University of Maryland, College Park

NSF Awards · FY 2026 · 2026-09

Ceramics possess exceptional resistance to heat, wear, radiation and corrosion, yet their widespread adoption is limited because direct manufacturing complex ceramic parts requires extremely high temperatures and often leads to cracking, defects, and long processing times. This Faculty Early Career Development Program (CAREER) award supports research in additive manufacturing (AM) of high-performance ceramics to enable faster, more reliable production of components used in aerospace, nuclear energy, electronics, and biomedical systems. Current AM methods either rely on multi-step processes that are slow and prone to distortion or single-step methods that generate severe cracking and poor material quality. Research enabled by this award seeks to overcome these limitations by developing a new AM approach that enables rapid, defect-resistant fabrication of complex components. By advancing reliable manufacturing of high-performance ceramics, the award is expected to accelerate innovations in energy efficiency, advanced transportation, and resilient infrastructure, strengthening U.S. technological leadership, economic competitiveness, and national security.    This CAREER award aims to establish the scientific foundation for a transformative single-step ceramic AM process based on laser-triggered flash sintering (LTFS). A central challenge is the lack of fundamental understanding of how coupled laser heating and electric-field stimulation initiate flash sintering, govern densification kinetics, and influence microstructure evolution, defect formation, and process reliability. To address this gap, research is planned to develop an integrated experimental, computational, and data-driven framework. Specifically, the research tasks include (1) design and construct an LTFS-enabled AM testbed with in-situ monitoring for real-time process characterization; (2) investigate flash-sintering initiation, stability, and microstructure evolution through coordinated experiments and multiphysics microscale modeling; (3) establish a multiscale electro-thermal-mechanical modeling framework to quantify how manufacturing parameters influence densification, shrinkage, and resulting material properties; (4) develop a Scientific Artificial Intelligence (Sci-AI) framework that integrates in-situ data with physics-based models to capture process stochasticity, improve predictive accuracy, and enable intelligent process control; and (5) demonstrate manufacturing capability through fabrication and evaluation of complex, high-performance ceramic components. The outcomes are expected to establish quantitative process–structure–property relationships for LTFS-based ceramic manufacturing, enabling defect-controlled fabrication of advanced ceramics and advancing smart, data-driven manufacturing of high-performance materials. 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 2026 · 2026-09

This Faculty Early Career Development Program (CAREER) award will advance understanding of how structural collapse influences fire behavior in residential communities, particularly in wildfire-prone regions. At the single-building scale, collapse during fires alters fire behavior by changing compartmentation, ventilation, fuel distribution, flame-spread pathways, and ember generation, with effects pronounced in light-frame wood structures, the most common residential construction in the United States. At the neighborhood scale, collapse can cause direct flame contact by toppling burning structures onto adjacent ones, introduce fuel into the space between buildings, and increase ember flux toward neighboring buildings, collectively leading to cascading fire spread in densely built areas. However, these interactions remain poorly understood and are rarely addressed in existing fire modeling tools. This project will address this critical gap and contribute to safer building design, improved neighborhood planning, and firefighter safety. The project will also integrate research with education by training students and engaging them in hands-on research and design activities, with research outcomes incorporated into coursework and design-based learning. These activities will equip students with practical skills in analyzing fire-induced collapse and designing fire-resilient structures and neighborhoods, strengthening the future workforce in fire safety and structural engineering. By addressing knowledge gaps in fire hazards and developing tools and insights for mitigating them, this project serves the national interest by promoting public safety and community resilience. This project aims to develop a comprehensive understanding and modeling framework for collapse-induced fire dynamics in light-frame wood structures at both the single-building and neighborhood scales. The research integrates experimental and computational approaches. Full-scale and scaled fire experiments on light-frame wood structures will be conducted to quantify how collapse affects fire behavior, including changes in ventilation, compartmentation, fuel distribution, and fire-spread pathways. These data will support the development of a computational framework that dynamically incorporates collapse processes into fire simulations through staged model updates, enabling efficient representation of evolving building conditions without requiring fully coupled fire-thermal-structural modeling. This modeling framework will be extended to simulate fire spread at the neighborhood scale, accounting for the effects of structural collapse on inter-structure fire spread. The developed modeling framework will be validated and used for parametric studies at both the single-structure and neighborhood scales to identify key factors influencing collapse-driven fire hazards. The outcomes of this project will provide new knowledge, modeling capabilities, and design insights to support fire-resilient building design and neighborhood planning in wildfire-prone regions. Insights from this project will also support emergency response by helping firefighters anticipate potential structure collapse, reducing their risk of injury or fatality during operations. 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 2026 · 2026-08

This project supports US-based mathematicians, postdoctoral researchers, and graduate students to participate in an international research event at the Okinawa Institute of Science and Technology (OIST), Okinawa, Japan, August 31–October 30, 2026. The goal of the two-month program is to bring together mathematicians and physicists to investigate fundamental questions about the structure of space, geometric form, and the physical laws governing our universe. The venue will create a sustained and concentrated research environment where insights from one field can inform and accelerate progress in the others. At its core, the program focuses on gauge theory, a theoretical construction originally developed in physics to describe elementary particles and the fundamental forces behind their interactions. Gauge theory has since become a powerful tool in modern mathematics. Using gauge-theoretic ideas, mathematicians can study the properties of curved spaces and uncover deep connections between seemingly disparate areas of mathematics, often suggested by considerations in theoretical physics. In terms of broader impacts, the program places strong emphasis on training and outreach. NSF support will prioritize early-career researchers, including graduate students and postdoctoral fellows, providing them with valuable opportunities for international collaboration and professional development. The program will also feature an autumn school, specialized workshops, and mentoring activities. In addition, public lectures aimed at non-specialist undergraduate audiences will communicate the excitement and significance of modern mathematics and physics to a broader public. Together, these efforts will help cultivate a broad and well-prepared scientific workforce while enhancing public understanding of fundamental research. The focus of the program at OIST will be the following three interconnected research areas: (1) Vafa–Witten invariants in four-dimensional gauge theory and their relation to higher-dimensional gauge theory; (2) Applications of gauge theory to low-dimensional topology; and (3) 3D mirror symmetry and its mathematical foundations. The unifying theme is the study of gauge theoretic moduli spaces arising across geometry, topology, and quantum field theory. These spaces appear in seemingly disparate contexts but share analytic and algebro-geometric features which also admit physical interpretations. Advances in one direction frequently lead to transformative developments in the others. These topics are at the forefront of modern geometry, topology, and mathematical physics. A central goal of the program is to identify and understand common structures that arise across these problems. By fostering close collaboration between mathematicians and physicists, the program aims to accelerate discoveries that advance fundamental knowledge in geometry, topology, and quantum field theory. The program's website is the following: https://sites.google.com/view/new-frontiers-in-gauge-theory/home 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 2026 · 2026-07

