Carnegie-Mellon University

NSF Awards · FY 2026 · 2026-09

Millions of people now use artificial intelligence (AI) systems that carry out tasks on their behalf, from writing computer code to navigating websites to managing data. However, when these systems misunderstand what a person wants, correcting them remains surprisingly difficult. Current methods for providing feedback either don’t provide much information to the system, or are so time-consuming that a person might as well complete the task themselves. This project will develop new methods that allow people to communicate corrections and preferences to AI systems more naturally and efficiently. By making it easier for people to guide and improve AI tools, this research has the potential to make these powerful technologies more practical and accessible to a broader population, including those without specialized technical training. This project addresses the challenge of enabling efficient human feedback for AI agents that operate in complex, long-horizon environments such as code editors and web browsers. The research is organized around three thrusts. The first thrust develops models and benchmarks for agents that can interpret blended feedback -- seamless combinations of natural language instructions and direct user actions such as code edits -- and reason about user intent by considering why a person chose one form of feedback over another. The second thrust creates algorithms that allow agents to generalize feedback by inducing reusable functions that users can inspect, debug, and refine; a single correction to a function can then improve the agent's performance across entire classes of future tasks, reducing the need for repetitive supervision. The third thrust designs agents that proactively ask clarifying questions by generating many candidate solutions in parallel, maintaining a probability distribution over them, and selecting queries that maximize expected information gain while minimizing the cost of interaction for the user. Across all three thrusts, evaluation will combine automatic benchmarks with human studies that measure both task success and interaction efficiency. 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

Nucleons (protons and neutrons) make up atomic nuclei and are a common building block of all matter in the universe. Understanding their interactions provides insight into the nature of the universe from subatomic to the cosmological scales. To better understand atomic nuclei, as well as to assist current experiments to find new particle physics and to understand particles known as neutrinos, it is important to carry out calculations of nucleons interacting with each other as well as other particles in nature, such as electrons, muons, and mesons. This project develops and distributes software to carry out these challenging computations on the nation’s most advanced supercomputer systems. The physics of hadron-hadron interactions can be studied using Monte Carlo estimates of path integrals involving quark and gluon fields on a space-time lattice. Baryon-meson and baryon-baryon scattering phase shifts can be computed, yielding important information on hadron structure. Quantities known as form factors which involve the nucleon and the so-called Delta baryon are particularly important since they are crucial to interpreting results obtained in accelerator-based neutrino experiments, such as the Deep Underground Neutrino Experiment (DUNE). New computational techniques have made possible such computations in lattice quantum chromodynamics (LQCD). One goal of this work is to build on past efforts to develop highly optimized software to carry out such calculations on modern Graphics Processing Unit (GPU)-accelerated architectures. In particular, this work focuses on the evaluation of various important correlation functions which involve meson and baryon sources and sinks constructed using software developed in a prior award. The tensor contractions needed in these calculations require program executions having different wall times and numbers of computing processors. Effectively bundling such numerous runs together into a handful of batch jobs on supercomputer systems is crucial in the work flow of these computations. This work will also extend the development of software designed to efficiently carry out this bundling. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program within the Physics Section of the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Adaptive behavior is a defining feature of human intelligence. Whether learning to use a new tool or recovering function after injury, people must detect when their actions no longer achieve desired outcomes and then discover new solutions. This project investigates the cognitive mechanisms that allow humans to adapt their behavior flexibly in changing environments. Rather than viewing adaptation as a slow and automatic process driven solely by trial-and-error correction, the project examines whether people instead test structured hypotheses about environmental change, leading to abrupt “moments of insight” that lead to effective new strategies. Understanding these mechanisms has broad implications for education, rehabilitation, healthy aging, and the development of intelligent technologies capable of flexible adaptation. The project will also advance open science through the public release of data, code, and educational resources, while supporting training opportunities for students in cognitive science and computational neuroscience. This project combines careful behavioral experiments, computational modeling, and unsupervised machine learning to study the cognitive mechanisms underlying deliberate error-based motor learning. Participants will perform visuomotor learning tasks in which sensory feedback is systematically altered, allowing researchers to measure patterns of exploration and strategy formation. The project tests the theory that deliberate error-based motor learning is governed by a structured hypothesis-testing process, characterized by discrete exploration and abrupt “aha” moments, rather than by passive error minimization. The work will generate new theoretical models of human learning and problem solving, provide insight into how flexible behavior changes across the lifespan, and establish scalable experimental tools for studying human intelligence in both controlled laboratory and large-scale online settings. 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

Combinatorics at the Confluence is an International Congress of Mathematicians satellite conference to be held in Pittsburgh, Pennsylvania, on July 20-22, 2026, immediately preceding the ICM in Philadelphia. The meeting will bring together researchers across the breadth of combinatorics, including algebraic, enumerative, extremal, geometric, probabilistic, and topological combinatorics, with the goal of strengthening connections across a field that has grown rapidly and, as a result, has increasingly fragmented into subcommunities. By convening a unified gathering of international experts and early-career scholars, the conference will highlight shared principles, common techniques, and emerging directions at the interfaces with areas such as algebraic geometry, topology, probability, optimization, theoretical computer science, and novel methods from machine learning and artificial intelligence. The conference is designed to broaden participation and amplify impact, with an emphasis on early-career researchers through travel support, wide dissemination of calls for participation, and a graduate-student-focused poster session. The event aims to serve as a model for integrative combinatorics meetings in the United States. The conference is organized in collaboration with the Institute for Computer-Aided Reasoning in Mathematics, and the program will feature a hands-on tutorial on using emerging machine learning and artificial intelligence tools in mathematical research. The website for the conference is at https://combcon.github.io/ 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

