University Of California Berkeley

NSF Awards · FY 2025 · 2025-12

The nation’s technology leadership depends on effectively training the computing workforce. Many students begin their education at community colleges before transferring to four-year universities to complete degrees in computing. These transfer students bring talent and perspective but often face barriers in navigating academic requirements, accessing support systems, and preparing for careers. This project examines how institutions can better serve transfer students in computing by strengthening advising, tutoring, orientation, faculty engagement, peer communities, research opportunities, and professional development. This project aims to increase graduation rates, support entry into graduate study, and improve career placement. Findings will provide colleges and universities across the country with evidence-based practices to help more students succeed in computing fields critical to national competitiveness, including emerging areas such as artificial intelligence and data science. This award supports a collaboration between the University of California and partnering community colleges to evaluate and enhance institutional effectiveness in serving computing transfer students. The project applies and refines a “servingness framework” to assess how institutional structures contribute to student persistence, degree completion, and post-graduation outcomes. Research activities include: (1) identifying institutional barriers to transfer student success, (2) implementing strategies that promote adoption of best practices at four-year institutions, and (3) evaluating the effectiveness of these practices in advancing student outcomes. Project deliverables will include replicable models, institutional action plans, and a theory of action that links policy, instruction, and student support to measurable gains in transfer student success. The work will provide a scalable foundation for strengthening computing transfer pathways nationwide. 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 2025 · 2025-11

PROJECT SUMMMARY Almost all new drugs on the market emerged from the synthesis of thousands of structurally similar molecules. Once a compound is identified to possess targeted therapeutic activity, medicinal chemists generate an extensive array of derivative compounds with numerous modifications in structure to tune key properties, such as potency, selectivity, stability and solubility. The ability to structurally edit a single compound at multiple sites would circumvent the need for lengthy de novo syntheses of each derivative, exponentially improving the efficiency of the drug discovery process. Transition metal-catalyzed functionalization of C–H bonds has emerged as a powerful tool for late-stage derivatization of complex molecules. However, selectivity between multiple C– H bonds in similar structural environments remains a challenge. Current methods to control site selectivity suffer from various shortcomings, including the need to install and remove directing groups, limitation to simple structures, and inability to overcome large differences in the reactivity of C–H bonds. The proposed research focuses on the development of a systematic and tunable strategy to control site selectivity of iridium-catalyzed C–H borylation that is capable of overcoming inherent substrate preferences and that can target specific C–H bonds. The proposed approach leverages a variety of common functionalities in bioactive molecules as recognition sites, conferring generalizability while avoiding the need for directing groups. In addition, the proposed approach is informed by a recent breakthrough by the sponsor’s laboratory showing a dramatic activating effect of 2-aminophenanthroline ligands on the borylation of C–H bonds. The impact of this research is a flexible tool for precise structural editing of bioactive compounds. Specifically, the proposed research will include the synthesis of a suite of ligands derived from 2-aminophenanthroline, bearing interaction sites that are connected to the backbone with a sidearm linker and that are capable of attractive noncovalent interactions with common Lewis-basic functionalities. A modular synthetic approach is described, enabling the assessment of various interaction sites as well as structure and connectivity of the sidearm linker. Iridium catalysts generated from the ligands will be tested for their ability to overcome the inherent selectivity of substrates toward undirected borylation. Upon success, a series of sites will be targeted for functionalization by adjustment of the linker structure. Subsequent functionalization of complex, biologically active compounds will demonstrate generality of the method and applicability to lead optimization. Mechanistic studies will be integrated in each step of the proposed research to guide catalyst design and improve reaction efficiency and site-selectivity. Achieving the specific aims of the proposed research will provide chemists with a powerful strategy for precise, targeted, late- stage diversification of complex organic molecules, thereby accelerating the generation of biologically active compounds.

NSF Awards · FY 2025 · 2025-11

Small denominator problems and quasiperiodic motion appear naturally in classical and quantum systems that have multiple incommensurate frequencies of periodic motion. Examples of such systems exist in celestial mechanics (planetary orbits), biology (population dynamics), solid state physics (quasicrystals), mathematical physics (quasiperiodic Schrodinger operators, or, more generally, time-dependent dynamics in systems with localization), and partial differential equations (non-linear Schrodinger and wave equations with periodic coefficients). The analysis of such problems requires dealing with small denominators; in other words, understanding how often and in what pattern would the system return to a state that is very close to the initial state. Traditionally, these problems have been approached by Kolmogorov-Arnold-Moser (KAM)-type techniques. In the setting of quasiperiodic operators, the main limitations of KAM methods is that they are very difficult to apply to truly multi-dimensional systems, due to the complicated structure of resonances. Alternative approaches (methods based on estimates of Green's functions) do not have these dimensional restrictions. Until recently, those methods have not been as flexible as KAM in the direction of parameter removal. However, this is currently changing largely due to the recent works of the principal investigators (PIs) of this project. The project involves research and training activities towards developing and refining these new methods and applying them to the study of problems involving quasiperiodic Schrodinger operators and nonlinear partial differential equations, obtaining previously inaccessible multi-dimensional and arithmetic results. These have potential applications in all the fields mentioned above. The technical heart of the proposal is the development of non-perturbative methods for Green’s function estimates for lattice quasiperiodic operators, assuming that the frequency parameter is restricted to a submanifold of a torus. Such problems appear naturally in the analysis of multi-particle quasiperiodic operators as well as nonlinear Schrodinger (NLS) and nonlinear wave (NLW) equations, and have been inaccessible until the work of Bourgain–Kachkovskiy which, however, is only the first step since it significantly relies on the two-dimensional setting. These methods will be applied to constructing new classes of spacetime quasiperiodic solutions of the NLS and NLW equations, by lifting the current dimensional and arithmetic restrictions of the Craig–Wayne–Bourgain approach. It is also expected that these methods will allow to construct full-dimensional KAM tori. Recent advances by the PIs from multiple directions also allow, for the first time, to consider arithmetic localization results for multi-dimensional quasiperiodic operators, motivated by recent sharp results obtained by Jitomirskaya and Liu. 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 2025 · 2025-11

