Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2025 · 2025-06

This project enhances the ability of the Research Data Ecosystem (RDE) to improve access to the Inter-university Consortium for Political and Social Research’s (ICPSR) Comprehensive Data Archive, the world’s largest social science data archive. In this project, ICPSR at the University of Michigan transitions its vast repository of over 20,000 studies and 6 million variables to RDE’s cutting-edge digital platform. This enhances the findability and usability of essential data resources. RDE’s modern technology gives scientists across the fields of science and engineering the ability to efficiently access and analyze different data types, increasing the reproducibility of their research. Such improvements are vital for advancing scientific breakthroughs that can lead to groundbreaking discoveries and for supporting data-driven policy decisions that can bolster national health, prosperity, and welfare and contribute to national security. Moreover, metadata mapping and enrichment analysis align existing data to FAIR standards, ensuring the discoverability and interoperability of various data types, including video and geospatial data, and increasing AI readiness. These invaluable data resources are made more accessible to all Americans, thereby enriching data literacy and strengthening the U.S. data workforce. This project migrates ICPSR's extensive data archives to the modernized Research Data Ecosystem (RDE) software platform, enabling improved data accessibility, usability, and security. Utilizing a robust ETL (Extract, Transform, Load) process, ICPSR's over 20,000 studies and 6 million variables are transitioned from outdated systems using a scalable and robust Data Migration Framework involving backend services and infrastructure for security and storage. Metadata mapping and enrichment analysis align existing data to FAIR standards, ensuring the discoverability and interoperability of various data types, including video and geospatial data. Workflow mapping analysis consolidates complex and disparate workflows from ICPSR’s legacy systems to a streamlined workflow in the modernized RDE platform, delivering a significantly better user experience for depositors, curators, project managers, reviewers, and administrative staff. Through three phases, the migration progressively integrates self-published, high-value public-use, and restricted datasets into the RDE software platform. This enables new and advanced functionalities, including for metadata enhancement, data exploration, and secure cloud-based analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Nanophotonics has become of critical importance in advancing the frontiers of modern science and technologies, including integrated photonics for information technologies, photonic quantum information systems, superresolution imaging, sensing etc. In nanophotonics, light is controlled by nanoscale structures engineered precisely for the desired photonic properties. Traditionally designs of specific nanophotonic devices are obtained through empirical, trial-and-error methods with very limited, high level guidance by physics models and intuition. The advancements in artificial intelligence (AI) techniques open up new opportunities to more efficiently design new and more optimal nanophotonic systems. Yet there are many fundamental challenges at present, such as requirement of large training data sets, domain adaptation issues, and limited generalization capabilities. A promising new approach that may mitigate these limitations is to use generative models, particularly score-based diffusion models. This project aims to develop an innovative deep learning framework that combines physics-informed principles with scientific domain-adapted generative diffusion models to overcome key challenges in scientific inverse design and accelerate scientific discovery. The research will advance the frontiers of artificial intelligence and nanophotonics. Furthermore, the developed methods are potentially generalizable to other scientific disciplines. Educational impacts include enhancing engineering and physics curricula for undergraduate and graduate students. Furthermore, the project will engage high school students in southeast Michigan through outreach initiatives and integrate undergraduate students into research activities. Collaborations with local organizations will further support academic research. The goal of the project is to establish the first model of generalizable, AI-assisted inverse design of complex nanophotonic systems through cross-disciplinary collaboration. Domain-specific generative diffusion models will be developed that efficiently capture photonic structure data priors with limited training data. A novel physics-informed machine learning approach will be integrated to ensure accurate predictions of physical properties and reliable conditional inverse design. A physics-guided posterior sampling method will enforce physical constraints during inference, enhancing the model’s reliability. The framework will be validated by inverse design of advanced topological photonic 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 2025 · 2025-06

This grant will support the participation of early-career US-based researchers at the research program "Modern Perspectives in Representation Theory," which is a six-week program May 5-June 13, 2025 based at the Sydney Mathematical Research Institute (SMRI). The program is focused on several overlapping aspects of interest in representation theory: combinatorial, number-theoretic, and new developments using machine learning. The program will bring together experts in these subjects, visiting Sydney in overlapping intervals of one to three weeks, with the aim of bringing together and encouraging new collaborations amongst a diverse international cohort of mathematicians working in different aspects of representation theory. Two workshops will be held during the six-week program. Senior scientists in residence will explore relations between the aforementioned aspects of representation theory through their expertise in the following topics: combinatorics of Coxeter groups, total positivity, and emerging geometric methods on the representation theory of p-adic groups. In more detail: (1) Combinatorics of Coxeter groups is one of the central interfaces between Lie theory and combinatorics. Of particular interest is the recent suggestion that techniques in machine learning and artificial intelligence can be applied to problems in Coxeter combinatorics. (2) Current interest in total positivity comes from multiple directions--the recent resolution of conjectures on the topology of Lusztig’s totally nonnegative flag varieties, the development of a theory of algebraic groups over a semifield, and the discovery of deep relations with scattering amplitudes in particle physics. (3) Progress in the theory of representation theory of p-adic groups has been fueled by geometric perspectives in representation theory. Of particular interest is the relationship between geometric and categorical approaches within the context of existing algebraic constructions of representations of p-adic groups. More information can be found at the program website https://sites.google.com/view/mod-perspect-representation/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 2025 · 2025-06