High-quality data are increasingly central to modern machine learning and artificial intelligence, enabling advances in scientific discovery, automated decision-making, and emerging AI technologies. Yet there often lack transparent and reliable mechanisms to appropriately credit and compensate those who contribute data used to train AI systems. This project will develop statistical and machine-learning methods for measuring the value of data in AI model training and data-driven decision systems. The work addresses fundamental challenges in data valuation, including robustness to strategic manipulation, computational scalability for large-scale learning systems, and principled uncertainty quantification in assigning value to data contributions. The outcomes of this project will support transparent, fair, and sustainable AI data ecosystems while improving incentives for sharing high-quality and socially beneficial data. The project will also support graduate and undergraduate training, development of educational materials, public dissemination of results, and open-source software for the broader AI and data science communities. The research will develop statistical foundations for scalable and robust Shapley-value-based data valuation in modern machine learning through three integrated directions. First, it will develop priority-aware valuation rules that incorporate precedence relationships and priority weights, enabling originality, provenance, and individual risk considerations to be incorporated within a unified axiomatic framework for AI data attribution. Second, it will study the statistical and computational limits of approximating Shapley values and related semi-values in high-dimensional and large-scale learning settings, with the goal of designing efficient estimation and approximation algorithms for contemporary AI models. Third, it will develop a population-level theory of data value through Shapley density, a continuous analogue of finite-sample data valuation, establish convergence guarantees, and provide methods for accelerated computation and principled uncertainty quantification. Together, these contributions are expected to advance the statistical and algorithmic foundations of AI data valuation, enable scalable and trustworthy assessment of training data contributions in machine learning systems, and support fair and robust data-sharing ecosystems for future AI applications. 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

The placenta is an important organ that allows the exchange of gases and nutrients between a mother and baby. When the placenta does not form or function correctly, it can lead to serious pregnancy complications and health risks for both mother and baby. Yet, despite its importance, studying the placenta during pregnancy remains difficult due to ethical and safety reasons. Studies in animal models provide limited information as the placenta can vary significantly between species. This CAREER project will use computer simulations and machine learning to understand how blood flow controls placental development. The project will study how changes in placental structure affect oxygen delivery to the baby. The project’s outcome will improve understanding of placental function and guide future strategies to improve maternal and fetal health. The project also includes an integrated education and outreach plan. Through a “Learning by Teaching” approach, engineering students will strengthen their technical knowledge and apply core engineering principles to explain the mechanics of pregnancy. Students will also build science communication skills and engage broader audiences in understanding maternal and fetal health through an engineering lens. This project will establishe a novel engineering framework for studying the placenta that integrates experimental data with physics-based modeling and machine learning within a unified multiscale platform. The goal is to advance fundamental understanding of placental development and function in both healthy and pathological states through three complementary computational models that target key aspects of placental physiology. These include (1) characterization and quantification of intervillous space (IVS) hemodynamics, (2) determination of how mechanical cues regulate placental villi development through trophoblast mechanotransduction, and (3) quantification of how placental maldevelopment compromises oxygen delivery to the fetus. A central innovation will be the development of algorithms that infer spatially resolved microscale hemodynamic distributions from macroscale porous media simulations, enabling accurate yet computationally tractable predictions of the placental microstructure. In parallel, a mathematical model of the trophoblast mechanosome will be formulated to describe how mechanical stimuli regulate trophoblast activity. Finally, coupled hemodynamic and transport simulations that rely on a detailed kinetic model for gas exchange will demonstrate how alterations in IVS flow and microstructure impair. Collectively, these efforts will develop multiscale models of the maternal-fetal interface that addresses longstanding gaps in the mechanistic understanding of placental development and function, advancing both novel computational modeling methodologies and placental biology 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 · 2026-06

Extended Reality (XR) technologies, such as Augmented Reality headsets, will transform how people access and interact with digital information and tools. Unlike smartphones or laptops, XR systems present digital content integrated with a person’s physical environment, making information continuously available while allowing people to remain aware of their environment. However, poorly designed XR interfaces can easily become distracting, cluttered, or difficult to use, limiting the usefulness and widespread adoption of XR technology. This project investigates how XR systems can intelligently adapt to a user’s context, including their environment, tasks, and mental state, in order to present information in ways that support attention, safety, and productivity. By developing knowledge and tools that allow XR interfaces to automatically adjust to changing situations, this research aims to make digital information more helpful and less distracting in everyday life. The results will support future applications in areas such as productivity, training, education, and maintenance. This project develops the scientific and technical foundations for adaptive XR user interfaces that dynamically adjust how and where digital content is presented. The research investigates how contextual factors such as physical environment, user activity, and cognitive demands influence effective XR interface design. First, the project analyzes existing XR applications and conducts surveys, expert workshops, and prototype studies to identify promising application domains and important contextual factors. These insights will be synthesized into a design space that characterizes when and how adaptive XR interfaces are beneficial. Second, the project develops algorithms, software libraries, and human-centered evaluation protocols that enable XR systems to adapt interface placement, appearance, and behavior based on contextual information. Third, the project evaluates adaptive XR systems in real world scenarios, including productivity and maintenance tasks, through longitudinal studies that examine usability, performance, and user experience over time. Together, these efforts establish a comprehensive framework for designing, implementing, and evaluating adaptive XR interfaces, advancing research in human computer interaction and enabling the next generation of context-aware computing 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.