Project Summary Mycobacterium tuberculosis (Mtb) is leading cause of death worldwide by a single infectious agent. In 2023, approximately 10.8 million people fell ill with tuberculosis (TB) and 1.25 million people died.1 The only licensed vaccine available, Mycobacterium bovis Bacillus Calmette-Guerin (BCG), has limited efficacy that wanes over time and fails to protect against adult pulmonary TB or impede TB transmission.3 Efforts to develop new and effective TB vaccines have remain unsuccessful primarily due to our lack of understanding of the immune responses that mediate TB protection. The persistence of the TB pandemic and the absence of an efficacious vaccine for preventing TB disease emphasize the urgent need to further understand mechanisms and immune correlates of protection to TB that can help guide the design of novel vaccines. The use of experimental vaccines that elicit protection against Mtb challenge in animal models could lead to insights into adaptive immune mechanisms that are important for protection against Mtb infection. We recently demonstrated that intranasal (i.n.) vaccination with the experimental vaccine H1/CDN provides remarkable protective efficacy against infection with Mtb in the mouse model. This protein subunit vaccine consists of H1 Ag, a fusion of two highly immunodominant Mtb protein antigens (Ag85b and ESAT-6), and the STING activating adjuvant cyclic-di-nucleotide (CDN) ML-RR-cGAMP (H1/CDN). While we have shown that IL- 17 and IFN-γ producing CD4 T cells are required for CDN adjuvanted vaccine efficacy, the precise phenotypes and localization of Th1 and Th17 cells required for protection are still unclear. In addition, the specific cell types that respond to IL-17 during vaccination and Mtb challenge and the role of IL17 in their activation and recruitment remain unknown. Furthermore, the mechanism by which STING activation leads to increased protection also remains elusive. In this project, our goal is to investigate the mechanisms and correlates of protection elicited by CDN adjuvanted vaccines for tuberculosis. In Aim 1, we will determine how spatial localization and trafficking of Th1 and Th17 cells impacts vaccine efficacy. In Aim 2, we will determine the role of IL-17 in the recruitment, spatial localization, and phenotypes of both epithelial and innate and adaptive immune cells necessary to confer vaccine-elicited protection. Finally in Aim 3, we will determine whether STING mediated autophagy contributes to CDN adjuvanted vaccine efficacy. Together these results will provide a better understanding of the mechanisms underlying CDN vaccine protection and inform the development of more effective vaccines against Mtb.

NSF Awards · FY 2025 · 2025-10

The second annual conference of the Northern and Central California Section of SIAM (SIAM-NCC) will be held October 27–28, 2025, at Lawrence Berkeley National Laboratory. This event brings together researchers from academia, industry, national laboratories, and government to strengthen regional collaboration in applied and computational mathematics. The conference aims to: (1) create an opportunity for scientific researchers to meet, network, and share the innovations and recent developments in their fields; (2) attract and energize students and researchers working in applied and computational mathematics and related fields; (3) offer SIAM members from all institutions in the NCC region the opportunity to attend a local meeting of the community; (4) provide early career researchers the access and opportunities to connect with others who are at similar career stages; (5) inspire the next generation of applied and computational mathematicians to get involved in the community and to innovate through research and education. Conference themes align with SIAM Activity Groups and include mathematical analysis, optimization, inverse problems, experimental design, high-performance computing, uncertainty quantification, scientific machine learning, AI, and digital twins. The program will feature two plenary talks, eight thematic sessions, two panels, two poster sessions with blitz presentations, a mentoring event, a CV/interviewing workshop, and guided tours of the National Energy Research Scientific Computing Center (NERSC) and the Advanced Light Source (ALS). The presenters will come from academia, industry, and national laboratories, representing many scientific backgrounds, career stages, and institutional affiliations. The themes and speakers for each thematic session will be selected via an open call and coordinated by the Technical Program Committee. More information is available at the conference: https://siamncc25.lbl.gov. 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 · 2025-10

Behavioral economics has had enormous success in the last several decades in making progress towards its goal of making economic theory more psychologically realistic. Through the use of careful experiments, clever modeling and convincing analysis of data, behavioral economists have developed a powerful, predictive portrait of how humans reason about and respond to risk, uncertainty, time delay and small scale social interactions. But behavioral economics thus far has not yet seriously turned its powerful lens towards the classical focus of economic theory: understanding how individuals respond to and reason about the mass behavior of large groups of people. Yet understanding the special psychology of mass behavior, and how it impacts markets, online cohorts, mass movements and electorates, has never been more urgent in our increasingly connected world. In this research, the investigator will use the powerful conceptual and empirical tools of contemporary behavioral economics to answer some basic questions about the psychology of mass behavior in order to guide the development of predictive models and guide decisions. Using experiments, the researcher will explore how large groups reason together, how individuals reason about large groups and how people respond to the strategic powerlessness of acting as a member of a large group. The dataset gathered by the investigator and the conceptual insights gleaned from it will allow us to better understand the function of human institutions, the way people form beliefs, and the rationality of large groups of people acting and interacting in concert. Behavioral economics has mostly, so far, focused its attention on understanding how people reason about and form preferences over either (i) exogenous variables or (ii) very small-scale strategic interaction. For instance, the vast majority of empirical and theoretical work in the field focuses on understanding individual-level optimization, statistical inference, risky choice, ambiguous choice, intertemporal choice, social preferences, and behavior in games that involve only a handful of players. This focus has been highly productive, generating both (i) a set of sharp characterizations of the structure of human reasoning and preference formation in these settings and (ii) a set of conceptual and empirical methods for characterizing and modeling individual behavior. This work will extend these methods to include how people reason about and respond to the behavior of large groups of other people (“mass behavior”). The idea is to use the same style of experiments that have been honed for decades for understanding statistical inference, optimization and preference formation in response to exogenous variables, and simply replace these exogenous variables with the endogenous aggregate behaviors of large groups of other people. The research project will focus on understanding (i) how large groups of people reason or compute together in various canonical institutional settings, (ii) how individuals respond to the strategic powerlessness of being a member of a large group (i.e., of being a “price taker”) and (iii) how people reason about the outcomes of mass interaction when forming social opinions and views. The data collected will be relevant to (a) building better models of large-scale institutions like markets, organizations and electorates, (b) understanding mob behavior and mass movements, and (c) designing better decisions for an increasingly integrated and coordinated world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Power electronics convert and control the electrical energy we use every day, and their advancement is critical to renewable energy, electric transportation, manufacturing, consumer electronics, computing, healthcare, and more. Next-generation technologies demand power electronics with ever-increasing efficiency and performance with ever-decreasing size and cost, but advancement along these dimensions is majorly bottlenecked by the passive components (i.e., energy storage elements) integral to their operation. This work will elucidate how an alternative passive component technology – isolated piezoelectric transformers – could enable major advances in the miniaturization and performance of power electronics. Piezoelectric components offer very high theoretical efficiencies and energy densities with favorable scalability to small sizes, but so far these advantages have only been realized in a narrow range of power conversion applications. Isolated piezoelectric transformers are positioned to extend the advantages of piezoelectrics to a wide variety of applications that require electrical isolation, such as grid-connected power supplies and medical devices, removing their need for bulky, lossy magnetic transformers. This project will generate significantly expanded scientific knowledge of piezoelectric materials and components as power passive components, and how to best utilize them in power electronics to enable drastic miniaturization and performance improvements. Further, the proposed educational activities will lay a foundation for realizing an expanded and interdisciplinary workforce in power electronics, electrical engineering, and STEM. While piezoelectric resonators have achieved promising efficiencies and power handling densities as power passive components, piezoelectric transformers have been limited to an order of magnitude greater loss and two orders of magnitude lower power density. This highlights a significant gap in demonstrated performance between components of comparable theoretical capability, so there is an opportunity to apply what is now understood about designing piezoelectric resonators to the development of high-efficiency, high-power-density isolated piezoelectric transformers. To pursue this on multiple levels, the research objectives of this project will include (1) characterizing and modeling piezoelectric material losses, (2) designing high-efficiency, high-power-density isolated piezoelectric transformers, and (3) developing power converter circuit topologies and control strategies that best utilize isolated piezoelectric transformers. The resulting models and design techniques will be validated in multiple experimental demonstrations of isolated piezoelectric-transformer-based power converters. The education objectives of this project include (1) developing a hands-on, open-access power electronics curriculum, (2) broadening participation in power electronics and STEM through curriculum intervention and other means, and (3) bridging the expertise gap between power electronics and acoustics/MEMS through professional education. 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 · 2025-10

