to the development of game-theoretical algorithms for determining optimal firms' levels of investment in production capacity and for final good producers to enter into competitive risk-sharing agreements with intermediate good producers to meet unanticipated demand and hedge against production shocks. The resulting analysis will quantify the conditions under which market-based supply networks are inherently fragile, particularly when these networks prioritize routine operational efficiency over systemic robustness. Additionally, the project will explore whether public institutions can reduce inefficiencies and facilitate outcomes superior to those achieved through decentralized market operations by implementing data-driven control policies. 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

In north Asia, extreme weather events, including unseasonal frost and summer drought, impact the traditional and primary livelihood, nomadic pastoralism, in this region. Existing annual tree-ring based climate reconstructions from the region are of annual temperature variation, and do not capture subannual weather events, so it is not known how the occurrence of these brief temperature extremes is changing with warming climate. The goal of this project is to use wood anatomical traits to develop two 1000-y long records of temperature from Mongolia, and use the records to assess the relationship between volcanic eruptions and cold conditions, and characterize longer-term temperature variability. This project will support building research capacity at William Patterson University, curriculum development, workshops between WPU, Columbia University and National University of Mongolia, and public outreach. The continental climate in north Asia is vulnerable to climate extremes, and recent severe droughts and temperature extremes impact regional communities. The goal of the project is to develop two millennial-length records of wood anatomical traits (cell wall thickness) and anomalies (e.g. blue rings and frost rings) from Siberian Larch (Larix sibierica) and Siberian Pine (Pinus sibirica Du Tour) from temperature-sensitive sites in Mongolia. The tree cores and cross-section samples for this study are already collected, and ring-width data from these samples are already measured. Ring width data do not capture intra-annual climate extremes, while quantitative wood anatomy and anatomical traits can record sub-annual and ephemeral temperature conditions. The quantitative wood anatomy proxy is new and there are few millennial-length records in the literature. The magnitude and timing of temperature extremes will be evaluated, and their frequency through time and connection to volcanism will be assessed. The multidecadal and millennial temperature variability will also be assessed. 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

Observations and data are the foundation of science. Scientists produce new data and build upon previous information as they work to expand the boundaries of human knowledge. But, inefficiencies in data management, access, and integration continue to pose major barriers to both scientific progress and public data availability. Scientific researchers need to ensure that the data which they generate are made openly accessible, well-integrated, and maintained for the long-term. That data also needs to be re-usable for further research, education, and public benefit. Researchers need systems which streamline the currently labor-intensive tasks of data curation and help to make data more accessible to other potential users. In this project, the lead institutions and collaborators will build upon several existing Earth science data systems to create a prototype platform that will enable better curation, integration, sharing, and discovery of Earth science data. This will make it easier for anyone to discover and access in a single location previously fragmented, disconnected, and inaccessible Earth science information. Making the world’s data more readily Findable, Accessible, Interoperable, and Re-usable (FAIR), is a goal shared by governments, scientists, and the public alike. This project will lead to the development of a sustainable Framework for FAIR Data Communities to address the lack of standardized, machine-readable, FAIR compliant data in the Earth sciences that are needed for new computational and data-driven research expected to deliver next-generation discoveries and breakthroughs. Using the global volcanic ash or “tephra” research community as a test case, the project will utilize an inclusive, bottom-up strategy to create the Tephra Information Portal (TIP) as a customizable prototype platform to support specialized research communities and advance the broader adoption of FAIR data practices. The project will build on existing cyberinfrastructure at the IEDA2 data facility (EarthChem, SESAR) and integrate and connect other geoinformatics resources and data, including StraboSpot, GeoDIVA, TephraBase and others. Objectives include: (a) helping researchers select a data repository; (b) ensuring consistent formats and rich metadata; (c) creating a central catalog and integrated critical mass of curated tephra data; (d) serving a single point of discovery, access, and use of distributed datasets; (e) providing protected workspaces with user authentication and management; (f) incorporating disciplinary standards; (g) supporting a next-generation toolkit and data access mechanisms; and (h) responding