NIH Research Projects · FY 2026 · 2026-06

Genome-wide association studies (GWAS) have identified thousands of common, non-coding variants linked to diseases, but the mechanisms through which these variants influence disease remain largely unexplored. Recent advances in functional genomics allow for the measurement of diverse molecular features in finegrained cellular contexts, such as chromatin accessibility, gene expression, and regulatory links at the level of cell types or individual cells. Integrating these data with GWAS offers significant opportunities to advance our understanding of disease etiology and expedite the development of therapeutic treatments. However, current methods of integrating functional genomics data with GWAS often focus on a single data modality, failing to capture the comprehensive insights provided by diverse functional genomics data. Additionally, most existing methods integrate functional data using generic computational algorithms, failing to account for the unique and heterogeneous structures of functional genomics datasets. Thus, there is a crucial need for advanced computational methods that can integrate diverse functional genomics data at scale while accounting for the unique structure of different data types to provide a thorough and nuanced understanding of disease biology. To address these challenges, my group will develop advanced computational methods that integrate diverse functional genomics data at scale, accounting for their distinct characteristics to provide a comprehensive understanding of disease biology. Specifically, we will: (1) leverage multimodal single-cell and spatial data to pinpoint disease-critical cellular contexts with high precision; (2) integrate functional regulatory data and leveraging causal inference to identify disease-critical genes and cell types at individual GWAS loci; and (3) develop graph foundation models capable of massively integrating multi-scale, multi-modal, context-aware functional genomics data, capturing complex biological relationships, and enabling the comprehensive interpretation of GWAS loci. Our methodologies and datasets will be made publicly available and thoroughly documented, ensuring broad applicability across heritable diseases and traits. By providing scalable, interpretable, and generalizable tools, this work will bridge the gap between genetic associations and disease biology, accelerating the development of therapeutic strategies.

NSF Awards · FY 2026 · 2026-06

This award supports research on three-dimensional (3D) chip design methods that advance national prosperity by enabling more capable and energy-efficient computing systems. Modern software applications, such as Artificial Intelligence (AI) large-language models, require orders-of-magnitude improvements in performance and energy efficiency beyond what traditional transistor scaling can deliver, and a growing share of energy is wasted shuttling data between processors and memory rather than performing useful computation. 3D chip integration can overcome this challenge by co-locating computation and memory within a single chip footprint. However, it introduces a combinatorial design landscape with tightly coupled tradeoffs across devices, interconnects, packaging, thermal management, and overall hardware architecture. This project will develop a hierarchical System-Technology Co-Optimization (STCO) framework powered by fast and accurate virtual models of devices, circuits, packaging, and applications. These models will enable rapid exploration of 3D chip design alternatives and, uniquely, will derive required 3D chip technology targets directly from application-level performance needs. The outcome will be new design methods, open-source design tools, and hardware prototypes that enable more energy-efficient 3D chip computing systems, strengthening U.S. leadership in advanced semiconductor technologies and workforce development. Education activities will integrate 3D chip design modules into hands-on fabrication and advanced chip-design courses, establish technician training in semiconductor manufacturing, and engage local K-12 students through interactive outreach programs. The technical goal is a hierarchical system-technology co-optimization (STCO) toolchain that jointly explores device choices, packaging, and chip architecture for 3D integrated circuits. The project will (1) build calibrated models for logic and memory device technologies used in 3D stacks by combining physics-based and data-driven modeling, calibrated with experimental measurements where available; (2) model and optimize 3D packaging, including high-density vertical wiring networks and thermal structures that improve heat spreading between stacked layers; and (3) integrate these device and packaging models with workload and architecture descriptions using hierarchical intermediate representations that enable fast design-space exploration. The STCO toolchain will map applications onto candidate 3D chip architectures and estimate power and performance in minutes, and will also derive target technology parameters, such as vertical connection density and transistor drive current, from application-level performance objectives using symbolic modeling and constrained optimization. The framework will be validated with prototype test chips fabricated using a foundry-based monolithic 3D process in which chip layers are built sequentially on the same wafer. Results will be disseminated through open-source tools, benchmarks, and course 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 · 2026-06

This grant provides funding to organize and execute the 2026 National Science Foundation (NSF) Foundational Research in Robotics (FRR) - National Robotics Initiative (NRI) Annual Meeting in Pittsburgh, Pennsylvania, 25-26 September 2026. This meeting will convene investigators of active awards of the NSF FRR and NRI Programs for the eleventh time since the NRI Program began in 2011 and the FRR program began in 2020. This meeting serves as a conference bringing together a community of robotics researchers whose work is specifically relevant to the FRR and NRI programs. The agenda includes talks from selected projects, poster sessions, workshops, panel discussions, multiple keynote talks, an outreach event, and Federal agency program updates from leaders in the research community. This meeting will also support activities to help aspiring robotics researchers write successful proposals to the FRR program. The annual FRR/NRI Program Meeting is a national event for the NSF FRR and NRI research communities, and serves as an annual forum for investigators to meet and share their research and best practices, discuss new research opportunities, explore new ideas and partnerships, and interact with Federal agency representatives, industry, and other stakeholders interested in NSF robotics research. This award will support organization of the FRR-NRI Annual Meeting, to promote dissemination of ideas, and to encourage collaboration between robotics researchers. NSF program meetings have played a major role in community-building across a broad range of robotics topics, sectors, and technologies, and services to bring together researchers with an interest in learning about the program and participating as future investigators, transition partners, or sponsors. Invitees to the meeting will include, among others, all PIs with active NRI/FRR grants. 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