This project studies the algorithmic foundations and methodological frameworks to augment human capabilities via a novel form of physical and cognitive collaboration between human and multi-agent robotic systems, creating Aerial Co-Workers. These machines will actively collaborate with each other and with humans and tackle the fundamental gaps related to human-MAV collaboration at both physical and cognitive levels. The project is organized along two main thrust areas: Physical Collaboration and Cognitive Collaboration. The first thrust aims to significantly augment the physical ability of human workers by taking advantage of physical collaboration between the operator and a network of interconnected quadrotors, equipped with a set of flying hands", transporting objects. This will produce novel scientific solutions for human-robot collaborations to account for the complex legibility of the motions, and the variability of the relative positions of the agents. The second thrust aims to address two perception consensus problems to enable MAV-assisted augmented reality (AR) to augment the cognitive ability of operator(s). The key is to consistently collect, analyze, and display contextual information via multiple MAVs for effective and natural human-robot visual interactions. Aerial Co-Workers will get vantage viewpoints of the environment occluded from the humans which can be customized and augmented directly in the workspace to facilitate human actions via novel metric-semantic collaborative space mapping. This project will have a strong societal impact as a disruptive technology for industry as well as the construction market, which is in urgent need of innovative solutions for enhancing the efficacy while maximizing safety. The outcome will enable safer, faster, and simpler task execution in scenarios including maintenance, inspection, transportation, and search and rescue. The project will contribute to lowering the barriers for new researchers in robotics, computer vision, and machine learning by making hardware designs, algorithms, datasets, and code available on open-source forums. The playful nature of AR tools and quadrotors employed in this project will contribute to engaging K-12 and undergraduate audiences. 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 · 2025-10

Genome editing tools have revolutionized biology. Scientists are using these tools to connect genes to phenotypes, generate novel phenotypic variation, and for diverse crop improvement applications. This project expands the possibilities for editing in crop plants to include a method for editing gene expression, so called “epigenetic editing”. Epigenetics is a broad term used to describe mechanisms that change gene expression without directly changing the DNA. Epigenetic variability can have profound impacts on an organism’s phenotype, and many important agronomic traits are influenced by gene expression. Broader impacts of the project include the wide dissemination of the method for the improvement of crop plants and other species, plus the training of students via integration with a long running and successful undergraduate internship program. Epigenetic crop improvement strategies will complement existing biotechnology and breeding strategies and may offer opportunities to help maintain crop yields in the face of climate change. This project will directly train the next generation of scientists to use these tools through an effective Research Experiences for Undergraduates (REU) program. All resources generated in this project will be made available for use. This project will expand the possibilities for editing in crop plants to include epigenetic editing. It was recently demonstrated that it is possible to affect gene expression through targeted DNA methylation at MeSWEET10a in the important crop, cassava. MeSWEET10a is not normally expressed in leaf tissue but is ectopically induced by a bacterial pathogen using a transcription activator-like (TAL) effector. De novo methylation of the MeSWEET10a promoter blocked TAL binding and led to decreased disease symptoms. Despite these encouraging results, epigenetic editing is still an immature technology. The goal of this project is to fill specific knowledge gaps related to establishment, maintenance, and inheritance of epigenetic edits and in so doing, lower the entry point for other researchers to adopt this powerful technology. Specifically, aim 1 of this proposal is to develop and distribute epigenetic editing tools in other crop species. The work will leverage and adapt the technologies known to work in cassava to accomplish similar disease outcomes in rice and tomato. Aim 2 will expand on the applications of epigenetic editing in crops. This includes the ability to target multiple loci simultaneously, “fine tune” gene expression and tissue specific editing. Aim 3 is dedicated to characterizing stability and heritability of de novo epialleles. If successful, other research projects and applications in biotechnology will benefit. This work will also provide additional jumping off points for fundamental work on epigenetics in non-model plant systems. Further, an ability to directly edit the epigenome will empower the larger field of epigenetics. 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 · 2025-10