Artificial intelligence (AI), large-scale data, connected devices, cloud and high-performance computing systems, that together form the nation's cyberinfrastructure, are central to national competitiveness, yet most computing students encounter them only in advanced elective courses. This project infuses aspects of AI, Big Data, and Parallel and Distributed Computing concepts and practices into three foundational computing courses. The first two form the usual introductory programming sequence, and third is the computer systems course that is often taken shortly after them. Thus, all computing majors, not only those pursuing upper-level elective courses, will develop critical skills for understanding and contributing to the modern computational ecosystem. The project addresses a persistent barrier to such curriculum modernization: many instructors need focused preparation, classroom-tested examples, and adaptable teaching materials before they can confidently introduce these topics in early courses. To overcome this bottleneck, the project develops courses and materials to train about 200 current and future instructors through three intensive in-person summer workshops, an additional six hybrid tutorials at major conferences, and complementary online workshops. Summer trainees adapt and implement the course exemplars at their own institutions and contribute evaluation data, classroom-tested refinement and local adaptation, enabling broader adoption. With the potential to impact about 250,000 students over 5-10 years, the project serves NSF's mission by strengthening computing education, expanding access to AI and advanced cyberinfrastructure skills, and building the nation's long-term technological and research workforce capacity. The project advances knowledge in computing education by producing rigorously classroom-tested exemplars infused with Artificial Intelligence (AI), Big Data (BD), and Parallel & Distributed Computing (PDC) for Computer Science 1 (CS1), Computer Science 2 (CS2), and Computer Systems courses. Implementation across 60 diverse institutions will generate evidence-based models that can be widely adopted, thereby transforming early computing education at scale. The project investigates how AI-enabled learning tools and pedagogy can modernize core curricula by enabling students to construct, explore, and reason about modern computing systems earlier and in more depth than was previously possible. Evaluation data from trainees' implementations - including student learning, retention, and institutional adoptability - will contribute to generalizable knowledge on the design and scaling of Cyberinfrastructure-centric curriculum innovations. The project incorporates aspects of AI, BD, and PDC concepts and practices into the foundational computing courses ensuring that all computing majors, not only those pursuing upper-level electives, develop critical Cyberinfrastructure-ready skills. The project's three core course exemplars will be nationally adoptable, with trainees providing local adaptations to build a community-driven ecosystem of shared materials. Two key innovations in this project are: (i) harnessing AI both for pedagogy and for enabling course modernization: AI tools will make it possible for introductory students to develop, experiment with, and understand software, data, and other artifacts that were previously too complex to explore meaningfully at scale; and (ii) explicit integration of how BD and PDC power AI, giving computing students insight into the Cyberinfrastructure ecosystems underlying AI-driven discovery and innovation. 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 2026 · 2026-07

The natural world is governed by wave equations: the electricity on a circuit board, the light in fiber-optic cables, the elementary particles inside atoms, and even the black hole in the center of the galaxy all propagate by wave dynamics. Though ubiquitous, wave-type equations are far from being well understood. A goal of this project is to understand how waves are affected by interference with themselves or with their environment. The research seeks to learn when and why some waves disperse, other waves persist, and still others collapse. Knowing how waves behave drives technological progress - smaller microchips, faster data transmission, and deeper insights into the fundamental physics of the universe. The project provides research training opportunities for graduate students and postdoctoral researchers. The investigator studies the long-time dynamics of solutions to nonlinear evolution equations, focusing on dispersive and heat type equations that admit topological solitons, which are used to model the physical phenomena described above. Solitons are localized solitary waves with a nontrivial topological invariant. They were introduced by Skyrme in the 1960s as candidates for particles in classical field theories. They have properties required from a particle in classical mechanics - one can define their position, momentum, and energy - and viewed from a distance, configurations of multiple solitons resemble systems of interacting particles. The investigator's work on multi-soliton dynamics has made this connection with classical mechanics explicit, reducing the dynamics of strongly interacting solitons to underlying n-body problems for their positions, momenta, scales, etc. A guiding principle in the analysis of soliton dynamics is the Soliton Resolution Conjecture, which predicts that generic solutions decompose near the final time of existence into a superposition of finitely many solitons and a term capturing the radiation, often a solution to the underlying linear equation. The investigator will work towards proving the conjecture in certain settings, going beyond it in others, and surprisingly, showing that it is false in two instances where it is widely expected to hold. The project focuses on three categories of problems: (1) the kink stability problem for the classical phi-4 equation on the line; (2) construction of explicit counterexamples to the soliton resolution conjecture for the harmonic map heat flow and the sphere-valued wave maps equation, both in two dimensions and without symmetry assumptions; and (3) the unique continuation problem for singular nonlinear waves past the blow-up time. 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 2026 · 2026-07