directly to identified community needs. The tephra research community provides an ideal test case due to its interdisciplinary and multifaceted nature which spans a wide array of scientific and societal interests such as deep Earth processes, volcanic hazards, and global change and which utilizes similarly diverse data types spanning physical properties, stratigraphy, geochemistry, and geochronology among others. This project will leverage a decade of international tephra community-building and consensus-development and the strong engagement and education program of IEDA2. These efforts are designed to engage students and early career geoscientists, broaden participation, and enhance equity and inclusion in the Geosciences through workshops, outreach, training materials, education, and accessible research tools and data. The TIP development process will involve deep collaboration between geoscientists, data scientists, and software developers and will proceed under the guidance of a steering committee constituted as a formal commission of the International Association of Volcanology and Chemistry of the Earth’s Interior. This project is supported by co-funding from the Division of Earth Sciences (EAR) to support EPSCoR jurisdictions, as well as the Education and Postdoctoral Fellowship programs in EAR. 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

To better understand how ice sheets will respond to future warming, scientists have been studying time periods in Earth’s past when temperatures were naturally warmer than they are today. This project focuses on the Last Interglacial, a time period approx. 130,000 to 115,000 years ago, which marks the most recent time in Earth’s history when the Greenland and Antarctic ice sheets were significantly smaller than they are today. The project aims to better understand when and how much these two major ice sheets melted, as well as what climate conditions and ice dynamical processes drove their mass loss. Understanding the factors that drove ice sheet melt in the past will help improve predictions of future ice sheet change. Project goals will be reached through a combination of fieldwork to obtain new estimates of past sea level, laboratory analyses to reconstruct past climate conditions, and modeling to simulate data-informed sea level, ice sheet, and climate histories. Project results will contribute to international efforts that inform policymakers of climate change through the Intergovernmental Panel on Climate Change (IPCC). Fieldwork at multiple locations will connect the project team with local researchers and communities to understand their needs and regional impacts of sea level change. The HISEAS project will use the Earth system model CLIMBER-X coupled with the ice sheet model PISM and sea level model VILMA to simulate ice sheet and sea-level evolution from the penultimate glacial maximum to the end of the Last Interglacial (140 – 115 thousand years ago). The model will be calibrated with a range of existing and new paleoclimatological data. Data products will include (1) a new comprehensive database of terrestrial and sea-surface temperature, iceberg discharge, sea-ice extent, deep ocean circulation, and vegetation data; (2) new paleoclimate records relevant to understanding climate-ice-sheet interactions (e.g., sea surface temperature, iceberg discharge, and sea-ice extent) using existing deep-sea sediment cores as well as fossil corals; and (3) new sea-level records from four locations paired with an existing database of Last Interglacial sea-level proxies. A sea-level fingerprint analysis will complement the Earth system modeling to provide a parallel estimate of ice sheet change during the Last Interglacial. The data-calibrated models will allow the researchers to evaluate the roles of external drivers (e.g., temperatures and precipitation), boundary conditions (e.g., bedrock elevation), and internal ice sheet processes in driving ice sheet change during the Last Interglacial period. 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

Silicon-rich continental crust is unique to Earth and is critical for habitability, but the processes that drive the long-term stability of this crust are unclear. The most enduring tracts of continental crust have resided at the Earth’s surface for billions of years and are characterized by enrichment of uranium (U), thorium (Th) and potassium (K)—the heat-producing elements—in the upper crust. This research project will address the question: what controls the mobility of the heat-producing elements through continental crust? The researchers will measure concentrations of U and Th in rocks and minerals across two temperature profiles in exhumed sections of middle and lower continental crust. Combined with constraints on the pressure-temperature-time evolution of these rocks, they will discriminate between competing mechanisms for the mobilization of the heat-producing elements during melting of the continental crust. The research will catalyze international collaboration between scientists in the US and Switzerland, foster the training of a graduate student, and engage undergraduates in academic research. Characterizing how the heat producing elements are mobilized in continental crust is fundamental to understanding crustal evolution, the temperature and mechanical structure of crust, Earth’s heat budget and chemical differentiation of the planet. Using a suite of complementary techniques, the researchers will test