Scientific computing (SC) involves using advanced computing capabilities to understand and solve complex physical problems. With the increasing complexity of physical models and a rise in demand for high-performance computing (HPC) resources, errors can occur at all levels of abstraction in the computing process, significantly impacting the process of computer driven-scientific discovery and decision-making. Thus, correctness in scientific computing remains a formidable challenge. Prior works have attempted to verify a numerical program end-to-end but fall short in terms of scalability in the proof process, and target an idealized implementation of a numerical program. This project addresses this gap by developing a scalable verification framework, which directly targets application SC libraries written in Low Level Virtual Machine (LLVM)-compatible programming languages, like C/C++, Fortran and Julia, instead of an idealized implementation. The project's novelties are mechanized functional models for SC algorithms, accuracy and stability proofs of critical numerical algorithms in reduced- and mixed-precision floating-point (FP) formats, and the development of evidence and assurance cases for guaranteed correctness of future ports to new accelerated hardware. The project's impacts are an increased productivity of model developers, by providing a framework for root cause analysis of numerical bugs, and strong assurance cases for highly performant SC applications. This project addresses the fundamental challenges of creating a scalable end-to-end verification of SC code by addressing issues in each of the following abstraction layers: (1) numerical behavior at the compiler level by rigorously modeling compiler optimizations for FP arithmetic and extending the translation validation framework of the Crellvm tool (which performs Coq-verified translation validation for LLVM), to prove correctness of FP optimizations at the compiler intermediate representation level; (2) modeling approximations in mathematical formulations by mechanizing the FP error model for reduced- and mixed-precision analysis, and formalizing the correctness properties of chosen SC modules, like the convergence and stability of numerical algorithms, with respect to the mechanized error model; and (3) algorithmic approximations in various HPC libraries by verifying that the HPC libraries implement the mathematics correctly by connecting the mechanized proofs of mathematical correctness to the compiler FP optimization correctness proof. The investigators also propose to develop several automations to boost scalability and productivity of end-to-end verification of SC applications. These methods will be applied in the context of matrix-free finite element algorithms in problems related to inertial confinement fusion and plasma applications. To increase adoption of formal tools in the scientific computing communities, key ideas and theories in formal methods will be disseminated through workshops and seminar series, thereby bringing together two widely separate communities, formal methods and scientific computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This NSF award provides support for US based participants to attend the Conference: Summer School on Discrete Subgroups of Lie groups: Dynamics, Actions, Rigidity to be held July 7 to 18, 2025 at IHES in Bures-Sur-Yvette outside Paris. The primary goal of this Summer School is to allow researchers to come together and learn about recent breakthroughs and exciting developments in this area. The Summer School will feature seven mini-courses on a variety of topics in discrete subgroups of Lie groups. Each mini-course will be organized into units of 2-3 hours of a lecture followed by a problem session. This will allow each lecturer to properly introduce their topic to non-experts, including graduate students and postdocs, and allow the participants to acquaint themselves in a reasonably detailed way with the techniques used to attack central problems. The primary purpose of the award is to provide travel funding to students and scholars from the US to participate in the Summer School, so that they can learn about and participate in this exciting research area. Rigidity properties of geometric and dynamical structures under either symmetry or extremality assumptions, e.g., on curvature or entropy, have been of great interest in both geometry and dynamics, and number theory for some time. The goal typically is to force such structures to be of classical, often algebraic nature. These ideas and results build on the celebrated works of Mostow, Prasad, Margulis and Zimmer. At the heart lies the discovery that the Mostow–Margulis type rigidity results for discrete subgroups of semisimple Lie groups have far-reaching counterparts in geometry and dynamics. Investigations in this area are often spurred by the sudden emergence of or deepening of connections to other areas of mathematics. Recently, new developments have been occurring at lightning speed. This school will highlight the remarkable progress on several important problems broadly centered around the study of discrete subgroups of Lie groups. The concentrated activity around this Summer School funded in part by this grant are crucial for capturing this momentum and spur further progress. Information about the Summer School can be found at the IHES website: https://indico.math.cnrs.fr/event/12324/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This grant fosters excellence and promotes student engagement and participation at the 7th Annual Learning for Dynamics and Control (L4DC) Conference, an event fostering interdisciplinary research at the intersection of machine learning, dynamical systems, control theory, and reinforcement learning. L4DC is committed to expanding the boundaries of model-based design through the integration of massive real-time data, both temporal and spatial, prompting a necessary reevaluation of established disciplinary foundations. L4DC 2025 will feature an array of presentations, including keynote addresses, oral presentations, and poster sessions, with a special emphasis on contributions that bridge multiple research domains. By facilitating these intersections, the conference not only advances the scientific area but also fosters a vibrant community where students and seasoned researchers collaboratively push the frontiers of knowledge in dynamics and control. The objective of this NSF-funded initiative is to substantially broaden student involvement in L4DC and enrich their academic and professional development, with a particular focus on students from underrepresented backgrounds. This grant will provide comprehensive support through several targeted initiatives. First, 25 to 40 travel grants will be offered to ensure broad student participation from across the country. These grants are aimed at students who might otherwise be unable to afford attendance, thus fostering a more inclusive academic environment. Additionally, the conference will host Industry Booths and facilitate networking events, providing critical career insights and networking opportunities that students can use to bridge the gap between their academic pursuits and future professional roles. By aligning with NSF's broader goals, this award supports the integration of education and research, preparing a new generation of