Nontechnical Description Two-dimensional (2D) materials are atomically thin sheets with atoms strongly bonded within each layer but weakly bonded between layers. They have unique versatility as their electronic properties can be tuned by changing the composition of a layer or stacking different 2D sheets to form heterostructures. 2D materials have shown promise for emergent technologies such as photonics and quantum computing. This project focuses on dihalides, a class of 2D materials consisting of stacked layers of transition metal ions sandwiched between halogen atoms such as such as chlorine or iodine. The dihalides being studied in this project feature coherent oscillations of electric polarization called ferrons. These oscillations occur at terahertz frequencies, which are important for imaging, communications, and ultrafast electronics. Despite their technological potential, the properties of ferrons have received little experimental attention. One challenge with such studies is the small lateral dimensions of 2D materials, which can be smaller than the millimeter wavelength of light at terahertz frequencies. This project develops near-field techniques with resolution far beyond what is possible with conventional optical studies to investigate ferrons in dihalides. The results will enhance the understanding of ferroelectricity in 2D materials and inform the development of nanophotonics and quantum electronics. The project supports a workshop to provide training in scientific communication. Such skills are needed to present scientific results from emerging research to people from all backgrounds. The workshop enhances education of scientific communication, which is central to training the future workforce and ensure U.S. leadership in quantum science. Technical Description This project investigates how ferroelectric polarization oscillations influence the nanophotonic and optoelectronic properties of van der Waals dihalides. In this project, the principal investigator develops and uses methods for near-field spectroscopy to investigate the properties of van der Waals dihalides with around ten nanometer spatial resolution at terahertz frequencies. These advances broaden the set of tools available to study resonant terahertz oscillations in van der Waals materials, which include two-dimensional materials reaching thicknesses of only a few atomic layers. Time-resolved imaging enables direct visualization of wave propagation at sub-diffraction-limited length scales in these materials. Within this project, the principal investigator uses nano-terahertz spectroscopy to study van der Waals dihalides and resolve outstanding questions on non-intuitive properties, including wave propagation below the diffraction limit. The principal investigator also develops innovative nano-terahertz methods and investigates non-linear responses of van der Waals dihalides to terahertz radiation. By studying dynamic properties of van der Waals dihalides in a deeply subwavelength operational regime, this effort deepens the understanding of how ferroelectric polarization oscillations influence optoelectronic properties of dihalides and informs the development of devices including subwavelength optical modulators. 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 2026 · 2026-06

Chaotic properties of smooth dynamical systems is a very active area of research with many open directions and applications in physics and geometry. This project develops a general framework for studying ergodic and statistical properties of such systems. One of the main motivating questions for the project is: to what extent can a deterministic dynamical system resemble a sequence of independent coin tosses. The main tools for studying this question come from ergodic theory, probability and geometry. The work of the project will result in progress in our understanding of fundamental dynamical phenomena with possible consequences and applications in other areas of mathematics, such as geometry and number theory, and also in physics and economics. The project provides research training opportunities for graduate and undergraduate students and postdoctoral researchers. The project is part of an ongoing program of studying ergodic and statistical properties of smooth systems on manifolds and their interactions with geometry and number theory. The Principal Investigator (PI) focuses on the following three main directions: 1. Chaotic properties for systems with non-zero exponents. Ergodic and statistical properties of smooth systems are quite well understood in the case where all Lyapunov exponents are non-zero (hyperbolic systems). On the other hand, ergodic theory of systems for which some (but not all) exponents are zero is much less understood. The PI studies appearance and interactions of ergodic and statistical properties for smooth dynamical systems; 2. Slow chaos for entropy zero systems. The PI plans to investigate quantitative chaotic properties such as rates of mixing, rates of equidistribution, spectral nature and limit theorems for wide classes of zero entropy systems. Even though recent years have seen a lot of progress on these questions, particularly for algebraic (unipotent) examples, there are wide classes of zero entropy systems for which these fundamental properties are not yet well studied; 3. Sparse equidistribution. Recently, there has been a lot of progress on understanding the behavior of orbits of dynamical systems when sampled at sparse subsets of the integers. The PI plans to continue his study of sparse ergodic theorems for wide classes of dynamical systems with emphasis on homogeneous examples where despite the recent progress, many questions are still open. This component of the project will focus on the behavior of orbits along polynomial sequences and also the sequence of k-primes. 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 2026 · 2026-06

Batteryless, energy-harvesting, intermittent computing platforms can be deployed indefinitely in environments that are unsuitable for traditional embedded devices, such as disaster zones, body implants, or even tiny satellites, enabling exciting new applications across bio-medical, disaster monitoring, and defense domains. The major challenge hindering this advancement is that harvested energy is unreliable and causes frequent power failures that introduce new errors and bugs in software running on the platform. Such errors make these platforms unsafe for applications, e.g., in health and defense spaces, where bugs could cause significant harm, either by accident or by a malicious party exploiting the bug. The goal of this project is to build up both theory and practical tools to create an embedded operating system (OS) that is verified (i.e., proven by machine) to execute applications correctly and securely through power failures. The project's novelties are that it defines the first formal model of an intermittence-safe OS and develops new programming languages and verification frameworks for proving that intermittent system implementations satisfy their correctness and security requirements. Such verification tools integrate into AI-based code generation workflows and make writing correct software much easier, as coding agents can write proofs and developers can check that AI-generated code meets requirements. The project's impacts are that it makes intermittent computing platforms trustworthy to run sophisticated safety-critical applications in previously inaccessible deployment environments; that it gives developers tools to write verified intermittent system software, either directly or through AI workflows; and that it supports the education of a future workforce who can create provably well-behaved system software, reducing costly bugs and cyber-attack vectors. To accomplish its objectives, the project pursues four major thrusts. The first thrust develops foundational models of an intermittent OS, including precisely defined abstraction layers that allow delineated tiers of trust and modular verification of components. The second thrust creates domain specific languages for writing intermittent systems code, leveraging ownership type systems to get basic correctness and security assurances "by construction", making it easier for coding agents and non-expert programmers to write intermittent system software. The third thrust develops libraries for the Rocq and Verus verification frameworks that are specifically designed to reason about effects of intermittent execution, making it possible to have correctness proofs that apply directly to intermittent system implementations. Finally, to evaluate the framework, the fourth thrust ports a slice of the TOCK embedded OS to run provably safely intermittently, leveraging the theory, languages, and verification libraries crafted by the first three thrusts. Together, these contributions push the state-of-the-art in both the complexity of the intermittent system that can be formally proven correct and secure, and in the rigor and practicality of proof tooling, bringing safety-critical applications across bio-medical, remote monitoring, and defense domains to new deployment frontiers. 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 2026 · 2026-06