five hypotheses—Equilibrium and Disequilibrium Melting, Mineral Shielding, Melt Buffering and Rejuvenation—for the distribution of the main heat producing elements, U and Th, across two well-characterized temperature profiles: contact aureoles of the Mafic Complex, Ivrea Zone, Italy and the Big Jim plutonic complex, Washington, USA. In-field Gamma Ray Spectrometer measurements will provide bulk-rock U and Th concentrations at a sampling density inaccessible to conventional geochemical techniques. Metamorphic zircon and monazite U/Th-Pb dates + trace-element abundances obtained by laser-ablation split-stream petrochronology will allow assessment of the timescale over which accessory mineral dissolution occurred. Petrologic constraints will be derived from P-T pseudosections, optimal thermobarometry, and trace-element thermometry. In conjunction, the latter two techniques will be used to reconstruct the peak metamorphic conditions and the timing and quantity of melt removal. The complete dataset will allow rigorous testing of the five hypotheses. 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

Marine heatwaves, periods of unusually warm ocean temperatures, significantly threaten marine ecosystems, biodiversity, and human activities. In 2023, extreme marine heatwaves in the North Atlantic and other ocean basins contributed to record-breaking global temperatures from June to September. This project will use observational data, climate models, and artificial intelligence to understand the roles of surface heat, clouds, and ocean currents in causing marine heatwaves. Given the severe impacts of marine heatwaves on ecosystems and coastal economies, this work will have important impacts on climate models and long-term forecasts. The 2023 marine heatwaves were more intense than expected from steady human-induced ocean warming. One hypothesis is that extreme marine heatwaves result from both natural variability and human impact and arise from combinations of atmospheric circulation anomalies, reduced mixing of cool water from below, and anomalous warm advection by ocean currents. This project will test whether the strikingly weak North Atlantic subtropical high in 2023 was critical to driving the extreme heat. This project will use machine learning to integrate observations and model simulations in order to formulate hypotheses of drivers of record-shattering extremes. This will determine if the 2023 events are anomalous or part of a pattern of higher ocean temperatures. The project will involve a postdoctoral researcher and build workforce capacity through a collaboration with Columbia University’s Learning the Earth with Artificial Intelligence and Physics Center. 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 Office of Advanced Cyberinfrastructure 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 · 2024-09

This research project will develop and investigate methods for the estimation of causal effects in randomized experiments. Randomized experiments are used as an empirical method by scientists and researchers in a wide range of fields, both in the public and private sectors. The method is appreciated by researchers because it allows for conclusions that are credible and robust. However, randomized experiments cannot be used to investigate complex settings, such as when study participants interact with each other, because estimation methods and theory are lacking. The project will address this gap by developing new estimators that can be used in complex experiments. The methods to be developed will allow scientists and researchers to investigate new and more intricate questions, ultimately advancing our understanding of both the social and medical sciences. In addition, graduate students will be mentored, and publicly available, open-source software will be developed. A central feature of this research project, which sets it apart from previous work in this context, is the development of a general framework and theory that will encompass most empirical settings in the relevant fields. The framework will cover both settings with interference, including spillover effects and network experiments, and complex experimental designs. This will be achieved by re-interpreting and understanding the empirical problem as a problem within the mathematical subdiscipline of functional analysis. Initial results indicate that the Riesz representation theorem from functional analysis can be used as the basis for a general approach to construct estimators for complex experiments. The project will investigate and develop this approach to a full-fledged estimation procedure and associated statistical theory. In addition to the core framework and theory, the project also will develop variants of the estimators that can accommodate high-dimensional models and adjustments based on background information. Methods for inference and uncertainty characterizations will be developed in the form of variance estimators and central limit theorems, allowing researchers to construct hypothesis tests and confidence intervals to gauge the statistical uncertainty in their investigations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This Major Research Instrumentation award supports Columbia University with the development of a quantum nano-scope (q-SNOM), a unique imaging and spectroscopy instrument with capabilities to measure true quantum coherent properties of the next generation of materials and on-the-chip devices. The most