researchers equipped to tackle emerging challenges at the crossroads of learning, dynamics, and control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Modern cyber-physical systems operate in unpredictable environments, exhibit complex behaviors, and are controlled by sophisticated computer algorithms. In safety-critical domains such as aviation and automotive systems, behaviors of cyber-physical systems are subject to stringent requirements both on safety and performance. This project seeks to advance control design and run-time performance assurance of such systems. Providing guarantees on safety and performance is challenging due to the inherent complexity and uncertainty of their operating environments of these systems, as well as the need for real-time decision making and adaptation. Traditional approaches to control design often fall short in handling the complexity of such systems. There is, therefore, a pressing need for developing new theories, algorithms, and tools that can enable more effective and robust control of complex nonlinear cyber-physical systems. Such advances have the potential to significantly benefit design processes of many systems of societal importance, especially those that are safety critical such as airplanes, as well as manned and unmanned ground vehicles. This project will develop theory and algorithms that will enable efficient and assured design of complex nonlinear systems operating under tight constraints which may significantly reduce the high cost of complex system verification. To achieve these objectives, this project will develop theoretically grounded hybrid hierarchical representations for nonlinear systems that can be used for verification, correct-by-construction control, and learning. The key insight is to lift complex nonlinear dynamics and nonlinear constraints into a hybrid domain with local linearizations, where learning, control design, and monitoring can be done more efficiently. Efficiency will be achieved by 1) defining partial orders among hybrid liftings to enable refinement and improvement of representation accuracy adaptively as needed, 2) developing novel implicit invariant set computation methods for hybrid liftings, 3) developing incremental hybrid lifted model learning techniques that can incorporate physics-based priors and that are complemented with statistical model invalidation methods. The results will be demonstrated with applications in safety critical-control and monitoring of vehicle management 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 2025 · 2025-05

Augmented reality (AR) is an emerging technology with potential applications in many fields, including education, entertainment, and public safety. In single-user AR, a user's perception is augmented by overlaying virtual objects onto the real world. In multi-user AR, multiple users are able to view and interact with a common set of virtual objects. However, in today's multi-user AR applications, users can experience poor performance (such as high latency or inconsistent views of the virtual objects by different users) due to the AR application's lack of advanced networking capabilities. This project seeks to address such user experience issues by developing the network capabilities of the underlying AR platform, in order to support future multi-user AR applications. The vision of this project is to create AR-enabling technology where multiple users can visually scan the environment, and a common set of relevant virtual objects swiftly pop up on top of the real-world objects. To accomplish this, the AR devices need to efficiently compute and exchange information amongst themselves, in order to synchronize their joint understanding of the real and virtual worlds. The planned research has three main technical components: (1) measuring and characterizing multi-user AR applications; (2) adapting an AR application to the computing and networking conditions; and (3) adapting the network to the needs of an AR application. Evaluation scenarios include an educational AR classroom application, as well as mobile clients in a 5G testbed. The success of this project will open up a new class of multi-user AR applications in a broad range of fields, including education (for example, multiple AR-equipped students in a classroom) and public safety (for example, multiple AR-equipped first responders in a disaster scenario). The findings from this project will impact how application developers design AR applications and how network operators manage AR network traffic. The multi-user AR platform will be incorporated into undergraduate classes and a campus makerspace, as well as in outreach efforts at local hackathons and high school workshops, with the aim of encouraging women to enter and stay in engineering fields. Results from this project, including papers, simulation and application code, and AR quality evaluation and benchmarking tools, will be available at the project website https://github.com/multi-user-ar. The website and code will be available and supported for the duration of this project, and preserved online thereafter. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

The Cyber and Terrorism Insurance Studies (CATIS) Center addresses the growing risks posed by cyber threats and terrorism, which can cause devastating financial and operational disruptions. Despite the increasing frequency and severity of cyber incidents and terrorism-related events, the insurance industry faces challenges including inconsistent risk assessments, limited data-sharing frameworks, and a shortage of professionals equipped to manage these evolving risks. CATIS brings together leading universities, cyber and terrorism insurance carriers, reinsurers, brokers, risk modelers, data providers, insureds, cybersecurity vendors, and policymakers to develop innovative research, tools, and educational programs that can strengthen the cyber and terrorism insurance markets. By improving risk modeling and sharing critical data, the Center assists businesses and government in anticipating and managing catastrophic risks. Additionally, the CATIS Center prepares new generations of professionals through workforce development initiatives, ensuring the insurance and cybersecurity industries have the expertise needed to navigate emerging challenges. With planning grant support from the Industry-University Cooperative Research Centers (IUCRC) program, the CATIS Center is a three-site collaboration, where each site contributes expertise in cyber risk modeling, terrorism risk analysis, and insurance. The Center's research focuses on developing artificial intelligence (AI)-driven risk modeling techniques, refining definitions of cyber and terrorism-related catastrophic events for insurance and reinsurance markets, and exploring the impact of cybersecurity controls and counter-terrorism measures on risk reduction. The Center also links with industry partners to enhance data-sharing methods, standardize underwriting practices for emerging risks like AI liability, and establish guidelines for pricing and managing catastrophic cyber risks. These efforts can lead to a stronger, more resilient insurance industry, providing businesses and policymakers with better tools to assess, mitigate, and insure against cyber and terrorism-related threats. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Creating a quantum network that efficiently distributes entanglement between distant nodes is a significant challenge in quantum information science (QIS). Building such a system would enable scientific breakthroughs with modularized quantum computing, enhanced metrology with distributed quantum sensors, and new forms of encrypted communication. It would also accelerate progress of the quantum technology sector in the U.S. and lay foundations for new capabilities in industry. However, the components for such a system still need to be developed. This CAREER award supports the PI's research team in building components for a robust quantum network. The research team will use ytterbium (Yb) atoms trapped in optical tweezer arrays and nanophotonic cavities to create quantum nodes. Quantum information will then be transmitted between nodes by entangled photons emitted by the atoms and channeled using the nanophotonic cavities. Unique optical transitions in Yb generate photons at telecommunications wavelengths, which will facilitate long-distance quantum communication. The award's educational activities are seamlessly integrated with the PI's research efforts, providing students with a comprehensive understanding of QIS and facilitating the dissemination of this research to the broader community. This research team will take a holistic approach to educating high school, undergraduate, and graduate students in QIS. High school students from local communities will experience lab tours, interactive lectures, and summer research positions in the PI's lab. The PI will also organize QIS research events for undergraduates. Additionally, seminars and panel discussions will inform upper-level undergraduates and graduate students about various QIS career paths. The PI’s team will ultimately explore experimentally uncharted territories in quantum science, establish a robust quantum networking backbone, and develop the next-generation quantum workforce. The award will support the development of a quantum networking platform capable of efficiently linking separate quantum systems, thus enhancing complexity while preserving the inherent advantages of entangled systems. The PI’s research team will construct quantum nodes using optical tweezer arrays of Yb atoms coupled to nanophotonic cavities, distributing quantum information between nodes with entangled photons. The ultra-long coherence nuclear spin qubit in Yb makes it a desirable candidate for the coherent storage of quantum information. A unique set of optical transitions in Yb produces entangled photons in the telecom optical frequency band, which are enhanced and efficiently transported by the nanophotonic cavity. The telecom operation of this quantum network is critical for the long-distance transfer of quantum coherence and integration with existing silicon photonic devices. Furthermore, the proximity of neighboring atoms in an atomic array leads to cooperative effects that can further enhance entangled photon emission into the nanophotonic structure. Understanding these cooperative effects in ordered atom arrays remains an open theoretical problem in quantum optics and is only recently experimentally feasible. The PI's team will investigate these effects using the nanophotonic cavity as a conduit to manipulate and read out the cooperative state of the atom array. This CAREER award will support the establishment of a robust and scalable quantum networking platform, paving the way for significant advancements in QIS. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The opioid crisis is a serious public health issue, causing over 80,000 overdose deaths in the U.S. in 2021 alone, which underscores the urgent need for safer pain medications that are not addictive. The GRAM-CAROLINE project aims to transform the way drugs are designed using advanced artificial intelligence (AI) by combining scientific knowledge and medicinal chemistry. The main goal is to develop new molecules that boost the body’s natural pain relief system, providing effective pain relief without the risk of addiction linked to opioids. This project will create a new generative AI system that uses scientific rules to produce better solutions and a machine learning model that uncovers hidden scientific patterns, making the AI results more understandable and reliable. This cutting-edge approach will be used to create new pain relief medications which can then be tested in labs and iteratively improved. In addition to tackling the opioid crisis, the project aims to achieve significant scientific breakthroughs that can be applied in numerous fields, enhance interdisciplinary education, and support diversity in science and engineering by incorporating the research into university programs. The GRAM-CAROLINE project demonstrates the potential of AI to solve urgent health problems while advancing scientific understanding and benefiting society. The ongoing opioid crisis, resulting in over 80,000 overdose deaths in the U.S. in 2021 alone, highlights an urgent need for safer, non-addictive pain medications. The GRAM-CAROLINE project (Grammar-Reinforced AI Modeling with Conditional Autoencoder and Relevance-Oriented Learning for Interpretable Knowledge Extraction) aims to address this need by leveraging advanced artificial intelligence (AI) to revolutionize the drug design process. The primary objective is to create de novo amplifier molecules that enhance the body’s endogenous pain control mechanisms, delivering effective pain relief without the addiction risks associated with opioids. This will be achieved by developing a novel AI framework that integrates domain-specific scientific knowledge and grammar-based molecular encodings with machine learning techniques to generate viable candidate molecules. Additionally, the project will establish a transparent machine learning model capable of extracting hidden scientific rules and constraints, leading to interpretable and reliable AI-generated solutions. The key research activities involve creating a generative AI system using a conditional variational autoencoder with grammar-based and physics-informed constraints, designing a new transparent machine learning model using reinforcement learning to extract unknown constraints, and applying these models to design amplifier molecules for the body’s natural pain suppression system. This process includes generating potential molecules, validating them in vitro, and iteratively refining both the learned constraints and generated outputs to enhance the probability of discovering viable drug candidates. The project promises significant contributions to AI and pharmacology by developing an interpretable framework for drug design, supporting advancements in other scientific fields, and fostering multidisciplinary education in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This project is related to the support for the 2025 Compound Semiconductor Week (CSW) Conference. CSW is the premier forum for science, technology, and applications of compound semiconductors, combining the International Symposium on Compound Semiconductors (ISCS) and International Conference on Indium Phosphide and Related Materials (IPRM) in a single venue. CSW 2025 will provide a unique platform for sharing the latest advancements in compound semiconductors, with a particular emphasis on interdisciplinary approaches to solve critical challenges in the field. The