Nontechnical Description: The increasing data volumes from artificial intelligence (AI), internet of things, and 5G/6G networks is challenging the processing power of CMOS-based computing hardware. To extend the computing power scaling and energy efficiency, we propose light-based photonic integrated computing circuits for computing at high clockrates and with ultralow-loss on-chip data movement. This project supports the national interest by significantly reducing the energy required for AI computations—potentially two orders of magnitude more efficient than current CMOS technologies—paving the way for powerful and sustainable computing systems. Its outcomes could revolutionize applications from autonomous vehicles and healthcare diagnostics to natural language processing and scientific discovery. The project will also strengthen U.S. competitiveness in semiconductor manufacturing by training a new generation of experts in chip design, photonics, and AI. Importantly, it will promote STEM participation through hands-on training and outreach. Technical Description: This project aims to build high efficiency (>100 TOPS/W), high-throughput photonic-electronic hybrid processors by leveraging wafer-scale heterogeneous integration of thin-film lithium niobate (TFLN) and silicon photonics/electronics. The key technical goals include: (1) developing space-time-wavelength hyperdimensional photonic circuits that can perform massive parallel tensor computations using scalable time-multiplexed data encoding; (2) enabling CMOS-compatible, high-speed electro-optic modulators via TFLN onto silicon to realize >50 GHz bandwidth and <10 fJ/conversion energy; and (3) co-designing hardware and software to, support large-scale models like LLMs and real-time decision-making in multi-agent systems. The proposed architecture achieves matrix-matrix multiplications with O(N) modulator scaling and energy efficiency—orders of magnitude beyond CMOS limits. The system will benchmark end-to-end AI performance in reinforcement learning with applications of autonomous driving. 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 · 2025-10

This Track 1 IUSE project aims to serve the national interest by improving research experiences for college students pursuing degrees in STEM fields and in math and science teacher preparation programs. Undergraduate research is widely recognized as important for the professional growth of STEM students by fostering authentic scientific inquiry, contributing to scientific knowledge, increasing integration into the scientific community, and improving retention in STEM academic pathways. Graduates who become teachers are positioned to pass on these benefits by educating, encouraging, and preparing their middle and high school students to pursue STEM majors and careers. The project includes the development of curricula for university research methods courses to strengthen student learning and research outcomes. It also features mentor workshops and coaching seminars to enhance teaching and mentoring for undergraduate research students. These resources are intended to be broadly shared with graduate students and faculty across college campuses. Ultimately, the project seeks to impact thousands of K-12 students, cultivating their interest in STEM and preparing them to enter STEM career pathways. This project pursues two primary goals: 1) to develop course curricula and professional activities that enhance learning and build confidence among undergraduate researchers in research methods courses and faculty laboratories, and 2) to design workshops that introduce graduate student mentors to effective teaching and mentoring practices. A set of instruments is employed to directly assess research products, measure mastery of research skills, and evaluate the instructional and mentoring approaches used by graduate students. This framework allows researchers to draw explicit connections between graduate students' teaching and mentoring practices and their mentees' understanding of research. For undergraduates who become teachers, it further links these practices to their future K-12 instructional methods and the impact on student learning. The resulting analysis supports the identification of effective evidence-based mentoring and teaching strategies that support the development of future educators and boost confidence in conducting scientific research. Findings from this work informs the design of research curricula, graduate student workshops, and coaching sessions aimed at strengthening undergraduate research experiences. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 · 2025-10

Large Language Models (LLMs) are increasingly deployed as the backbone of real-world applications such as Google Search with AI Overviews and Microsoft Bing Copilot. When data and code are not properly separated within an application, the latter (including AI applications) is vulnerable to cyber-attacks. This project's novelties are twofold: (1) conducting a systematic study to deepen the understanding of such threats, and (2) developing new defenses to mitigate such attacks. Its broader significance and importance lie in establishing foundational security principles for the rapidly growing ecosystem of AI applications, which are now widely deployed across diverse societal domains. Moreover, the released code and materials produced by this project will not only help secure real-world LLM-integrated applications but also serve as valuable educational resources for undergraduate and graduate courses, fostering the next generation of researchers and practitioners in this emerging security area. Security history shows that when data and instructions are not properly separated within a system, injection attacks can emerge—for example, SQL injection attacks in traditional software. Similarly, due to the lack of a clear boundary between instructions and data in prompts, LLM-integrated applications are inherently vulnerable to prompt injection attacks. To understand and mitigate such threats this project adopts a holistic approach comprising three interconnected research thrusts to systematically investigate the security vulnerabilities of LLM-integrated applications to prompt injection attacks and to develop new methods to prevent, detect, and attribute such attacks. The project will also open-source a platform that integrates our developed algorithms along with a comprehensive tutorial on prompt injection attacks and defenses. 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 · 2025-10

The three-dimensional vertically integrated circuit (3D IC) has emerged as a key technology for extending the trajectory of Moore’s Law by increasing transistor density and circuit functionality through 3D chip stacking. In addition to the benefits of scaling, the 3D IC technology enables heterogeneous integration, allowing different chipsets fabricated by different semiconductor technologies to be combined for optimal performance and cost reduction. This approach holds a significant promise for the next-generation radio-frequency integrated circuits (RFICs). However, current advanced IC packaging technologies remain largely limited to 2.5D integration and antenna-in-package implementations. The direct face-to-face stacking of RFIC chips for full 3D integration, especially with high-frequency RF interfaces, remains largely unexplored. This project aims to develop new fundamental techniques for implementing true 3D-integrated RFICs. As a proof of concept, the project will build a phased-array front-end system operating at frequencies higher than 100 GHz to establish a foundational building block for future high-frequency wideband wireless communication platforms. In parallel, the project includes a robust education and outreach program to introduce advanced wireless and semiconductor technologies to graduate and undergraduate students as well as K-12 students. Through an interdisciplinary hands-on approach, the project aims to inspire students' interest in the STEM fields and foster their early engagement with science and engineering. The project's community outreach efforts will help extend these opportunities to younger students, cultivating the next-generation innovators in advanced wireless and semiconductor technologies. This project aims to develop a scalable planar phased-array transceiver using 3D RFIC techniques to enable next-generation ultra-wide-bandwidth wireless communication systems with antenna beamforming capabilities. The system will be realized through three key engineering innovations: (1) Wireless Interconnects: a low-loss signal transmission method for vertically integrated RFICs separated by a 10-to-20 µm gap, coupled with a cost-effective chip-stacking process. (2) Antenna Characterization via Backscattering: a simplified antenna measurement technique based on electromagnetic backscattering, eliminating the need for traditional RF probing. The method incorporates a CMOS-based electronic calibration technique to de-embed parasitic reflections caused by surrounding structures. (3) Compact Out-phasing Radio Architecture: a compact out-phasing transmitter supporting the 16-STAR modulation, integrated with 3D-stacked silicon-germanium (SiGe) power amplifiers and silicon-based dielectric resonator antennas for efficient transmission and reception. By integrating these innovations, the team aims to demonstrate a scalable 4×4 planar phased-array system operating at both 140 GHz and 240 GHz, paving the way for future compact high-performance wireless systems operating beyond 100 GHz. 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 · 2025-10