Modern life depends on the ability to solve large and complicated problems involving networks, decisions, and hidden patterns in data. Examples include moving goods efficiently through supply chains, routing information across communication systems, and identifying meaningful structure in large collections of data. This project develops new mathematical tools for solving such problems more effectively. One part of the work focuses on improving methods for planning and optimization in networks, with possible long-term relevance for logistics, telecommunications, and artificial intelligence. Another part asks when weak or indirect patterns in complex data can still reveal useful underlying structure, a question that matters for the foundations of computing and data analysis. The project also supports education and workforce development through course design, student mentoring, workshops, summer schools, and outreach activities that help bring advanced ideas in mathematics and computer science to a broader group of learners. At a technical level, the project studies how combinatorial and analytic techniques can be brought together to make progress on open problems in algorithms and complexity theory. On the optimization side, the research develops new interior point method frameworks for minimum-cost flow and related graph problems, with the goals of lowering iteration complexity, extending nearly linear-time methods to broader graph settings, and designing dynamic data structures for tasks such as incremental and decremental shortest paths and cycle detection. These dynamic tools may also lead to faster algorithms for static problems, including matching and flow problems. On the analytic side, the project investigates correlations in high-dimensional functions and constraint satisfaction problems, aiming to prove new inverse theorems for larger-arity settings and to apply them in areas such as property testing, approximation, multiplayer games, communication complexity, and additive combinatorics. A further direction studies combinatorial patterns such as longer combinatorial lines, seeking stronger bounds through new structural and higher-order analytic methods. 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

Bridges-3 is the continuation of PSC’s long standing role in providing production quality national cyberinfrastructure through the NSF Advanced Computing Systems & Services (ACSS) program. As the successor to Bridges 2, Bridges-3 carries forward the program’s core objective of delivering systems and services that adapt to rapid evolution in computing and data technologies while remaining reliable, accessible, and broadly usable by the U.S. research community. Bridges-3 offers a balanced, converged environment that integrates advanced GPU accelerated computing, high performance, large-memory CPUs, a hardened all flash parallel file system, and a high bandwidth InfiniBand fabric, providing a cohesive, scalable platform for simulation, artificial intelligence, data analytics, and complex high-performance computing workflows. The overall architecture is informed by science drivers drawn from the critical scientific domains listed in memorandum M-25-34/NSTM-2 from the Office of Science and Technology Policy, which outlines priorities for national investment and actions in Fiscal Year 2027, including artificial intelligence, quantum information science, materials and microelectronics, biomedical research, advanced manufacturing, and energy systems. Bridges-3 will continue to integrate with science gateways, classroom instruction, and community outreach programs to ensure that advanced computing remains accessible and useful across communities. These efforts align with national goals of supporting gold-standard science, building the science and technology (S&T) workforce of the future, expanding world-class research infrastructure, and strengthening America’s S&T ecosystem. Combined with seamless interoperability with national platforms and resources such as the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) capability, the National AI Research Resource (NAIRR), and the Leadership-Class Computing Facility (LCCF), these elements ensure that Bridges-3 will provide stable and productive service to the U.S. research community while supporting the broad goals of the NSF strategic plan. -- 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 is funded through the NSF Translation to Practice (TTP) program, which supports efforts to turn research discoveries into practical tools that benefit communities, industry, and society. Despite record spending on research and development (R&D), breakthrough discoveries are becoming rarer and harder to achieve. A key reason is that experts often get stuck thinking within their own field, missing solutions that already exist in other domains. History shows that some of the most transformative innovations come from borrowing ideas across fields. Today, these kinds of creative leaps happen mostly by luck, leaving organizations trapped in incremental improvements and rising R&D costs with diminishing returns. With this TTP project, an artificial intelligence (AI) platform will be developed that makes cross-domain discovery systematic and repeatable, giving R&D teams in industries such as automotive, consumer goods, and advanced manufacturing a way to generate more novel ideas, create stronger intellectual property, and move promising concepts to market faster. By increasing the effectiveness of existing R&D investments, the platform directly strengthens competitiveness of the United States in critical technology sectors. The platform will be tested and refined through structured pilots with corporate R&D partners, with the goal of launching a self-sustaining commercial enterprise. Beyond its commercial potential, the project trains the next generation of innovators by integrating the platform into university courses in design, business, and human-computer interaction, and it aims to make powerful innovation tools accessible to startups, nonprofits, and academic labs. This project creates an end-to-end, human–AI collaborative engine for solving complex research and development problems at scale by mining evidence-backed analogies from assorted domains. The system uses a novel agentic AI workflow to dynamically abstract a problem into its core structural components, searches millions of multimodal sources across the open web for analogous scenarios that address similar underlying challenges, and translates those discoveries into concrete, ranked "idea sparks" that are evaluated for novelty, impact, and feasibility. Unlike standard large language model prompting, which tends to produce repetitive and homogeneous ideas and rely on surface similarity, this approach optimizes for novelty and variety by finding and transferring underlying structural mechanisms across multiple domains. A key technical contribution is bridging the gap from raw inspiration to actionable concepts through automated de-risking, where multi-agent workflows synthesize evidence from patents, scientific literature, and market data to evaluate feasibility and surface trade-offs. The project also advances human-AI interaction by developing interactive tools that enable domain experts to navigate thousands of candidate solutions and steer the AI's analysis. The platform will be validated through structured pilots with industry partners, user studies with professional research and development professionals, and innovation challenges, measuring impact via expert-rated novelty, value, and feasibility of generated concepts alongside usability and creativity support metrics. 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