scope will cover the entire spectrum of compound semiconductor research, from fundamental physics to novel electronic and photonic device architectures and integrated systems. The conference will feature a mix of plenary and invited talks, technical sessions, and interactive poster presentations designed to stimulate in-depth technical discussions and foster collaborative research. By hosting technical sessions and cross-disciplinary discussions, CSW 2025 aims to push the boundaries of semiconductor research and accelerate the adoption of novel technologies in real-world applications. CSW covers important topics related to photonics materials and device/integration science. CSW 2025 will bring together world-leading researchers, scientists, and engineers to present pioneering work in areas such as III-V, II-VI, GaN, SiC, and emerging materials such as two-dimensional semiconductors and ultra-wide bandgap materials, providing a platform for sharing the latest advancements and breakthroughs in compound semiconductor science and technology. Participant support will be in the form of stipends for US students and early career professionals to help cover their registration/travel and related costs, with the goal to increase US students and professionals’ participation. Through this support the conference aims to nurture the next generation of compound semiconductor scientists and engineers, thereby increasing participation in STEM fields in the US. Outreach efforts will include mentoring activities related to career development and navigating academic and industrial research paths. These activities will help prepare students and early career professionals to become future leaders in academia, industry, and government research. CSW 2025 will also serve as a venue for early career researchers to present their work and network with established experts in the field, promoting interdisciplinary research and 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 2025 · 2025-04

The Industry-University Cooperative Research Center for Digital Twins in Manufacturing (IUCRC-DTM) will address fundamental research challenges for developing, deploying, maintaining, updating, and evaluating Digital Twins in manufacturing domains. A Digital Twin is a purpose-driven replica of some physical component, such as a machine or a process. A Digital Twin collects real-time data from its physical counterpart, and uses this data, together with one or more models, to estimate or make predictions about important metrics, such as system health and product quality. Using these estimates or predictions, decisions can be made to improve quality and cost-effectiveness through reduced downtime and wasted raw materials. For example, machines in a factory may degrade slowly, resulting in inferior product quality, but such degradations may not be noticed right away. Predictions from Digital Twins can be used to alert manufacturers to degrading health of their machines, before significant quality issues arise. The mission of the Center is to generate pre-competitive research outcomes for its members (and the manufacturing industry in general) towards the advancement of Digital Twin technology, and consolidate many current Digital Twin solutions around a common framework to advance extensibility, reusability, and interoperability of the solution components across different manufacturing settings. To empower the manufacturing workforce, materials will be developed for education, training, and workforce development. The IUCRC-DTM will initially propose three thrust areas. (1) Digital Twin Frameworks and Standards: development of a Digital Twin framework that is maintainable and extensible, incorporates existing solutions and solution threads, is reusable (within and across domains), and exhibits inheritance and generalization, interoperability and exploration into new domains. (2) Digital Twin Applications: improving key components of Digital Twins such as reconfiguration, composition, and human interfaces, as well as identification of key use cases of Digital Twins models in various manufacturing sectors. (3) Digital Twin Tools and Workforce Development: creation of software tools, workforce development materials/programs, and high-quality datasets to prepare future workforces and successfully deploy Digital Twins in the manufacturing industry. Through integration of research projects across these three thrusts, the Center seeks to advance several transformative concepts in Digital Twins, including novel knowledge representations, aggregation and composition methods for Digital Twins, and automatic generation and maintenance of Digital Twins. The University of Michigan Site will leverage the system-level manufacturing automation research testbed, with additive and subtractive processes, collaborative robots, and integrated data collection and visualization. The U-M affiliated faculty contribute expertise in manufacturing, robotics, control, uncertainty quantification, through relationships with the automotive, chemical, and aerospace industries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The purpose of this project is to provide travel and accommodation support for participants based in the United States to attend the workshop "A Conference on the Geometry, Topology, and Dynamics of Infinite-Type Surfaces", which will take place at the Casa Matemática Oaxaca in Oaxaca, Mexico from the 22nd to 27th of June, 2025. The workshop aims to create a dynamic platform where both established and emerging researchers, specializing in infinite-type surfaces and related fields, can come together. This event's goal is twofold: first, to facilitate a comprehensive understanding of recent breakthroughs in the field, and second, to explore various avenues for future research and encourage future collaboration. Furthermore, enabling US-based scientists to attend this international meeting has two additional benefits: (1) Allowing for future long term collaborations with researchers outside of the US, and (2) Providing a means to disseminate research and results to the global mathematical community. Infinite-type surfaces serve as a common thread connecting various areas of mathematics. An infinite-type surface is a two dimensional manifold with "infinitely much topology,'' e.g. the sphere with a Cantor set of points removed or a surface of infinite genus. They appear in the study of polygonal billiards, topological dynamics, as generic leaves of foliations on compact spaces, in the study of Thompson groups, or in 3-manifolds that fiber over the circle. More recently, there has been a significant surge in the study of mapping class groups associated with infinite-type surfaces, also known as big mapping class groups. These are topological groups exhibiting rich phenomenology. As automorphism groups of countable structures, often endowed with well-defined large-scale geometry, they also inhabit the intersection of geometric group theory, model theory, and descriptive set theory. While infinite-type surfaces possess this interdisciplinary nature, historically, only two workshops (in 2013 and 2019) have brought together experts from diverse fields sharing this common theme. Thus, the primary scientific objective of this workshop is to convene a diverse group of mathematicians to facilitate cross-pollination among the different areas converging on infinite-type surfaces. More information on this workshop can be found at: https://www.birs.ca/events/2025/5-day-workshops/25w5359. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This research studies how accountability-measuring algorithms are informing decision-making efforts in the integration of standards and measurements. It explores how the production of environmental, social, and governance (ESG) measurements, using AI- and machine-learning (AI/ML) technologies, re-configures constructions of societal accountability. Through ethnographic research with companies that produce ESG data and the decision-makers and investors who use them, this project highlights the limits of computing systems that seek to operationalize ethics and accountability and identifies criteria that would enable more meaningful engagement with accountability in financial markets. Along with the training of a graduate student, this project also contributes to public conversations about AI/ML ethics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This award supports the participation of graduate students and postdoctoral researchers in the July 8-12, 2025 "Bootcamp" to be held the week preceding the 2025 Summer Research Institute in Algebraic Geometry at Colorado State University, Fort Collins, CO. The algebraic geometry research community has a tradition of running a Summer Research Institute every ten years. The institute convenes researchers from around the world to discuss the developments of the past decade and to chart out the most pressing and far-reaching problems for the next decade. The next Summer Research Institute in Algebraic Geometry will take place immediately following the Bootcamp at Colorado State University from July 14 to August 1, 2025. The Bootcamp is aimed at advanced graduate students and early postdocs and has two main goals: first, to familiarize participants with a broad range of developments in algebraic geometry in an informal setting prior to the start of the Research Institute, and second to provide a venue for early-career researchers in algebraic geometry that will foster peer and near-peer collaborations. The Bootcamp will focus on fifteen areas in algebraic geometry that have seen significant developments in the past decade. Topics include birational geometry and applications, advances on algebraic curves and their moduli, derived algebraic geometry and Bridgeland stability, perfectoid geometry with applications to positive and mixed characteristics, Gromov-Witten and Donaldson-Thomas theory, K-stability and moduli spaces of higher dimensional varieties, and combinatorial geometry. More details about the Bootcamp can be found on the website: https://sites.google.com/view/agbootcamp2025/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Starting on January 7, 2025, a series of wildfires broke out in Southern California in the greater Los Angeles region. Due to extreme drought and severe winds, the fires entered densely populated areas, producing mass destruction, causing loss of life, and damaging or destroying 17,000 buildings. These wildland-urban interface (WUI) fires observed in Southern California can attack buildings by two means: (1) flames impinging on surfaces, and (2) embers (also called “firebrands”) being carried by the wind and raining down on buildings. While ignition due to flames is relatively well understood, ember exposures are much less studied. This Grant for Rapid Response Research (RAPID) will crowdsource video footage of the Palisades and Eaton fires in Southern California to understand how embers travel in an urban environment. This data will inform computational models as well as testing methods that can advance the robust performance of structures under exterior fire exposures. This research will contribute to national welfare, namely, how to ensure the fire safety and resilience of WUI communities. The objective of this RAPID project is to crowdsource time-sensitive video and image data from news outlets, businesses, and private citizens, in conjunction with satellite data and event timelines, to measure the distribution and transport of firebrands based on windspeed and fire-proximity data from the Palisades and Eaton fires in Southern California. Using machine learning techniques, the videos will be analyzed to estimate the distribution and trajectory of embers in an urban environment. Site visits will be made to video locations to take measurements of distances, e.g., between buildings, observe and record the damage, interview witnesses, and collect other available contextual information. Project data will be archived and published in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot repository (https://www.Designsafe-ci.org). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Sociality provides advantages in defense and food resource acquisition, but the ecological landscape of gregarious species is complex and requires accessing and accruing social resources (e.g., allies, friends, and mates) that can impact fitness. Studies that focus on gregarious primate species have revealed how social resources influence male interactions (e.g., male-male confrontations over hierarchy and mates), but little is known about female-female competition. This doctoral dissertation research project investigates the impact that female competition over males, and the social resources they provide, have in the reproductive success of a close human relative. The study informs the evolution of sociality among primates, provides educational opportunities, and creates pedagogical material. Studies show that competition for food resources influences female sociality and fitness outcomes, but little is known about the impact and prevalence of social resource competition. Although some non-human primate females have behavioral patterns that suggest they compete over males, who may provide protection and infant care, direct evidence is scarce. This project leverages long-term databases and integrates novel behavioral, demographic, and hormonal data to explore female social resource competition in habituated non-human primates. The study evaluates whether females use competitive behaviors (i.e., aggression and dominance) to monopolize males and receive social benefits from them. Additionally, glucocorticoids and reproductive hormones levels are measured to determine if they influence competitive behaviors in ways that limit or promote access to social resources. The study advances knowledge regarding the causes and consequences of female-female competition, as well as the social and physiological mechanisms through which such competition may influence survival and reproductive success in social animals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Thermal management is a major bottleneck in electronics cooling. Traditional techniques can no longer provide the necessary cooling. State-of-the-art devices use phase change (condensation and boiling), but this is limited by the direction and magnitude of gravity. The proposed concept overcomes this limitation by amplifying gravity using acoustic waves for improving heat transfer. The outcomes of the proposed work will make important contributions to basic science and benefit society by sustaining progress in the semiconductor industry. The project includes integrated education and outreach programs to motivate, inspire, and enrich