Boolean satisfiability (SAT) is a core problem in computing with broad, high-impact applications. A wide range of critical problems in industry and defense, e.g., in hardware and software design and verification, artificial intelligence, robotics, and drug discovery, use SAT solvers and often take weeks to complete on modern large-scale computing systems. This project will develop new types of accelerator chips custom-designed for SAT to reduce the time and energy required to solve all such important problems by more than two orders of magnitude compared to the best-known existing approaches. Completely new ways to combine logic circuits and memories will be developed, along with methods and tools to create these chips. The development of this hardware will dramatically benefit organizations across engineering, artificial intelligence, science, business, logistics, and defense. This project will also advance the art and science of custom computing, which will continue to increase in importance in the foreseeable future. Students will be trained in this new art and science. The models, methods, and tools developed will be shared with researchers as well as industry and defense experts to foster a vibrant community. The project will develop an algorithm-to-transistors co-optimization approach to accelerator design for SAT and an extensive set of combinatorial problems, demonstrating significantly higher efficiency than existing solutions. It will also provide new computer-aided design (CAD) tools for the realization of powerful SAT accelerators by mapping SAT algorithms and heuristics to optimized architectures, including combinations of memories, content-addressable memories, near-memory logic, and custom interconnects that enable maximal parallelization. The advantages of the new designs will be demonstrated via chip fabrication, silicon measurements, and the development of chiplet-based architectures enhancing the economics of accelerators for SAT and other problems. The technological advancements pursued by this project, and especially the new methods and tools for the design of hardware accelerators, will contribute to the state of the art in custom computing, which will become increasingly more important in the post-Moore era. The resulting designs will dramatically benefit organizations across engineering, artificial intelligence, science, business, logistics, and defense. 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 · 2025-10