NONTECHNICAL SUMMARY Superconductivity — the ability of certain materials to conduct electricity with zero resistance — was one of the landmark discoveries of twentieth-century physics. It revolutionized technology, enabling powerful magnets for particle accelerators and MRI machines, maglev trains, and today serves as a promising foundation for quantum computing. Superconductivity also deepened our understanding of nature by revealing a remarkable quantum state visible on macroscopic scales. Yet, even after decades of research, many superconductors defy conventional explanations. They appear and persist under conditions where standard theory predicts they should not exist, leaving the mechanisms behind them largely unknown. This project seeks to uncover how superconductivity emerges in these unconventional systems — specifically those with very low electron density and strong electron-electron repulsion, both normally hostile to superconductivity. By studying representative materials where unusual behavior is observed, the PI will identify plausible microscopic mechanisms, evaluate them against experimental data, and propose new measurements to distinguish between competing scenarios. This effort will clarify how exotic superconducting states form, what makes them unique, and how they might be engineered or controlled. Ultimately, the results will guide the search for new superconducting phases and advance their potential use in future technologies. The educational component aims to broaden participation in science and strengthen the future quantum workforce. The PI will expand undergraduate involvement in theoretical physics research and spark scientific interest among high-school students and educators in the Pittsburgh region. Outreach activities — conducted through the Sigma Xi honorary research society and in collaboration with Carnegie Mellon’s Eberly Center and Leonard Gelfand Center — will include public lectures, science fairs, teacher-facing workshops, and hands-on research engagement events. A key objective is to lower the barriers that students often face when entering theoretical physics. To this end, the PI will promote research opportunities and organize networking events for Carnegie Mellon undergraduates, helping them build the skills and connections needed to pursue careers in physics and quantum science. TECHNICAL SUMMARY Many experimentally confirmed superconductors fall outside the scope of conventional Bardeen-Cooper-Schrieffer theory. Particularly intriguing are systems with low carrier density, strong Coulomb repulsion, and non-trivial band topology, where superconductivity emerges despite conditions that should strongly disfavor pairing. The main challenge is to identify the microscopic mechanisms responsible for pairing in these regimes. Although numerous theoretical proposals exist, they often lack definitive, experimentally distinguishable predictions. This project will address this gap by developing and analyzing candidate pairing mechanisms in low-density and strongly interacting superconductors, and by formulating measurable signatures that discriminate among competing theories. The research will explore how strong electron-electron interactions promote or suppress superconductivity, determine the regimes in which pairing survives at vanishing carrier density, and establish routes toward realizing topological superconducting states. To differentiate pairing scenarios, the PI will compute experimentally accessible observables — including penetration depth, specific heat, upper critical field, and collective modes — as functions of temperature, magnetic field, and carrier density. These results will enable ruling out incompatible mechanisms and identifying those consistent with experimental trends. By comparing theoretical predictions with existing data and proposing new targeted probes, the project aims to reveal the microscopic origin of pairing in representative materials. The outcomes will advance the understanding of unconventional superconductivity, guide the discovery of new superconducting phases, and clarify connections to other correlated systems, possibly including high-temperature superconductors. The educational component aims to broaden participation in science and strengthen the future quantum workforce. The PI will expand undergraduate involvement in theoretical physics research and spark scientific interest among high-school students and educators in the Pittsburgh region. Outreach activities — conducted through the Sigma Xi honorary research society and in collaboration with Carnegie Mellon’s Eberly Center and Leonard Gelfand Center — will include public lectures, science fairs, teacher-facing workshops, and hands-on research engagement events. A key objective is to lower the barriers that students often face when entering theoretical physics. To this end, the PI will promote research opportunities and organize networking events for Carnegie Mellon undergraduates, helping them build the skills and connections needed to pursue careers in physics and quantum science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