the educational experience of K-12 students. Using experiments and modeling, the goal of the research program is to develop a comprehensive framework to effectively manipulate droplets and bubbles during phase change for enhancing heat transfer rates for thermal management applications. By superposing gravity with an acoustic field, the research program aims to demonstrate unprecedented heat transfer rates in condensation and boiling. Using state-of-the-art thermal-fluidic experimental facility and theoretical and numerical modeling, the research program investigates the heat transfer rates in condensation and boiling with three principal objectives: 1) improving the heat transfer rate in dropwise condensation and the critical heat flux in pool boiling by superposing gravity with tunable radiation pressure of acoustic waves, 2) developing a theoretical framework and analytical model for acoustically enhanced condensation and boiling, and 3) implementing acoustic wave-assisted film-wise condensation. The proposed research is expected to advance basic science by producing new knowledge that enables beyond-gravity condensation and boiling. The work will benefit society by enabling forward progress in the semi-conductor industry by providing efficient cooling for the next-generation compact microelectronic devices. It also benefits space exploration studies in microgravity environments where condensation and boiling are practically impossible due to the absence of gravity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This award supports a study of time-dependent formation and evolution of a plasma generated using photoemission driven by ultrafast lasers. Plasmas are often created through electrical breakdown of neutral gas to generate charged particles, but it is also possible to create a plasma by shining a laser on a metal surface causing electrons to be ejected by a process called photoemission. Recent developments in ultrafast nano-optics have made it possible to create plasmas using photoemission that have specific properties in the spatial and velocity-distribution of the resulting electron population. A better understanding of the fundamental physics of this process will be transformative for both basic scientific research and emerging applications, such as plasma based high speed electronics and microchips, plasma catalysis and processing, and robust ultrafast laser detectors. The project will theoretically and numerically study how the plasma electrons evolve from a given anisotropic velocity distribution due to photoemission to a thermalized state. Plasmas with precise seeding offer a widely accessible laboratory platform to study fundamental plasma physics, such as the evolution of plasma waves and instabilities. In addition, by tailoring the photoemission pulse properties and repetition rate, the research explores a possible new way of direct normal glow plasma generation using photoemission, by skipping Paschen’s breakdown, which will help to eliminate circuit stress, reduce plasma contamination, and improve system reliability. Plasma generation and control using cheap, low intensity light sources will also be explored, with the help of plasmonic resonant enhanced photoemission. Furthermore, this research may enable a new paradigm of few-cycle laser characterization for carrier-envelope phase (CEP) sensitivity with significantly enhanced signal strength using plasmas. Sub-optical-cycle control of plasmas is also important to potentially transformative applications, such as precise electrification of chemical reactions, chip-scale plasma accelerators, ultrahigh speed plasma electronics, and ambient-condition testbeds for strong-field nano-optics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

NON-TECHNICAL SUMMARY This research project aims to develop next-generation lightweight metals, with a focus on aluminum alloys reinforced with nanoparticles. These nanoparticles have sizes 1/1000 smaller than human hair, but when added to metals, they can strengthen metals so that less of the material is needed to provide structural integrity. Such composite materials are critical for industries like aerospace and automotive due to reduced material and energy consumption. A key challenge in manufacturing these composites is achieving a targeted distribution of nanoparticles during the casting (solidification) process. For example, an uneven distribution of nanoparticles can lead to a weaker material. The team is investigating the interactions between the nanoparticles and the solidifying metal to understand the mechanisms that influence particle distribution. By using state-of-the-art experimental techniques and computational models, this work is identifying the processing conditions that ensure a desirable distribution of particles. This research is expected to advance the science underlying metal processing by providing the insights needed to produce high-strength materials. This work has broader implications for the manufacture of advanced materials with tailored properties via solidification and offers insights that could be applied to other types of materials. Additionally, the project is training future engineers in cutting edge materials science and is engaging underrepresented groups in STEM fields through outreach programs. TECHNICAL SUMMARY The goal of this project is to address the fundamental mechanisms governing nanoparticle redistribution during solidification in metal-matrix nanocomposites (MMNCs). Specifically, the focus is on aluminum alloys reinforced with nanoscale particles. Achieving a controlled distribution of these particles is critical for realizing the desired mechanical properties such as strength, stiffness, and plasticity. However, the kinetics of particle interaction with a moving solidification front — whether particles are pushed or engulfed — remain poorly understood due to limitations in existing models, which are often based on oversimplified assumptions. This project combines five-dimensional (3D space, time, and composition resolved), synchrotron-based x-ray imaging with phase-field simulations to explore the dynamics of particle-front interactions. The experimental data and models will guide the development of processing strategies that control particle distribution, leading to improved MMNC microstructures, for example, by manipulating external thermal fields and the size and shape of the specimen. This work has broader implications for the manufacture of composite materials with tailored properties and offers insights that could be applied to a range of other systems, including immiscible alloys. Additionally, the project is preparing the next generation of engineers through training in advanced experiments and simulations, while actively engaging underrepresented groups in STEM fields through outreach programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

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

NSF Awards · FY 2025 · 2025-01