The Lawrence Hall of Science at UC Berkeley and the American Institutes for Research propose a transformative 4-year study to build teacher capacity for integrating Computational Thinking (CT) into Grades 6–8 science classrooms. This initiative will advance current priorities of the NSF ITEST and DRK-12 programs by addressing the critical gap in computational practices within science curricula through the creation and implementation of a scalable professional learning model for middle school science teachers. The project will support teacher professional vision and adaptive expertise for CT-infused instruction through instructional coaching, professional development workshops, and an online professional learning community. These efforts aim to empower teachers to design, enact, and adapt CT-integrated lessons that foster students’ positive attitudes toward science and enhance their knowledge of science and CT. The project will directly serve over 1,000 students and create a teacher professional learning model that is positioned for scale to serve teachers and students across the country. The project builds on prior research and frameworks developed by the project team, ensuring the integration of evidence-based strategies and a content focus on authentic computational and scientific practices. In collaboration with multiple school districts, this study will pilot, refine, and implement the professional learning model, leveraging mixed-methods research to evaluate its impact. Data collection methods include classroom observations, teacher surveys, documentation of coaching sessions, and student assessments, all aimed at measuring changes in teacher practice and student outcomes. The project will develop a professional learning workshop and ongoing instructional coaching for in-service science teachers to support them in integrating CT skills and concepts into their science curriculum and instruction. Analysis of project data will provide evidence of the relationship between program participation and teacher and student outcomes, supporting future implementation of the professional learning model. By advancing knowledge on how CT integration can transform science education, the project aspires to broaden participation in STEM fields among all students. The dissemination of findings through conferences, policy briefs, and practitioner resources ensures scalability and sustainability, enabling educators nationwide to adapt existing science curricula to incorporate computational thinking effectively. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Sexual and gender minority (SGM) youth (including lesbian, gay, bisexual, transgender people ages 13- 18 years), a population growing in public health visibility, experience disproportionately high risk for sexual and intimate partner violence (SV/IPV), with worse SV/IPV-related health outcomes (e.g., poor physical and mental health, suicidality) than their non-SGM counterparts across the life course. Racial/ethnic minority SGM face even greater risk for SV/IPV. Alarmingly few evidence-based SV/IPV prevention approaches address inequities in risk among SGM youth, with even fewer targeting upstream, social determinants of SGM health. In the U.S., two major social determinants of SV/IPV among SGM youth are state-level anti-SGM policies (e.g., bans on transgender healthcare, SGM gag laws) and social climates (i.e., societal-level stigma based on social norms and social conditions) that create and sustain the marginalization of SGM identities. Since 2021, there has been a rapid acceleration in the use of policy to restrict the rights of SGM people, with 170 anti-SGM laws adopted in the U.S., creating an urgent need to better understand the impact of these laws on SGM health. In contrast, SGM-affirming laws (e.g., SGM anti-discrimination, gender-inclusive facilities, SGM parental rights laws) increase equity in rights and access, reducing SGM vulnerability to SV/IPV. Moreover, social climates with low SGM stigma have high potential for buffering harmful effects of anti-SGM laws and amplifying the benefits of SGM-affirming laws on risk of SV/IPV among SGM youth, yet this interaction has not been studied. As such, the overarching goal of this research is to rigorously evaluate the causal effect of recently adopted state-level SGM laws (anti-SGM and SGM-affirming) as a primary prevention approach for SV/IPV among SGM youth and to understand the buffering or amplifying effects of SGM-related social climates. To do this, we will conduct a mixed- method quasi-experimental evaluation on the effects of SGM state-level laws passed between 2021-2025 on SV/IPV among SGM youth utilizing a difference-in-differences approach, state-representative outcome data from the Youth Behavioral Risk Survey System, and qualitative interviews with state-level providers to clarify potential mechanisms for effects. The aims of the proposed research are: 1) To estimate the causal effect of state-level SGM law adoption (anti-SGM and SGM-affirming) between 2021-2025 on risk for SV/IPV victimization among SGM youth, including racial/ethnic minority SGM youth, in the U.S.; 2) To evaluate whether the effect of state- level SGM laws on SV/IPV among SGM youth differs based on state-level social climates regarding SGM stigma; and 3) To qualitatively explore mechanisms through which state-level SGM policies and social climate impact SV/IPV among SGM youth across three states with high, medium, and low adoption of SGM-affirming laws. This research will provide critical evidence on the extent to which state-level policy interventions targeting social determinants of SV/IPV provide population-level benefit across multiple violence outcomes.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The synthesis and modification of biologically active molecules is critical to the develop- ment of new pharmaceuticals. Carbene and nitrene group-transfer reactions are particularly valuable for these purposes because they enable the rapid construction of complex scaffolds and the introduction of functional groups into complex molecules. However, selective, intermolecular, group-transfer reactions are challenging to achieve. Biocatalysis with artificial metalloenzymes is an increasingly promising approach to meet this challenge, and the development of artificial metalloenzymes has enabled a wide variety of C–H functionalization and olefin cyclization reactions by group transfers. P450 enzymes have been used for these transformations, due to the presence of a highly evolved, enclosed active site that provides greater activity and selectivity than the rigid and/or shallow binding pockets common in other metalloenzymes. As a result, unnatural intermolecular reactions are usually accomplished with this class of enzyme. However, accomplishing new transformations with P450 enzymes is typically limited to directed evolution of a natural P450 enzyme or further evolution of an evolved variant. Because current P450 enzymes with porphyrinoid cofactors do not catalyze group-transfers between many synthetically valuable combinations of carbenes or nitrenes and alkenes or C–H bonds, this limitation impedes the application of P450 enzymes to synthetic problems. Thus, new strategies are needed to redesign P450 enzymes for binding unnatural cofactors and new classes of substrates. One possible strategy is the de novo design of new enzymes. Methods for de novo design are increasingly powerful, and it is now possible to generate complex tertiary structures that bind to any desired reactive complex. However, it remains challenging to design de novo active sites with the high activity and selectivity of natural enzymes. We propose to use de novo protein design strategies to redesign cytochrome P450 enzymes to incorporate new cofactors and substrates, while retaining the natural P450 active site. This redesign will allow us to develop group-transfer reactions that are difficult to achieve with current P450 enzymes. We will follow two approaches to test this hypothesis. First, we will use a diffusion model to design a new cofactor binding motif for incorporation of an unnatural piano-stool iridium cofactor. Because this cofactor is more active than porphyrin cofactors for acyl nitrene transfers, this approach will allow us to evolve highly active en- zymes for olefin aziridination and C–H amidation. Second, we will use sequence redesign to stabilize an enzyme that possesses an active site that can accommodate large molecules but that has been too unstable to evolve for unnatural reactivity. Together, our approach for the development of artificial metalloenzymes will combine the flexibility of de novo design methods with the highly evolved active site of natural P450 enzymes. In doing so, we will accomplish the first use of a non-porphyrinoid cofactor within a P450 enzyme and enable the late-stage diversification of molecules by selective nitrene and carbene group-transfer reactions.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The global impact of the COVID-19 pandemic has been profound, with nearly 800 million infections and seven million deaths worldwide. With the majority of fatalities resulting from respiratory failure, there is a pressing need to understand the factors that promote respiratory dysfunction following infection. The current paradigm centers on immunopathological mechanisms behind disease, but fails to incorporate how sensory neurons change during infection and contribute to the hallmark immune dysregulation and airway inflammation that characterize severe COVID-19. Airway sensory neurons are robustly activated by inflammatory mediators, pathogens and noxious irritants to modulate respiratory tone and initiate defense reflexes like cough, mucus production and bronchoconstriction. They also regulate the immune response to pathogens in the lung and become aberrantly activated during airway inflammation, thereby exacerbating reflex symptoms that contribute to morbidity. However, whether they play a role in the pathogenesis of coronavirus disease has not been studied, despite the potential to impact our understanding of airway physiology and identify new avenues for treatment. This proposal will test the following hypotheses: 1) that lung-innervating sensory neurons regulate the immune response to promote airway inflammation in coronavirus infection and 2) during infection, these cells undergo functional changes that contribute to respiratory dysfunction. Using the MHV-A59 mouse coronavirus infection model, our preliminary data support this hypothesis by demonstrating that the systemic ablation of TRPV1+ sensory neurons improves airway function while attenuating mortality and disease in infected mice. I will expand on these results by performing a specific, targeted ablation of the lung-innervating sensory neurons to elucidate their role in coronavirus disease pathology. I will determine if lung-innervating sensory neurons regulate airway inflammation by learning and performing spectral flow-cytometry to profile and quantify pulmonary immune cells, while separately measuring viral titer, neuropeptide and inflammatory cytokine concentrations in the lungs of infected, airway sensory neuron-ablated and intact mice. To understand how MHV-A59 infection modulates airway sensory neuron function to promote respiratory disease, I will acquire expertise in two-photon calcium imaging and electrophysiology to characterize their phenotypic changes in MHV-A59 infected animals. Lastly, I will measure oxygen saturation, breathing rates, respiratory mechanics and airway hyperreactivity in sensory neuron-ablated and intact mice to understand how these cells contribute to respiratory dysfunction. These studies will define the contribution of lung-innervating sensory neurons to coronavirus disease pathogenesis and lay the groundwork for discovering new treatments for respiratory disease.

NIH Research Projects · FY 2025 · 2025-09

Investigating the Role of Transcription Factor Snail1 in Astyanax mexicanus Heart Regeneration (7) PROJECT SUMMARY/ABSTRACT With heart disease consistently being the leading cause of death globally each year, exploring heart regeneration across models has been a promising avenue for revealing how these abilities were evolutionarily gained or lost. Astyanax mexicanus provides a rare intra-species comparison of rapid heart regeneration loss between two morphotypes that diverged as recently as 20,000 years ago. While the river-dwelling surface fish fully regenerate amputated heart tissue within 64 days, the cavefish develop a permanent fibrotic scar with no tissue replacement. The transcription factor snai1b was found to be upregulated in cavefish compared to surface fish hearts in bulk RNA sequencing data. An upregulation of Snail post-heart injury was associated with increased fibrosis and decreased cardiac function in mice, where the Snail locus was labeled with histone lactylation, an epigenetic modification that correlates with open chromatin in migrating cells. Variable food and oxygen availability between the isolated caves and open river habitats caused a metabolic divergence, with cavefish preferring glycolysis over oxidative phosphorylation (OXPHOS). A similar shift in preference from OXPHOS to glycolysis occurs in migratory stem cells during epithelial-to-mesenchymal transition (EMT). This shift increases lactate and consequently lactylation of EMT-related genes, including snail. After migration, cells undergo a mesenchymal-to-epithelial transition (MET) to reintegrate into the repaired tissue. However, since both OXPHOS and glycolysis decrease in cavefish upon injury, snail levels may remain elevated and MET may not occur. I hypothesize that intrinsically raised glycolysis in cavefish leads to the lactylation and activation of snail, increasing fibrosis and EMT without MET after heart injury. This interdisciplinary approach will combine studies of metabolism, gene regulation, and morphogenesis to understand the ability to regenerate cardiac tissue. Aim 1 will spatially survey glycolysis and OXPHOS post-ventricular amputation through immunohistochemistry (IHC), then pharmacologically target these processes and observe the effects on regeneration. Aim 2 will utilize IHC, ATAC sequencing, and CUT&RUN to characterize lactylation and gene regulation in injured cave and surface fish hearts, followed by inhibition or induction of lactylation and evaluation of regeneration. Aim 3 centers on tracking EMT via IHC and using chemical inhibitors to suppress EMT and encourage MET to assess their roles in A. mexicanus cardiac regeneration. This project aims to uncover why some vertebrates can regenerate their hearts while others cannot. By comparing metabolic shifts, gene regulation, and EMT following injury within the same species, I will examine the role of the transcription factor Snail in driving differential heart regeneration abilities, and uncover potential treatments to recapitulate regenerative phenotypes in a non-regenerative animal. Support from the F31 will enable me to not develop technical skills such as confocal microscopy, pharmacological trials, and bioinformatics, but will also provide me with opportunities to participate in international conferences and share my findings with the community.