This award supports participation of undergraduate and graduate students from US-based institutions in the IEEE International Conference on Computational Photography (ICCP) 2026, to be held as an in-person event between July 11–25, 2026 at Princeton University, in Princeton, NJ. Now in its eighteenth iteration, ICCP is the premier annual conference dedicated to computational photography, and supports a research community across optics, image and signal processing, computer vision and graphics, and sensors and electronics. The ICCP 2026 program includes keynote and invited talks, paper presentations, poster and demo sessions, and networking events. This program creates rich opportunities for cross-pollination of ideas across research and application areas and provides both junior and senior researchers with an environment where they can engage in brainstorming and mentoring discourse. This award facilitates the increased participation of students in ICCP 2026, including participation from undergraduate students, students from research areas that are adjacent to ICCP but traditionally do not attend the conference, and students that lack resources to attend research conferences. The award provides travel grants for 20 US-based students, selected through a competitive process: Applications are solicited broadly across the US, and travel grant recipients are selected by a committee of ICCP 2026 organizers. Each travel grant partially covers travel, lodging, and registration costs. Recipient students will attend all elements of the ICCP 2026 program (paper presentations, keynote talks, invited talks, poster and demo sessions, networking events); present their research work as a poster; and participate in a mentoring session with faculty and industry professionals providing research and career development advice. Additionally, recipient students will attend the second ICCP summer school on computational imaging, where they will attend courses offered by world-class instructors on active research topics in computational imaging. Participation in ICCP 2026 will support the students’ career development, providing them with networking opportunities and equipping them with research skills. This increased student participation will additionally benefit computational photography and computing research and will contribute towards next-generation workforce development in the United States, by encouraging recipient students to pursue STEM careers. The aggregate effect will be an increase in the number of active researchers and educators in STEM fields, helping accelerate the research and development of technologies with broad beneficial societal impacts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This grant will provide partial travel support to about 12 U.S.-based students to attend the 37th Eurographics Symposium on Rendering (EGSR 2026), to be heldJuly 1–3, 2026 at the University of Bordeaux, in Bordeaux, France. EGSR is the premier annual symposium dedicated to rendering research and is organized with the vision of fostering a community of researchers that all converge to this field from traditionally distinct disciplines, including computer graphics, vision, machine learning, physics-based simulation, visual perception, applied mathematics and physics. This proposed program facilitates the participation in EGSR 2026 of undergraduate and graduate students from research labs that traditionally do not attend EGSR but work on closely-related problems; and students that would not otherwise have the resources to attend. The program include students presenting novel research ideas to the wider rendering community. Dissemination of these ideas can have downstream effects including future publications, internship and academic opportunities, and advancing the state-of-the-art in the field. Research topics include physically-based rendering, real-time rendering, appearance modeling, appearance acquisition, global illumination, neural rendering, stochastic estimation, visual perception, image synthesis, and generative methods. The EGSR 2026 conference chair and program chairs will serve as the committee to select students awarded travel grants. To avoid conflicts of interest, students advised by members of this committee will be ineligible. The selection will prioritize: (1) students that lack funds to attend; (2) students in research labs that traditionally do not visit EGSR but work on closely related problems. Within each category, the selection committee will prioritize students that have not previously attended EGSR and will additionally consider the academic merit of the students. The committee will document its deliberations and the considerations determining their outcome. This program will also benefit society by encouraging the invited students to pursue research and STEM careers and equipping them with research skills and networking opportunities for success in graduate school and beyond. 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

This award supports the participation of U.S.-based students in the Ninth Conference on Machine Learning and Systems (MLSys 2026), to be held in Seattle, Washington, from May 18 to 22, 2026. As artificial intelligence systems become increasingly central to technology, healthcare, scientific discovery, and national competitiveness, there is a growing need for researchers who understand both the algorithms behind machine learning and the computer systems that make these algorithms work at scale. This project addresses that need by enabling students, especially those from institutions with limited travel funding, to attend a leading conference where academic researchers and industry practitioners come together to share new ideas and best practices. The conference features keynote talks, paper presentations, and a Young Professionals Symposium designed to connect students with mentors and career opportunities. By broadening access to these experiences, the project helps cultivate a diverse, well-trained workforce prepared to advance the nation's capabilities in artificial intelligence and related fields. The project provides approximately 30 travel grants of roughly $1,000 each to U.S.-based students attending MLSys 2026, partially covering airfare, lodging, food, and registration costs. A selection committee of conference organizers will review applications, which consist of a student statement, a curriculum vitae, and an advisor recommendation letter. Priority will be given to students who have papers accepted at the conference, those participating in the Young Professionals Symposium, and those for whom the grant would make attendance possible that would not otherwise occur. The selection process also emphasizes recruiting applicants from a range of institutions, including smaller and less well-resourced programs, to ensure broad representation. 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

Supply chains are being stressed by shocks that can halt production, raise prices, and slow innovation. Companies often respond with approaches that are slow and expensive, and that can leave systems brittle when disruptions arrive early or cascade across tiers. This EArly-concept Grant for Exploratory Research (EAGER) project develops a practical alternative: maintaining large sets of partially-developed product designs alongside partially-qualified suppliers so organizations can pivot faster when something breaks. The work strengthens domestic manufacturing resilience and in doing so supports US competitiveness. The benefits of this work have the potential to impact sectors like automotive, aerospace and electronics. This work will also produce reusable software tools that help engineers and decision-makers evaluate resilience options transparently. The research specifically establishes an optimization-based framework that treats design portfolios and supplier readiness as first-class decision variables that are optimized simultaneously. The team builds mathematical models that balance profitability with two forms of flexibility: (1) keeping multiple product alternatives at different validation levels and (2) enabling supply chain pivots that activate alternate suppliers and substitute designs when disruptions occur. The project uses robust optimization methodologies to compare plans that cannot adapt versus plans that can, quantifying the value of deferred decisions and discrete pivots under plausible disruption scenarios. Methods are evaluated on realistic combinatorial design-and-supply-chain benchmarks (including ground-vehicle-style design spaces), tracking outcomes such as profit, adaptation cost, and time-to-recover/time-to-survive. Expected results include validated formulations, scalable solution strategies, and evidence-based guidance on when and how portfolios of designs and suppliers measurably improve resilience. 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