The IEEE International Symposium on Information Theory (ISIT) is the premier international conference on information theory, the scientific discipline that studies the mathematical foundations of efficient, reliable, and secure communications, e.g., for telephony, television, wireless, optical, and data storage systems. It has been promoted under the umbrella of the IEEE Information Theory Society (ITSoc) since 1954, originally every two years and annually since 2000. Recent editions of the ISIT have had around 1000 paper submissions with 600 papers accepted and 850 participants including 350 students who benefit from the excellent learning and networking opportunity that ISIT provides. This award supports the participation of US-based students in ISIT 2025, which will be held in Ann Arbor, Michigan, June 22–27, 2025. Participation at this conference will enhance the research experiences for students and provide increased opportunities for new collaborations. Students form an integral part of the Information Theory community, and the IEEE ISIT continues to promote student participation through the ISIT Jack Kiel Wolf Best Student Paper Award, the creation of the ISIT tutorial series, and the Information Theory Society Student Committee. Students comprise about one-third of the participants at ISIT, and the travel funds offered through this award will support the attendance at the 2025 ISIT by students enrolled in a graduate or undergraduate program in the United States and whose papers have been accepted for presentation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project will advance the state of the art of humanoid robotics, towards a vision of collaborative partners performing physically demanding tasks alongside humans. A series of gears connects the arms and legs of the robot to the electric motors that act as “muscles.” Conventionally the effect of these gears is to multiply the torque applied by the motor to the limbs by more than one hundred times, which is needed for standard low-torque, high-speed electric motors to meet the demands of robot tasks. However, this amplification creates a safety hazard, because the effect of external torques on the motor is reduced by the same amount, and therefore the motor does not “feel” contact, such as with a human coworker. The development of “quasi-direct-drive” motors requiring little or no torque amplification is a major advance in human-safe robots, but these new motors use power for static operations, like holding up a heavy weight, leading to low efficiency and reduced operating times. This project complements state-of-the-art electric motors with innovative spring mechanisms to generate steady or periodic actions with a minimum of added energy. These novel actuators will provide humanoid robots with new capabilities to safely work alongside humans, move naturally, react quickly to their environments, and operate for longer periods. These features will be experimentally demonstrated using a one-legged hopping robot. Potential societal benefits include better collaborative robots for manufacturing and logistics, and longer lasting and higher performing prostheses and orthoses. To inspire the next generation of roboticists, the project will offer hands-on STEM experience to 7th and 8th-grade students through the University of Michigan WISE GISE summer camp and will host lab tours to students from the Detroit area. This project will establish a systematic framework for the design and control of humanoid robots with an energy-efficient compliant actuator, which combines a Quasi-Direct-Drive (QDD) motor and Unidirectional Parallel Spring (UPS). This unique combination allows robots to generate high torque with significantly less energy consumption and heat dissipation, without inhibiting the leg swing motions. Integrating QDD and UPS results in complex interaction between robot hardware and control, since the parallel spring introduces a coupling effect between neighboring robot links. The holistic design framework developed by this project will simultaneously optimize the mechanical and control parameters so that the controller can utilize rather than counteract the robot’s natural dynamics. By addressing key questions about scalable optimization and bridging the gap between simulation and the real world, the investigators aim to pave the way for humanoid robots with high power autonomy and agility, facilitating prolonged operation in remote areas. This project will answer the following research questions: (1) how to quantify the performance of humanoid robots with QDD motors and UPS, (2) how to simultaneously optimize hardware and control parameters for improved energy efficiency and agility, and (3) how to bridge the simulation-to-reality gap in humanoid control to achieve agile and efficient locomotion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Coastal flooding in the Great Lakes region poses significant risks to communities, infrastructure and ecosystems due to fluctuating lake levels, heavy rainfall, and coastal erosion. These stressors that are expected to worsen with climate change are already overwhelming stormwater systems and damaging property. They will also disproportionately affect communities suffering from historical and ongoing socioeconomic disparities and environmental injustices. This project aims to enhance the resilience of Great Lakes coastal cities by co-producing climate information with city practitioners and community-based organizations (CBOs) that can support their decisions to better prevent, prepare and adapt to these stressors. This will be achieved by creating an online participatory tool that integrates climate, hydrological, spatial data and participatory GIS. This decision support tool (DST) called Participatory Urban Modeling and Climate Projections for Community-Driven FlOoding Resilience (PUMP-COR) will be developed with participants in two Great Lakes cities: Benton Harbor (MI) and Milwaukee (WI). It will allow participants to better understand and visualize their risks and make choices that can influence the implementation of solutions. By directly engaging CBOs and city practitioners, this project will broaden and diversify participation in both cities and cultivate more inclusive development decisions that can be generalized to other decisions and geographies. Specifically, the project will: 1) explore the viability of integrating three existing modeling efforts (across climate, hydrological and spatial information) to inform decision-making that builds the resilience of households, communities, and cities to flooding risks; 2) organize focus groups with CBOs and city practitioners to better understand different definitions and perceptions of resilience, risk, equitable and just solutions, and to provide feedback to each other and to the research team about their preferences and aspirations for PUMP-COR; and 3) build a network of researchers, CBOs, city practitioners, professional associations, and regional organizations to engage in the co-production process for the ongoing project and for a future broader proposal based on what can be learned from the planning grant. This broader proposal and engagement with communities will focus on how online DSTs can increase the number of people, communities and cities using PUMP-COR to build resilience of coastal cities. The outcomes will include improved decision-making for flood resilience, enhanced community participation, and better climate information tailored to the needs of diverse decision-makers. This research will significantly contribute to the fields of climate modeling, participatory GIS, and the science of actionable knowledge, offering innovative solutions for climate adaptation and 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.