This project explores new ways to make location-based data more trustworthy and useful in everyday life. Location information supports many critical activities, including disaster response, environmental monitoring, infrastructure management, and security. However, most of this data is currently managed through centralized systems that can be vulnerable to failures, manipulation, and limited transparency. This project investigates an alternative approach based on decentralized technologies that allow data to be shared, verified, and managed across distributed networks rather than controlled by a single entity. By bringing together researchers, developers, and stakeholders, the team will design and grow an open, collaborative ecosystem for robust location data. The effort helps ensure that future technologies — including agent-based automated systems that rely on location information — can operate with greater reliability, transparency, and accuracy. While Web 2.0 technologies enabled volunteered geographic information, interactive maps, and cloud services, these advances have also concentrated control in centralized systems, creating vulnerabilities and a need for a more trustworthy and durable geospatial data infrastructure. Meanwhile, decentralized computing technologies, such as blockchains, peer-to-peer networks, consensus mechanisms, digital signatures, and cryptographic protocols offer advantages that address these challenges in ways traditional systems cannot. Their principles emphasize openness, durability, and user-controlled participation. Applied to geospatial systems, these features enable verifiable provenance while remaining compatible with incumbent standards. This project scopes and designs an open-source ecosystem (OSE) for the decentralized geospatial web to consolidate fragmented efforts into a coherent framework anchored by the Location Protocol, a schema for signed spatial data. This POSE Phase I project supports stakeholder engagement, governance refinement, and roadmap preparation enabling the ecosystem to progress towards maturity and long-term sustainability. 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 2026 · 2026-06

Organisms frequently rely upon cell-cell interactions to build various structures throughout their body plan. One outstanding example of this is the formation of the cranial trigeminal ganglion, a tissue that consists of neurons arising from both neural crest and placode cells. The central question of this research is to understand how trigeminal ganglion neurons coordinate, in space and time, their initial formation, and extension of axons to target tissues, to detect sensations. To address this question, an innovative, multidisciplinary approach will be taken that spans the single cell to whole embryo. Results from these experiments will shed light on processes underlying trigeminal ganglion neurodevelopment, providing immediate relevance for the identification of fundamental mechanisms controlling intercellular interactions and neuron formation during development and the maintenance of adult tissues. The research produced through this project will have broad impacts on our understanding of normal neurodevelopment, offering insight into how abnormalities underlie deficits and promoting scientific progress and national health. It will also provide preK-12 and undergraduate STEM education and teacher training activities through outreach to high school students, undergraduates, and teachers, along with giving experiential research opportunities to undergraduate and graduate students. As such, this project advances NSF’s priorities in Biotechnology by developing an American STEM workforce that is globally competitive in STEM education and biotechnology. The objective of this proposal is to uncover mechanisms by which sensory neurons of the cranial trigeminal ganglion extend axons to their target tissues using the chick as a model system. The trigeminal ganglion is generated by interactions between two key precursors, placode cells and neural crest cells, with the former differentiating into neurons before the latter. While the dual cellular origin of the trigeminal ganglion has been known for years, the molecular mechanisms mediating trigeminal ganglion development remain obscure. The Taneyhill lab’s publications have begun to bridge this knowledge gap by defining molecules involved in initial trigeminal ganglion assembly and later axon outgrowth, including N-cadherin (N-cad), which is expressed by all trigeminal ganglion neurons regardless of cellular origin. Their results show integrin-extracellular matrix (ECM) and N-cad-N-cad interactions respectively orchestrate axon outgrowth of initial placode cell-derived “pioneer” neurons and later “follower” neurons arising from placode and neural crest cells. The lab also discovered that N-cad physically interacts with TrkA, a cell surface receptor in neural crest-derived trigeminal ganglion neurons that is crucial for neuron survival. Further, recent findings point to a novel role for N-cad in mediating TrkA glycosylation and transit inside the cell. Building on these results, the aims of the proposed research are to 1) define N-cad-independent mechanisms underlying integrin-ECM interactions that drive trigeminal ganglion pioneer axon extension; 2) determine the function of N-cad in controlling TrkA glycosylation and trafficking; and 3) characterize, for the first time, the trigeminal ganglion glycoproteome. These aims will be achieved using biochemistry; live imaging; molecular perturbation assays; glycoproteomics; and immunohistochemistry coupled with high-resolution confocal microscopy. 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 2026 · 2026-06

This project supports a symposium on closing critical strategy gaps for the future of artificial intelligence (AI) infrastructure. As AI becomes increasingly important to national competitiveness, economic growth, and scientific discovery, continued progress depends not only on advances in algorithms but also on the systems that support their development and deployment. These systems include computing hardware, energy and water resources, thermal management, policy frameworks, and workforce preparation. The symposium convenes experts from academia, industry, and government to examine these interconnected challenges and to identify pathways toward a more resilient, efficient, and sustainable AI ecosystem. Through keynote talks, panel discussions, and breakout sessions, the symposium creates a forum for cross-sector dialogue on the infrastructure needs of the AI era. The symposium also includes a strong educational component by examining the knowledge, skills, and perspectives needed for graduate students and early-career professionals to thrive in a rapidly changing technical landscape. By engaging researchers, technologists, policy experts, and students, the activity broadens participation in conversations that are often separated by disciplinary and institutional boundaries. A summary report disseminates the major findings and provides guidance for future research, education, policy, and industrial practice. The focus of this symposium is the widening gap between rapid advances in AI and machine learning and the physical and regulatory infrastructure required to support reliable, scalable, and sustainable deployment. The symposium studies this problem by integrating perspectives on advanced computing hardware, high-performance system design, data center resource demands, and regulatory constraints that shape technology development and use. Specific topics include advanced silicon and packaging, graphics processing units and other accelerators, power delivery, water use, and thermal management, as well as export controls and related policy considerations. The symposium also examines the transition from centralized cloud computing to edge and embodied AI, where intelligent processing moves to local devices and autonomous systems with stringent requirements for safety, reliability, and energy efficiency. The scope of the research activity centers on defining the most important infrastructure challenges for the next era of AI and clarifying how software, hardware, environmental constraints, and public policy interact. The symposium uses invited presentations and structured discussion to identify key research questions, engineering tradeoffs, and collaborative opportunities across sectors. By bringing together complementary expertise from multiple communities, the symposium develops a holistic research agenda and produces a roadmap for future investments in AI infrastructure and workforce development. 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 2026 · 2026-06