This award supports travel for undergraduate, master’s, and doctoral student researchers from US institutions to attend the 36th International Conference on Automated Planning and Scheduling (ICAPS) and to participate in the Doctoral Consortium or the LaunchPad Workshop held in conjunction with the conference. ICAPS is the premier international conference on automated planning and scheduling, a field that develops methods for intelligent decision making in dynamic environments and supports applications in transportation, logistics, robotics, remote sensing, and related areas. The 36th ICAPS will take place in Dublin, Ireland, from June 27 to July 2, 2026, and the Doctoral Consortium and LaunchPad Workshop will be held on the first day of the conference. The Doctoral Consortium and the LaunchPad Workshop play important roles in strengthening the next generation of scientists and practitioners in AI and automated planning and scheduling. The Doctoral Consortium enables doctoral students to present their research, interact with peers, and receive in-depth feedback from senior researchers. The LaunchPad Workshop helps undergraduate and master’s students enter the field by connecting them with planning and scheduling research, potential advisors, and future research opportunities. Students will also benefit from the broader conference program, including technical sessions, workshops, tutorials, and other educational activities. Together, these experiences help students build research skills, develop professional connections, and engage with cutting-edge advances in the field of automated planning and scheduling. 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

Reliable water supplies in agricultural regions are critical for long-term water and food security. A strategy called "managed aquifer recharge" stores extra surface water in aquifers. During wet periods, farmlands can be flooded to replenish groundwater. This water can later be used during dry periods or drought. This approach increases water supplies, but it can also affect water quality. Soils naturally contain toxic metals such as arsenic and uranium. When soil conditions change during flooding, these metals can move into groundwater. This CAREER project will study how managed aquifer recharge affects the movement of these metals in soils and water. The project will combine field sampling, laboratory experiments, spectroscopy, and transport modeling to identify fundamental processes the control the redistribution of toxic metals. Project outcomes will aid the development of water management strategies that increase water storage while protecting groundwater quality for crops and communities. This CAREER project will evaluate how flood managed aquifer recharge promotes fine-scale redox heterogeneity in agricultural soils that ultimately governs the redistribution and speciation of naturally occurring metal(loid)s in soil systems. The research will investigate how flooding scheme, soil type, and irrigation-water composition influence metal release to porewater, transport to groundwater, and bioavailability for plant uptake. Controlled laboratory experiments will be combined with reactive transport modeling to quantify the development and persistence of microscale redox heterogeneity by simulating managed aquifer recharge in agricultural soils. The work will address fundamental gaps in understanding how hydrologic perturbations induced by managed aquifer recharge alter redox structure and metal behavior in complex agricultural systems. Although this work will focus on the context of flood managed aquifer recharge, findings will be broadly applicable to agricultural soil systems experiencing cyclic flooding and intensification of the hydrologic cycle. Educational and outreach activities will be integrated with the research to translate findings into workforce development and professional training opportunities related to water management. 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

As artificial intelligence becomes ubiquitous in our daily lives, there is a critical need to cost-effectively deploy AI models to diverse environments, such as data centers, smartphones, and web browsers. This project aims to bring a new AI/ML compiler toolchain to help accelerate and automate AI deployments. The project’s novelties are: i) a unified way to represent optimizations in AI models, ii) bringing AI agents together with domain expertise to optimize deployment, and iii) end-to-end solutions for automated model deployment. The project's impacts are three-fold. First, it makes AI/ML models more accessible in a broad set of environments, from the cloud to local devices. Second, it helps improve the overall AI/ML tools and toolchain ecosystem by providing automated solutions to optimize the toolchains that power applications like ChatGPT, Claude, and Gemini. Finally, it helps reduce the effort required to establish AI/ML toolchains for the latest hardware through AI-based automation. This project addresses the challenges that span multiple levels of the ML stack—from adjusting modeling choices and high-level execution strategies—to implementing low-level optimizations across diverse hardware architectures. The investigator proposes to build a unified program representation that encapsulates high-level computational and data encodings, as well as low-level optimizations. An AI-driven ML compiler agent then interacts with structured tools to iteratively optimize the program. This project accelerates the deployment of advanced machine learning models across a broad spectrum of platforms, enabling rapid innovation and supporting the next wave of AI applications and toolchains. 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 project will support a United States–Korea Forum on nanotechnology, to be held September 14 & 15, 2026, in Raleigh, North Carolina, at a time when rapid growth in data generation and processing is driving urgent demand for more energy-efficient computing systems. As emerging technologies converge across quantum, digital, and biological domains, there is a critical need to develop new approaches that enable low-power, high-performance sensing and information processing. Neuromorphic systems offer the ability to process complex sensory data in real time with reduced energy consumption, while quantum technologies open new possibilities for advanced sensing and computation. This Forum will provide a platform for researchers, engineers, and stakeholders from both countries to address these challenges and explore transformative solutions. By fostering collaboration among academia, industry, and government, the activity will advance innovation in priority areas identified by both nations, including semiconductors, artificial intelligence, quantum science, and neuromorphic engineering. The Forum will also promote knowledge exchange, support workforce development, and strengthen international partnerships, contributing to technological leadership, economic growth, and societal benefit. The project will convene leading experts in nanotechnology, sensing, semiconductors, artificial intelligence, and quantum and neuromorphic systems to define research directions and identify opportunities for collaboration in energy-efficient sensing and computing. The research team will organize focused sessions, invited presentations, and structured discussions designed to integrate advances in materials, devices, and system architectures. Emphasis will be placed on the co-design of hardware and algorithms for low-power operation, as well as on approaches that leverage quantum effects and brain-inspired computation. The activity will build on prior United States–Korea forums by strengthening connections among research communities and funding agencies, and by facilitating the exchange of technical knowledge. Expected outcomes include the identification of key scientific challenges, the development of collaborative research agendas, and the establishment of sustained partnerships that advance the frontiers of nanotechnology and energy-efficient information processing 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.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Read mapping is the central problem of genomic sequence analysis. The problem is to efficiently and correctly align millions of sequencing reads (fragments) to a set of relatively unchanging reference genome sequences. While conceptually a straightforward problem, the challenge comes from the scale of the problem and the frequent need to solve it. The quest to solve it quickly has given rise to new data structures, new algorithms, and highly efficient implementations that are foundational to academic and industry research efforts and to genomic analysis in general. However, while advances in mapping have led to significant reductions in computational resource requirements, it remains one of the most costly steps of most analysis pipelines. Hence, additional improvements in speed and memory are needed. Further, as new technologies and new use cases arise, new methods for mapping must be developed. But this poses a significant challenge: while there are many interesting algorithmic ideas to pursue to improve read mapping, practically testing these ideas requires significant software engineering effort unrelated to the core new ideas. Hence, innovation in read mapping is slower than it needs to be. We will develop a modular read mapper that will serve as a research framework and testbed for innovations in read mapping. This system will be built on a new modular architecture that allows for easily swapping new techniques for various subcomponents. This will be augmented with a flexible build system that allows researchers to distribute new mappers in a lightweight manner without forking or duplicating popular mappers. We will use these module definitions to implement a complete, modern, high-performance modular mapper to serve as a research and innovation platform for read mapping. This mapper will be implemented using literate programming techniques, and we will extend literate programming tools and validate the paradigm for its applicability for creating reproducible bioinformatics software. Using this framework, we will explore many ideas for improving read mapping. These include new deep-learning- based sketching and seeding schemes, new full-text indices, new BAM storage and compression approaches, network-based distributed read mapping, and hyper-parameter and modular selection optimization. These will lead to better mappers that are applicable in new settings and to new approaches for creating extensible research software creation. The proposed project will catalyze greater innovations in read mapping far beyond a single research group. It will improve the tools, processes, and insight in how to structure large-scale, high performance bioinformatics software, and it will itself result in the implementation and validation of various new algorithmic ideas in mapping.