significant novel experiments enabled by the proposed apparatus are the witnessing, verifying, and quantifying of quantum coherence in materials, heterostructures and molecular systems at the native length scales of all these phenomena. The apparatus is designed as a user-friendly instrument and will be made available to research groups within and outside Columbia through established protocols at shared user facilities operated by Columbia Nano Initiative. The q-SNOM project will have multifaceted research and training impacts. The quantum nano-scope will enhance research not only at Columbia but also at several universities in the New York City area extending to Stony Brook University. At the larger national level, the experiments enabled by quantum nano-scope are unique and will address pressing open problems leading to high-impact results in the field of quantum science and technology. Also, the q-SNOM will aid the competitiveness of US companies in the quantum area. Specifically, the project includes industrial partners at Cryogenic Industries, Renaissance Scientific and RHK, government laboratories and in the emerging quantum industry. Finally, the award creates a unique training ground for graduate students and summer undergraduate students within the Columbia-Howard Research Experience for Undergraduates program in a project requiring innovation in optics, quantum metrology, and mechanical solutions. The objective of this MRI project is to develop an entirely novel scanning probe nano-optical apparatus: the quantum scattering near-field optical microscope (q-SNOM), which will enable previously impossible inquiries into quantum correlations in quantum materials at deeply subdiffractional nanometer length scales. The q-SNOM will provide unobstructed access to electron-photon states formed by material excitations and entangled photons. Information on quantum coherence and dephasing effects in materials encoded in the properties of emitted or scattered photons will be readily accessible. Moreover, the q-SNOM offers the means to investigate the properties of quantum emitters and correlated photon waveguides at the nanoscale in the setting of on-chip structures, thus empowering both fundamental and applied advances. Q-SNOM will be a stable research instrument readily manageable by users with minimum experience with conventional SNOMs and broadly accessible as a shared-use tool at CNI-Columbia. The q-SNOM will extend the reach of the quantum optics toolbox to the nanoscale. The immediate impact will be in research in molecular systems, technologically important quantum dots and quantum materials. Once deployed in real applications producing publishable results, q-SNOM is likely to become a standard tool in multidisciplinary quantum research, enabling new experiments in physics, chemistry and engineering with an impact on par with that of its classical predecessor. 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

Surveys of the night sky have discovered classes of objects that are hundreds of times brighter than ordinary stellar explosions. The origins of these events are not understood, because they require a novel source of power. One potential power source is a rapidly spinning neutron star, which emits a magnetized wind of charged particles similar to, but more extreme, than our own Sun’s solar wind. Over a three year program, a team led by the principal investigator at Columbia University will perform computer simulations that predict the radiation emitted from neutron star winds and how it propagates through the debris of an explosion. The calculations will also be applied to spinning neutron stars created from the merger of two neutron stars. These events represent sources of gravitational wave emission whose electromagnetic counterparts will be predicted. The research program will provide training for undergraduate students with technical skills in radiation hydrodynamics and computation. A week-long international workshop will be organized on gravitational wave astrophysics to train the larger community. A growing number of stellar explosions have been discovered with peak luminosities too high to be powered by traditional energy sources, such as radioactive decay. These include the rare class of stellar core collapse known as “superluminous supernovae” (SLSNe), as well as luminous transients with fast evolving light curves, indicative of explosions with lower ejecta masses (“Fast Blue Optical Transients”; FBOTs). The best-studied FBOT, AT2018cow, peaked on a timescale of only a few days and was accompanied by non-thermal emission from radio to X-rays to gamma-rays. Powering the light curves of SLSNe and FBOTs requires prolonged heating of the ejecta by a central energy source, such as an accreting black hole or the rotationally-powered wind of a magnetar with a millisecond spin period. The investigators will create computer models of engine-powered transients that predict the radio, optical, X-ray, and gamma-ray emissions. Synchrotron radio emission from the nebula will be calculated. The predictions of this work will be tested with data obtained with ground and space-based observatories. Their results will also enable predictions for the emission from binary neutron star mergers that might be detected with LIGO/Virgo. Research and educational goals will be integrated in two ways: (1) Undergraduate students are directly involved in key aspects of the proposed research, exposing them to an active and