As cloud computing, artificial intelligence infrastructure, and internet services continue to grow, it becomes increasingly important to monitor the large networked systems that support communication, commerce, education, health, and science. These systems include massive collections of servers, network devices, storage services, and software components that must work together reliably and efficiently. However, the data generated about network traffic, resource usage, and failures can be too large to analyze in full, especially when operators need answers in real time. This project develops an approximation-first approach to telemetry for hyperscale networked systems, using compact, informative data summaries to answer important monitoring questions quickly while greatly reducing cost and overhead. The project establishes an end-to-end approximation-first telemetry architecture for hyperscale networks through four research thrusts. The first develops mergeable summaries that can be created on end hosts and networked devices while tracking uncertainty. The second develops low-latency aggregation and query methods that answer telemetry questions directly from these summaries. The third develops learning-guided compression for long-term telemetry storage using both lossy and lossless approaches. The fourth creates a management engine that maps user goals for accuracy, responsiveness, and cost into efficient telemetry configurations. Together, these thrusts advance telemetry systems, networked systems, and large-scale distributed computing. The project's cost-effective telemetry can help operators detect network anomalies, bottlenecks, failures, and attacks more quickly while lowering the compute, storage, and energy required for monitoring. The project will also create educational materials and hands-on learning opportunities in networking, cloud computing, and systems, and will release open-source software to support researchers, students, and practitioners building new analytics tools. Project software, documentation, and research artifacts will be released through a project repository (frootlab.cs.umd.edu/projectasap) in University of Maryland hosted web resources. These materials may include software, publications, experimental artifacts, and selected datasets or benchmarks. Public resources will be maintained for at least five years after the project ends or after data release to support reproducibility, reuse, and follow-on research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Tensor contraction sequences are foundational computations in quantum chemistry, quantum physics, and materials science. As these workloads grow in scale and complexity, scientists increasingly rely on optimization techniques — mixed precision, reordering, and fusion — to reduce runtime and memory costs. While effective, these optimizations can introduce subtle numerical errors that are difficult to detect and that erode confidence in scientific results. The project's novelties are a scalable formal verification framework for optimized tensor contraction sequences and a principled method for connecting correctness guarantees directly with performance optimization. The project's impacts are more reliable and reproducible scientific computing, open-source tooling for the broader high-performance computing (HPC) and formal methods communities, and interdisciplinary training opportunities that bridge formal verification, numerical analysis, and high-performance computing. This project develops a scalable, sound, and precision-aware formal verification framework for reasoning about optimized tensor contraction sequences under interacting optimization strategies. The project creates automated modeling and property-checking methods for mixed-precision tensor contraction sequences using domain-specific satisfiability modulo theories (SMT) encodings in quantifier-free floating point (QF_FP), automated formula generation, and counterexample-guided abstraction refinement. It extends verification to contraction-order optimization and fusion through incremental SMT solving with solver-state reuse, portfolio-based solving, and equality-saturation methods that preserve tight numerical bounds. The project further integrates verification into multi-objective design space exploration, using interaction-aware analysis and graph-based performance–error optimization to identify Pareto-optimal implementations across FLOP count, memory usage, and verified numerical error. The expected result is a practical methodology for discovering tensor contraction implementations that are both high-performance and provably correct for mission-critical scientific workloads. 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 2026 · 2026-05

The Security, Privacy, and Trust in Cyberspace (SaTC) program, a flagship initiative by the National Science Foundation (NSF), addresses critical cybersecurity challenges from a socio-technical perspective. By delving into deep scientific and engineering issues and considering human behaviors, SaTC aims to advance the field of cybersecurity and privacy. Given the escalating national significance of cybersecurity, effective communication between program officers, researchers, and government funding agencies becomes paramount. A robust SaTC community will drive innovation, identify novel research avenues, prevent duplication, and enhance graduate education opportunities. This project encompasses the 2026 SaTC PI meeting venue and conference logistics, including registration, audio-visual support, communications, and meeting space in College Park, Maryland. The planning date for the conference is August 6-7, 2026. The 2026 SaTC Principal Investigator (PI) meeting will help as follows. 1) Stimulating research ideas: By bringing together PIs working on different projects, the meeting encourages cross-pollination of ideas. Discussions, workshops, collaborative sessions, and networking opportunities foster creativity and may spark novel research directions. 2) Exploring new opportunities: PIs can explore interdisciplinary collaborations beyond their immediate domains. Interactions between researchers from different disciplines can yield new insights, foster synergies, and prevent redundancy in existing research efforts. 3) Transitioning Research into Practice: Sharing experiences and learning from others helps PIs refine their research approaches. Practical insights gained during the meeting contribute to more effective research outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