NIH Research Projects · FY 2026 · 2026-04

ABSTRACT ___________ Somatostatin-expressing (SST) GABAergic neurons are a key component of neocortical circuits, implicated in sensory perception, plasticity, and disease. Understanding SST function has been hampered by the diverse transcriptional and electrophysiological profiles of this inhibitory neuronal class, with between 10 and nearly 40 different proposed subtypes. We have identified a molecularly-defined subset of SST neurons localized to superficial layers of mouse sensory cortex that show response plasticity when sensory stimuli predict reward outcome, but not when stimuli and rewards are uncoupled. This critical insight provides a foothold to bridge the local network properties of specific subtypes of SST neurons with brain-scale signals involved in reinforcement learning. Here we will define the circuit properties of this SST subtype and how they change during whisker- dependent sensory association learning, taking advantage of cell-type and pathway-specific analysis in acute brain slices. In parallel, we will use longitudinal Ca++ to image these SST neurons during learning, adapting our freely-moving task to a head-fixed training paradigm. In vivo imaging will also enable us to dynamically manipulate stimulus-reward contingencies during learning, to determine how predictive sensory input, expectation, and surprise regulate SST response properties. Finally, based on our preliminary data that implicates neuromodulator activity in SST response plasticity during stimulus-reward pairing, we will test the role of cortical norepinephrine and serotonin in regulating SST activity during learning.

NSF Awards · FY 2026 · 2026-04

Discovery in science, machine learning, and artificial intelligence (AI) invariably employs computations with matrices and their higher-dimensional extensions, tensors. Examples include the design of new antibiotics or cancer drugs, the development of quantum computers which can rapidly solve even the harder problems in chemistry and physics, or the construction of novel artificial intelligence architectures which can work more effectively alongside human researchers while reducing energy and hardware costs. These operations typically demand a major part of the computational resources, necessitating the use of fast computers and high-performance software. Convenience and flexibility are also of great importance, as cutting-edge applications often require new functionality and rapid development. The project investigates and delivers a new, adaptable framework for a broad class of matrix and tensor computations, targeting the entire high-performance hardware stack while vertically integrating the software layers. This supports innovation in science and engineering and the application of advanced models to real-world problems. The software is available under open-source license. It is designed to conveniently support existing and future computational tools, while reducing barriers to entry and facilitating the training of the next generation of computational and data scientists. Dense linear algebra software libraries, developed over the past four decades, have had an arguably unparalleled impact on scientific computing and, more recently, machine learning, data science, and AI. While much innovation has happened over this time, the fundamental approach and exported interfaces have changed little. The Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (FAMLIES) project leverages highly successful prior research and development, sponsored by the National Science Foundation and industry, to develop, design, and deploy a new, vertically integrated dense matrix and tensor software stack. The library targets the entire hardware stack, including single and multi-core, GPU-accelerated, and massively parallel compute environments. It is simultaneously backward compatible via its support of widely used interfaces and forward compatible because it is a framework for synthesizing new functionality. The effort builds on decades of experience by the research team turning fundamental research on the systematic derivation of algorithms into practical software for these domains. This project implements key linear algebra and tensor operations, highlighting the flexibility and effectiveness of the new framework. The software is shared via GitHub, allowing contribution from and dissemination to the broader 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.