NIH Research Projects · FY 2025 · 2025-09

1 PROJECT SUMMARY 2 Successful development of new active pharmaceutical ingredients (APIs) requires synthesizing many 3 structurally related compounds to optimize pharmacokinetic properties related to absorption, distribution, 4 metabolism, excretion, and toxicology. However, the expense of time and resources needed to synthesize each 5 candidate makes API optimization a bottleneck. As a result, researchers have developed a relatively short list of 6 expedient synthetic methods on which they rely to develop APIs. These methods tend to involve fragment 7 couplings that forge new C-C, C-N, or C-O bonds by joining two building blocks, which allows those seeking new 8 APIs to purchase libraries of suitable building blocks and explore their pairwise couplings in modular fashion. 9 The modularity of this approach means that each new fragment increases the quantity of structures that can be 10 explored in a nonlinear fashion. However, some of the most frequent substructures in APIs—saturated medium- 11 sized nitrogen-containing heterocycles—are not well represented in commercial catalogues of building blocks, 12 due to limitations in state-of-the-art synthetic methods used to produce them. The ability to create custom building 13 blocks in a modular fashion, which would enable both more thorough and more efficient structure optimizations 14 of APIs, would represent a significant advance in modern synthetic chemistry. However, current synthetic 15 limitations make exploration of 3D structural variants of these heterocycles difficult. 16 This proposal outlines a strategy to develop new catalytic methods that will convert readily accessible starting 17 materials into structurally complex heterocyclic building blocks in modular fashion. The modularity of the 18 proposed methods, the accessibility of starting materials, and the proposed catalyst control of stereochemistry 19 in these chiral products will make exploration of new, chiral variants of these important building blocks more 20 practical. The proposed research includes a tandem fragment coupling-cyclization approach to assemble 21 multiple building blocks to construct structurally complex, medium-sized, saturated heterocycles. Mechanistic 22 experiments and computational exploration of key mechanistic steps is proposed so that both activity and 23 selectivity of the catalysts can be understood and iteratively improved with the assistance of new data 24 representations and machine learning. Accomplishing these goals would provide practitioners access to diverse 25 structural variants of important building blocks for API development, as well as demonstrate the future role of 26 machine learning as a tool that can accelerate the development of methods with a large scope.