supportive research environment; (2) A week-long international workshop will be organized on time-domain gravitational wave astrophysics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Imagery around the world—from satellites to drones and social media photographs—provide vital information about our planet. There is a unique opportunity in the fields of artificial intelligence and computer vision to understand global and local phenomena from these images, providing insight about climate change, public health, and agriculture. However, the state-of-the-art methods in computer vision are not designed for these applications where decision-making is complex, and accuracy, robustness, and interpretability are required. Existing large-scale AI models, such as ChatGPT, only process individual images on the internet and cannot synthesize conclusions from planet-scale image collections. Even on single images, these models cannot reliably perform sophisticated logical reasoning, and building models to do such reasoning reliably requires unfeasibly large datasets. Creating such large models and datasets is a significant barrier for scientific and societal applications of computer vision, particularly for organizations that do not have the computational resources of large corporations. This project will create a new class of machine learning models, called programmatic foundation models, that have the capability and efficiency to scale to planetary-scale image and video datasets. These models can be queried by experts using natural language, thus empowering scientists and experts to benefit from AI related visual discovery from the vast amounts of visual information available in satellite imagery even if they lack expertise in machine learning. The proposed research has applications across public health, climate change, agriculture, security, and the economy. The research objective of this project is to tightly integrate visual representations and program synthesis together, thereby delivering an accurate, interpretable, and robust machine learning framework for answering questions about what is visible in image collections. Across two research thrusts, the project will drive the creation of these new programmatic foundation models. The first thrust proposes new techniques for building open-world recognition primitives across multiple sensing modalities based on vision-language models, but without any language annotations. It introduces new cross-modal contrastive learning techniques, as well as approaches for reasoning about temporal change. The second thrust proposes new techniques to learn to synthesize programs, incorporating uncertainty, learning from feedback and adaptive computation. Given a query, our proposed framework learns to synthesize a customized program that breaks the task down into constituent steps and control flow that can be directly executed for solving the vision task. To execute each step, the project proposes new methods for training open-world classification, detection and segmentation models for satellite, aerial, and ground imagery. Unlike prior foundation models, this integrated approach has many potential benefits in interpretability, logical soundness, modularity, compositionality, efficiency, and generality to different tasks. The two thrusts taken together combine program synthesis with open-world recognition models for analyzing satellite, drone, and ground imagery around the world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The incidence of extreme heat events has increased in frequency and intensity in the last century as global temperatures have risen, driven by anthropogenic greenhouse gas forcing. When extreme heat occurs at the same time as drought, the impacts are exacerbated. These "hot drought" events have complex consequences for communities across North America, including altered water resource availability and fire regimes, as well as the magnitude of the uptake of carbon dioxide by forests. This project will compile new and previously collected temperature reconstruction data from tree cores from across North America into a "North American Temperature Atlas," which will allow for the analysis of relationship of heat and drought at a range of time and spatial scales. The goals of this project are to make new blue intensity measurements on previously collected tree cores from North America, compile existing blue intensity and maximum latewood density tree ring chronologies from North America, and combine the new and existing datasets together to create the “North American Temperature Atlas” (NATA), a gridded reconstruction of warm season surface air temperature. The NATA will be compared to a gridded North American drought atlas and a gridded North American seasonal precipitation atlas to determine the contribution of temperature to past droughts, evaluate the temperature-drought relationship, and place the modern occurrence of drought in the context of the last several centuries. The Broader Impacts are to create a web interface for public access to the NATA, support for graduate students at University of Tennessee, Knoxville, and University of Idaho, development of outreach to water and natural resource managers, creation of K-12 STEM activities for middle school students, tours of tree ring lab for K-12 students, mentoring high school and undergraduate students underrepresented in STEM on projects related to this work. 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.