The use of light-activated gold nanoparticles for thermal ablation of cancerous tissue and localized thermally-activated drug and gene delivery systems has been extensively investigated. However, these therapeutic approaches have stalled at the preclinical stage because the nanoparticle temperature cannot be controlled precisely, which leads to effects on tissue away from the therapeutic target. The conversion of light to heat inside the nanoparticles is efficient and fast, which means the key parameter determining overall therapeutic efficiency is the thermal energy dissipation across the interface between the gold nanoparticles and the surrounding medium, i.e., biological fluid. Therefore, the main goal of this project is to broaden our understanding of heat transfer across solid-liquid interfaces, which is a highly complex problem that involves surface chemistry, interfacial liquid properties, and energy carrier physics. The broader activities of this project will include the creation of a podcast for societal outreach, and several educational activities, including a course on the research tools used by the investigators. The goal of this project is to engineer a methodology for the spatiotemporal temperature control of solvated gold nanoparticles by focusing on the interfacial dissipation of thermal energy. The research plan is driven by the hypothesis that interfacial liquid properties and structuring determine the solid-liquid interfacial thermal conductance, which is the missing link between existing theory and experiments. This project will address this knowledge gap through a combination of unique experimental and computational efforts including: (i) spectroscopy techniques for probing the interfacial liquid properties; (ii) interface-sensitive laser pump-probe metrology for accurate thermal boundary conductance measurements; (iii) reactive force field development for capturing thermally-sensitive chemistry; and (iv) multiscale (atomistic and continuum) modeling of heat transfer in solvated nanoparticle systems. Thiolated gold surfaces and nanoparticles will be considered for thermotherapy and drug delivery systems based on Diels-Alder chemistry. Primary objectives are the creation of a comprehensive computational tool, based on the reactive force field (ReaxFF) able to capture thermally-sensitive chemistry and interfacial liquid properties as measured by spectroscopy; experimentally observing for the first time the computationally predicted relationship between adsorbed liquid ordering and solid-liquid conductance; and using these findings to create continuum models capable of incorporating the granularity of atomistic scale parameters and laser irradiation to formulate temperature control strategies. The successful execution of this project will bring significant advances in the fields of heat transfer, biomedical engineering, surface science, and potential cancer therapeutics. 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 2026 · 2026-04

To bolster America's leadership in quantum technology and its applications, this workshop convenes a meeting of leading scholars, researchers, and practitioners in academia, industry, government labs, foundries, and federal agencies to support the growth of a national quantum photonics translation and commercialization ecosystem. Quantum Integrated Photonics technology offers a compelling route toward scalable quantum computing, networking, and sensing for diverse applications in energy, finance, healthcare, transportation, and others, by leveraging (1) photons as robust carriers of quantum information and (2) silicon and related foundries as a mature, high-volume manufacturing platform. Despite rapid progress in laboratory demonstrations, translation of photonic quantum systems into deployable technologies has been hindered by the lack of scalable, silicon-compatible sources of quantum light and the absence of standardized pathways for integrating quantum devices into commercial semiconductor foundries. This project supports a two-day national workshop that addresses challenges and opportunities in translating and commercializing robust scalable quantum integrated photonics systems, as well as develop a strategic roadmap for their manufacturing, packaging, and testing using semiconductor foundries and infrastructure. Coordination across various sectors will help accelerate and maintain US leadership in quantum and related technologies, expand economic prosperity, and advance national security. 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 2026 · 2026-04

This project will advance the understanding of causes and implications of recent extreme sea ice variability in the Antarctic through development of a research and logistical partnership with New Zealand. We focus on the Ross Sea as an area of strategic interest for the US and New Zealand, a major locus of recent variability, and as a key area of significance to global ocean circulation and intact ecosystem food webs, motivating the establishment of the Ross Sea Marine Protected Area (MPA). Understanding drivers of sea ice variability and its implications for this large and remote region requires integration across a range of approaches. This pilot study will integrate deployment and testing of advanced observing technology, modelling, and satellite remote sensing to assess capabilities and strategies for a broader integrated program to understand the drivers and implications of the recent rapid sea ice decline in the Ross Sea. This program seeks to advance capability in key areas, building a strategic collaboration between the United States Antarctic Program and the New Zealand Antarctic Research Program and other international partners, in alignment with the “Antarctica InSync” initiative, supporting coordinated, sustainable research in one of the world’s most logistically challenging environments. This will foster increased collaboration and shared logistics support, and further enhance US leadership in the Antarctic. Insights from this work will help improve predictions of how the Southern Ocean and sea ice both respond to and influence global environmental change. Antarctic sea ice extent has exhibited extreme recent variability, with a modest long term increase culminating in 2015, followed by a dramatic decline in 2016 and record lows in both summer and winter in 2023, although with significant variability over the past decade. These changes in sea ice extent are likely closely related to changes in thickness. The causes of this recent variability and its implications have been identified as a key theme for the international research effort “Antarctica InSync”. This collaborative RAPID project will (1) evaluate advanced and emerging technology that can contribute to an observational network capable of capturing key processes across the Ross Sea, (2) improve and evaluate both satellite and model products with in situ observations, and (3) develop a combined modelling, satellite, and in situ observational strategy to understand these processes. This is centered on capability development through evaluation of techniques in the McMurdo region, leveraging existing programs and logistics. This capability can then be exploited in future projects through widespread deployment of in situ observations, integrated with a refined modelling and satellite observation strategy to address the complex coupled role of various atmosphere-ice-ocean processes in driving sea ice variability. 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 2026 · 2026-04

This project provides support for a workshop on the topic of Quantum Biotechnology. To bridge the gap from innovation to impact, the workshop will discuss the latest advances in quantum technologies and determine the needs of the biomedical community for improved sensing, reporting, control, and analysis and how biotech can fulfil these needs. The conference will also identify the most pressing needs in national workforce development in quantum information science and engineering, a field identified as critical to U.S. technological leadership. The target for attendance is 200 scientific leaders in the field from around the US with participation also from Europe and Australia. The NSF funds will go to bringing the leaders in the field together to identify the most promising opportunities and urgent needs for quantum technology to impact biotechnology. The goal of the workshop is to highlight the current state of the art in quantum sensing, quantum reporters, quantum control, and quantum analysis of complex, large scale biological data and to identify opportunities in technology development. 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 2026 · 2026-03