NIH Research Projects · FY 2025 · 2025-09

Diabetes-related exacerbations are among the most common and expensive reasons for potentially preventable hospitalizations. Health-related social needs (HRSNs) such as housing, food, and support to pay for utilities impede diabetes care management and result in preventable hospitalizations due to diabetes- related exacerbations. Regional initiatives aimed at improving public health, social care, and health care system alignment and screening and referral for addressing HRSNs have high potential to improve diabetes care management, behavioral health, and reduce racial and ethnic disparities. From 2018-2022, the Centers for Medicare and Medicaid Services (CMS) supported the implementation of the Accountable Health Communities (AHC) Model in multiple geographic regions to strengthen clinic-community linkages and address HRSNs through resource navigation. We advance evidence by using a natural experiment of CMS AHC implementation in 13 geographic regions to estimate the impact of the AHC model on care management among adult Medicare, Medicaid, and dually-enrolled adult beneficiaries with type 2 diabetes and behavioral health (alcohol misuse, physical activity, and smoking). The project will identify how the AHC model impacted diabetes care management and behavioral health and how the social connectedness and area-level social determinants of health (SDoH) of AHC model regions impacted diabetes care management, behavioral health, and racial and ethnic disparities. A difference-in-differences (DiD) design will be used to account for time- invariant differences between counties with and without the AHC model. Propensity score methods will be paired with DiD regression models to account for differences in beneficiary characteristics that differ in the pre- AHC (January 1, 2012 to December 31, 2017) period and AHC implementation (January 1, 2018 to December 31, 2022) period. We hypothesize that the CMS AHC model (1) improved diabetes care management and behavioral health, (2) reduced racial and ethnic disparities in diabetes care management; and that (3) social connectedness and area-level SDoH will partially explain variation in the AHC model’s impact on diabetes care management and racial/ethnic disparities between geographic regions. The rationale for the proposed research is that, once it is known how the AHC model impacted diabetes care management and behavioral health, the evidence can inform future policies to support regional infrastructure and interorganizational collaboration to address HRSNs and diabetes-related disparities. Our long-term goal is to ensure that adults with type 2 diabetes receive coordinated, integrated care for their medical, behavioral, and social needs. Strengthening resource navigation and systems alignment can support the wellbeing of adults with type 2 diabetes. The proposed project has high potential to close evidence gaps about the impact of regional initiatives that aim to improve systems alignment, diabetes care management, and behavioral health.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The motivation to seek food is one of nature’s strongest driving forces and is essential for the survival of any species. Mammals often show an increased motivation to consume calorie-dense food, which is evolutionarily favorable as it allows them to store excessive calories in the form of fat to cope with future food scarcity. Many humans, however, live in conditions where food is readily available and abundant, and the evolutionary drive to take advantage of calorie-dense foods can become maladaptive and promote obesity. The role of the neuropeptide neurotensin (NTS) in feeding and obesity has been studied for decades; however, largely based on its expression in the lateral hypothalamus. We found that NTS is highly expressed in a central node of the brain’s reward system, the lateral nucleus accumbens (NAcLat). NAcLat neurons project to the ventral tegmental area (VTA) and we have previously demonstrated that optogenetic stimulation of the NAcLat→VTA pathway promotes reward-related behaviors. Yet, the function of NTS in the NAcLat→VTA pathway is unknown. In preliminary experiments, we found that optogenetic stimulation of NAcLat terminals in the VTA strongly increased hedonic feeding in mice that are kept on a regular chow diet, but not in mice that are on a high-fat diet (HFD). While the feeding produced by optogenetic stimulation of NAcLat→VTA neurons was restored when HFD mice were placed back on a regular diet for an extended time, optogenetic stimulation of NAcLat→VTA neurons never affected feeding behavior of regular chow. Importantly, optogenetic induced hedonic feeding behavior was prevented by infusion of an NTS receptor antagonist into the VTA suggesting a potential role for NTS in the NAcLat→VTA pathway for hedonic feeding behavior. Based on our preliminary data, we propose to study NTS signaling in the NAcLat→VTA pathway in the context of hedonic feeding behavior and determine pre- and post-synaptic mechanisms of how prolonged HFD may affect this circuitry to promote behavioral and metabolic adaptations. To do this, we have established a collaboration with Lin Tian (Max Planck Florida), who has developed a novel neurotensin sensor that allows us to measure NTS release in vitro and in vivo. We will also collaborate with the lab of Csaba Földy (University of Zurich) to perform patch-seq experiments and analyze diet-induced adaptations in gene expression and cellular physiology. Because many functions of the brain reward circuitry are conserved between mice and humans, our work to understand diet-induced changes in the mouse brain will provide an important foundation for the development of future obesity treatments.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Human cardiac injury, such as a heart attack, leads to irreparable damage and life-long heart complications. Developing translational strategies for inducing heart repair has been limited to laboratory accessible models such as the zebrafish and mouse. Using the zebrafish, which can regenerate their heart after substantial injury, we have previously shown that neural crest-derived cardiomyocytes promote injury-induced proliferation of surrounding cardiomyocytes by re-activating developmental gene networks after injury. Importantly, genetic ablation of neural crest-derived cardiomyocytes leads to a failure of regeneration and a large scar. Now knowing the importance of neural crest-derived cardiomyocytes and re-activating developmental networks, many questions remained unanswered on how these networks are re-deployed after injury and if these networks remain silenced in human hearts after injury. Our current hypothesis is that human neural crest-derived cardiomyocytes are unable to redeploy developmental gene networks after injury and are therefore unable to induce repair mechanisms. Until recently, assessing gene regulatory dynamics in human-derived cardiac tissues was not possible. Now, self-assembling cardiac organoids derived from human pluripotent stem cells have presented a new avenue for exploring cardiac repair in human-derived tissues; however, these cardioid models do not contain cardiomyocytes derived from neural crest. Here, we propose to (i) assess the dynamic chromatin landscapes of the regenerating zebrafish heart using single cell ATAC-seq to unravel critical components necessary for re-activating developmental programs that control cardiac regeneration in the zebrafish, (ii) interrogate the reactivation of developmental programs in a human-derived cardioid model after injury using a multiomics approach, and finally, (iii) use next-generation CRISPR-based functional genomics screens to identify gene circuits responsible for “repair impairment” of human neural crest-derived cardiomyocytes. Ultimately, our goal is to combine gene regulatory network information from zebrafish repair circuits and our human-derived screen to identify optimal targets for potential intervention using any relevant therapeutic modality for driving cardiac repair in vivo post-injury.

NSF Awards · FY 2025 · 2025-09

This I-Corps project is based on the development of an intelligent maritime navigation system that helps ships avoid fuel-wasting ocean conditions by optimizing routes in real time. The global shipping industry spends over $150 billion annually on fuel, with more than 20% lost due to inefficient routing through encountering waves. This technology offers a solution using autonomous, low-cost ocean sensors to collect real-time wave data and deliver smart route guidance through an intuitive software application. The system enables up to 6–10% fuel savings per trip, potentially cutting $10–14 billion in costs annually and reducing global emissions by 3%. This saving is equivalent to removing 12 million cars from the road. The technology may provide real-time marine data that may benefit cargo shipping companies, marine tour operators and soil barge operators as well as smaller operators such as yacht and tour boat companies. Additionally, scientists, the offshore wind industry, and the ocean wave power sector, may benefit from using these predictions and models. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a real-time, artificial intelligence (AI)-powered ocean forecasting and routing system for maritime navigation. Despite advances in ocean modeling, limited real-time wave data and sparse satellite coverage continue to hinder routing accuracy and fuel efficiency. This technology integrates ocean data acquisition with machine learning-based forecasting to overcome these limitations. At its core is a fleet of autonomous ocean drones (sailbots) powered by wind, wave, and solar energy. These drones collect high-resolution wave, wind, and ocean health data across wide areas and operate as a low-cost, self-powered mesh network for real-time data sharing and distributed learning. The sailbot’s affordability and modularity support economical multipoint deployment at scale. The system uses AI to accelerate complex wave simulations without sacrificing accuracy, enabling predictive routing optimized for safety, fuel use, and comfort. Users benefit from improved operational efficiency and enhanced safety. Beyond navigation, this high-resolution, distributed data platform has applications in offshore wind forecasting, weather pattern modeling, and autonomous maritime logistics, which are sectors that increasingly rely on localized, real-time ocean intelligence for informed decision-making and resilient operations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This I-Corps project focuses on the development of a wearable biosensor that continuously measures molecular biomarkers in the human body. Current approaches to biochemical monitoring rely on infrequent sampling and lab-based analysis, which miss rapid changes and provide limited insight into dynamic physiological processes. The technology introduces a new sensing platform that captures real-time biochemical data noninvasively and with high sensitivity. By enabling continuous monitoring, the technology supports advances in health tracking, early disease detection, and personalized care. This approach promotes scientific progress and public well-being by making biochemical information more accessible, timely, and actionable. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of electrochemical biosensors that use structure-switching deoxyribonucleic acid integrated with custom low-power, low-noise complementary metal-oxide-semiconductor circuits. The sensors convert molecular binding events into electrical signals, allowing real-time quantification of multiple biomarkers at relevant concentrations. The platform includes wearable and point-of-care formats designed for small sample volumes and wireless operation. This innovation advances biosensing by combining biochemical specificity with scalable electronics, enabling new applications in longitudinal health monitoring and mobile diagnostics. 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.