Dr. Hong-Zhou Ye of the University of Maryland, College Park is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Section to develop new theoretical methods for accurately and efficiently simulating chemical reactions catalyzed by transition metal surfaces. Understanding surface reactions at the atomic level is essential for designing improved catalysts with higher energy efficiency, better chemical selectivity, and improved synthesizability, which have critical impacts on the energy, technology, and manufacturing sectors of the U.S. and global economies. However, existing computational tools often face a trade-off between accuracy and efficiency, limiting their ability to model the large and complex systems relevant to realistic catalytic processes. Dr. Ye and his research group will address this challenge by integrating advances in quantum chemistry theory and software developments to create a new generation of computational tools capable of reliably predicting catalytic reaction mechanisms and energetics. These methods will be implemented in the open-source software package PySCF to ensure broad access. In parallel with the research effort, Dr. Ye and his team will pursue educational and outreach activities aimed at strengthening quantitative and scientific computing skills across multiple educational levels, helping to cultivate the next generation of the STEM workforce. These activities will include hands-on instructional modules and summer research experiences for local high school students, as well as an annual computational chemistry workshop for undergraduate and graduate students at the University of Maryland. The project is directed toward developing a Gaussian-based local correlation framework for metallic solids to enable accurate and efficient simulations of transition-metal-based heterogeneous catalysis using correlated wavefunction theories such as the coupled-cluster theory, thereby complementing the prevailing plane-wave-based density functional theory paradigm in computational materials science. Dr. Ye and his team will address two key technical barriers limiting the scalability and accuracy of coupled-cluster methods for transition-metal surfaces by developing (i) a linear-scaling local coupled-cluster theory suitable for metallic systems and (ii) correlation-consistent Gaussian basis sets specifically tailored for transition-metal solids to improve accuracy and numerical stability. The resulting methods and basis sets will be rigorously benchmarked against experimental surface reference data and applied to investigate the reaction mechanisms underlying the industrial Fischer–Tropsch process for synthetic fuel production. Through open-source release of the developed tools and basis sets, this project will lower the barrier to high-level electronic-structure calculations for a broad range of materials systems beyond catalysis, thereby amplifying their impact by enabling the development of systematically improvable approximate theories and fostering synergy with modern data-driven and machine-learning approaches. 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 2026 · 2026-03

Small particles in the atmosphere, known as aerosol, play a significant role in cloud and precipitation development. It has been shown that clouds that have a significant connection to the lowest layer of the atmosphere are more affected by aerosol than clouds that are disconnected. The research in this award will determine how well that theory holds over different land surface types, such as urban, forested, and coastal regions. The national importance of this work is related to improving understanding and numerical modeling of clouds and precipitation. Multiple early-career researchers will be trained, fostering the next generation of scientists. The goal of this award is to improve understanding of aerosol-cloud interactions, and more specifically, their relationship to cloud-surface coupling. The research team will use data from ground, airborne, and space sensors, combined with machine learning and numerical simulations, to address the primary scientific question: How does cloud-surface coupling influence aerosol-cloud interactions under varying atmospheric conditions and surface types? 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 2026 · 2026-02

Particle accelerators are powerful tools used in medicine, manufacturing, and fundamental science, but conventional accelerator facilities are large, expensive, and limited in number. This award advances a promising alternative: laser-driven particle accelerators that can achieve extremely high energies over much shorter distances by using plasma as the accelerating medium. The award supports the extension of this type of accelerator to unprecedented power and length scales on the highest power laser in the United States while leveraging machine learning in the design and optimization of the experiments. The upcoming experimental campaign at the NSF ZEUS laser user facility aims to uncover new physics and set new performance benchmarks for laser-driven accelerators. Beyond advancing fundamental knowledge, this work supports the long-term development of compact particle accelerators with potential applications in medical imaging and therapy, advanced radiation sources, and materials processing. The project will train graduate students and early-career scientists in cutting-edge experimental science at a major national facility, providing valuable experience in interdisciplinary research, data analysis, and scientific communication through publications and conference presentations. This RAPID award supports upcoming experiments at the NSF ZEUS multi-petawatt laser facility at the University of Michigan. These experiments aim to demonstrate >1 meter high-power laser guiding in plasma, investigate relativistic laser-plasma interaction at new, extreme scales, and acceleration of electrons to energies beyond 20 GeV. The award will support the development and characterization of specialized hardware for these experiments, including >1 meter supersonic gas jets with longitudinal density profiles, specialized optics for generating tailored plasma channels, and diagnostics for both the guiding and acceleration processes. Preparatory work will be carried out at the University of Maryland and will include novel use of artificial intelligence / machine learning techniques to optimize the channel design. 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 2026 · 2026-02

The theory of von Neumann algebras, originating in the 1930's as a mathematical foundation for quantum physics, has since evolved into a beautifully rich subfield of modern functional analysis. Studying the precise structure of von Neumann algebras is rewarding for many reasons, as they appear naturally in diverse areas of modern mathematics such as dynamical systems, ergodic theory, analytic and geometric group theory, continuous model theory, topology, and knot theory. They also continue to be intimately involved in a variety of fields across science and engineering, including quantum physics, quantum computation, cryptography, and algorithmic complexity. The PI will focus on developing a new horizon for research on structural properties of von Neumann algebras, by combining entropy (quantitative) and boundary (qualitative) methods, with applications to various fundamental open questions. This project will also contribute to US workforce development through mentoring of graduate students and early career researchers. In this project, the PI will develop two new research directions in the classification theory of finite von Neumann algebras: applications of Voiculescu's free entropy theory to the structure of free products and of ultrapowers of von Neumann algebras; the small at infinity compactification and structure of von Neumann algebras arising from relatively properly proximal groups. This will involve a delicate study of structure, rigidity and indecomposability properties via innovative interplays between three distinct successful approaches: Voiculescu's free entropy theory, Popa's deformation rigidity theory, Ozawa's theory of small at infinity boundaries and amenable actions. 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 2026 · 2026-02

Wireless communications, which has become an essential tool both for American consumers and the US industry, requires constantly increasing data rates to accommodate growing consumer demands. The US frequency regulator (Federal Communications Commission FCC) is currently making available new spectrum in the frequency range 7-24 GHz, also known as ‘’upper mid-band”, to satisfy this demand. In order to efficiently use this spectrum, it is necessary to deploy adaptive antenna arrays at both base stations and user locations; such adaptive arrays can direct the transmissions into specific directions, thus reducing interference to other users, and extending the range over which wireless links can be sustained. The use of antenna arrays can reduce transmitted power as well. While adaptive antenna arrays have been widely used in the past, modifying them to communicate over the very wide bandwidth available in the upper mid-band presents a huge challenge. Conventional phased-array technology needlessly divides the upper mid-band into three or four sub-bands, each served by a dedicated narrowband antenna array. This project aims to develop a single wideband antenna array to communicate over the entire upper mid-band, in turn requiring a complete rethinking of both antenna structures and antenna adaptation algorithms, in addition to the development of new mathematical tools. A new generation of large ultra-wide-band (UWB) antenna arrays would offer powerful sensing capabilities on top of their communication functions. This research could materially improve the global competitiveness of the United States in wireless technologies. The standard phase-shift techniques that underpin antenna beamforming for narrowband operating conditions is not effective for ultra-wide-bandwidth (UWB) operation. To overcome this limitation, this project will develop “true time delay” (TTD) beamforming methods necessary for deployment of UWB adaptive antenna arrays. The project exploits the fact that TTD beamforming is mathematically equivalent to the Radon transform, which has been extensively applied to computer tomography as well as to wide-band geophysical signal processing. The traditional space/frequency characterization of narrowband beamforming is not valid in the extremely large bandwidth regime of UWB communications. In contrast, the Radon transform accurately describes the mathematics of beamforming in the UWB space/time domain. Aperiodic arrays, which have been explored for both communication and sensing, have received little attention in the context of UWB operation, but are expected to have significant benefits for this operating regime; their optimization will also be analyzed via the Radon transform. To address the problem of UWB impedance matching, the project investigates the radical idea of not doing impedance matching of the antenna to the channel: rather it is proposed to drive the antenna with high impedance current sources during the tramsmit stage and to measure open circuit voltages during the receive stage. The research combines the development of communications theory with the development of experimental prototypes to demonstrate this novel approach to UWB space-time communication systems. 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 2026 · 2026-02

This project focuses on the behavior of physical, chemical, and biological systems that can be modelled by partial differential equations (PDEs). The forces that determine the time-evolution of these systems are complex, making their analysis subtle and technical. Two fundamental questions of interest are the qualitative behavior of these systems, e.g., whether solutions have large fluctuations, and their long-time behavior, e.g., by quantifying the speed with which an invasive species overruns a new environment. These questions are interdependent, with the latter relying on an understanding of the former. Our ability to understand the long-time behavior of PDE, including identifying the key quantities on which each long-time outcome depends, allows us to predict the behavior of real-world systems in a way that cannot be captured purely by numerical simulation, which, by necessity, is restricted to finite time scales. This project will develop novel methods for these goals. Graduate and undergraduate research will be integrated into the project, training the next generation of applied mathematicians and scientists. The project also involves a summer boot camp for entering applied mathematics PhD students transitioning from adjacent, but nonmathematical, fields that shore up their mathematical reasoning (logical thinking) and technical writing skills. Their training is impactful because these students have diverse interests (mathematical biology, machine learning, data science, PDE and numerical analysis, etc.) and go on to careers in industry, academia, and national labs. This project focuses on advances in reaction-diffusion equations and collisional kinetic equations. In the former, the project will develop a novel "Stein's method" approach to PDE that is based on the observation that monotonic steady states of a given PDE satisfy first order autonomous ordinary differential equations (ODE) and that, to show convergence of a generic solution of the PDE to such a steady state, it is enough to show that the generic solution converges to a solution of the ODE. The research will leverage new functional inequalities and ideas in the calculus of variations. In the latter, the project will import techniques from parabolic theory and stochastic analysis to characterize when blow-up occurs in generic domains (both with and without boundaries). This requires the precise and quantitative understanding of the regularity of solutions near the boundary in physical space and the decay of solutions at "large" velocities. 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 2026 · 2026-01

Understanding and predicting wildland fire are critical for effective response, resource allocation, and risk mitigation. However, this remains a major challenge due to the complexity of fire dynamics and the limitations of current data sources and modeling approaches. This project will develop innovative large-scale artificial intelligence (AI) and machine learning (ML) algorithms for the detection and forecasting of wildland fires using multimodal and longitudinal geoscientific data. The research team will address key computational challenges in large-scale geoscientific data mining. The project will develop and validate an advanced AI framework to curate geoscientific data, detect wildland fire, integrate multimodal data sources, enable longitudinal data-based forecasting, and support collaborative geoscientific data analysis and model learning. This project will generate broad societal and educational benefits through outreach, data dissemination, and curriculum development. It will produce an open-source, integrated dataset to support the broader research community in benchmarking and developing new methods. The project will also engage with different stakeholders, including federal, state, and local agencies, such as National Aeronautics and Space Administration (NASA) centers, the U.S. Forest Service, and the National Park Service, to ensure that the developed tools align with operational needs for fire tracking and management. The research objective of this project is to answer urgent needs in wildland fire research by developing a new advanced AI framework for geoscientific data analysis aimed at addressing important computational challenges through several key efforts. First, the team will integrate data from multiple platforms and modalities to create a comprehensive dataset that combines multi-instrument satellite observations from both geostationary (GEO) and low-Earth orbit (LEO) platforms, atmospheric reanalysis data, historical fire records, surface characteristics, and fuels information. Second, an advanced AI framework will be developed via fully utilizing the potentials of multimodal and longitudinal geoscientific datasets for wildland fire detection and forecasting. The proposed advanced AI framework includes: an interpretable multimodal transformer to integrate diverse data and ensure interpretability in fire-related feature extraction; incomplete multimodal learning model to leverage both GEO and LEO satellite observations during training and remain robust when only partial data are available during inference; a temporally structured deep learning model for future wildland fire forecasting; and a federated learning platform for collaborative geoscientific data analysis and AI model learning. The innovative integration of large-scale machine learning and data-intensive computing for heterogeneous geoscientific data mining holds great promise for wildland fire early forecasting. 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.