Tufts University

NSF Awards · FY 2025 · 2025-06

This project makes American Medical Directories (AMDs) data accessible to social science researchers by extracting, formatting, and geolocating data from these sources. It demonstrates the utility of these data by exploring a set of sociological hypotheses, and sustainably archives them for future generations. The AMDs, periodically published by the American Medical Association from 1906, present an immense opportunity to illuminate the organizational dynamics of professional medicine in the early 20th century. In addition to physicians’ names and practice locations, these volumes also contain valuable information about individuals’ training histories and medical specializations, demographic characteristics, and membership in state and local societies. Because AMDs were published triennially, they also present an opportunity to link individuals over time, exploring physicians’ movement between regions, as well as how and where training pipelines for the medical workforce developed. No other data source offers such nuanced, individual-level information about the early medical workforce, yet the AMDs remain underexplored archival sources, largely due to the difficulties of extracting large quantities of data from original archival sources. Additionally, public-facing outputs and activities of the project bring its utility to a broader audience, enabling still further kinds of non-academic inquiries and applications. This project centers on a critical period of history in professional medicine and captures significant demographic and cultural moments that likely interrupt previous patterns, including the Flexner Report, Great Migration, and World War I. Specifically, the project: 1) delivers a novel, publicly-accessible, longitudinal physician database, extracted from purposively selected editions of the AMD between 1906 and 1938; 2) links these records to other relevant historical data sources, including decennial Censuses, leveraging existing NSF-funded data infrastructures; 3) tests specific sociological hypotheses related to practice markets, growth patterns, physician migration, and specialization to demonstrate the dataset’s utility; and 4) engages broader audiences through data visualization, media, and public events. The project enables interdisciplinary quantitative and mixed methods analyses that previously were limited by the inaccessibility of AMD records, and provides a “gold standard” version of extracted records that could be used in future efforts to extract data from additional AMD editions. By building a novel, longitudinal dataset of the medical workforce between 1906-1938, the project provides a through-line from the early days of professional medicine in the US, and, critically, makes a trove of physician-level data accessible to social science researchers and the broader public. This project is jointly funded by the Sociology Program and the Human Networks and Data Science-Research (HNDS-R) Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Ensuring software is secure is a fundamental challenge in today's technology-driven world. To improve software security, software development best practices recommend that developers begin development with security in mind by following a structured process called "threat modeling". Threat modeling is a structured brainstorming process where developers review a system's parts, asking what could go wrong and how they could fix it, identifying threats and mitigations, respectively. There are many recommended threat modeling processes, but it is not clear which are best. Developing guidelines and support for this essential process requires understanding relevant human decision-making and collaborative problem solving. There have been some efforts to study threat modeling practice, but these have either been very expensive or the authors have had to make design decisions that reduce study costs but potentially limit result reliability. This project includes experiments comparing experimental design approaches to assess their effects. This approach will help future researchers design reliable threat modeling experiments while minimizing study cost. The resulting best practices will be shared with threat modeling researchers and incorporated into professional education as well as integrated into courses in security and software systems engineering. This project will increase threat modeling research reliability by empirically evaluating best practices and tradeoffs for experiment design. Researchers are undertaking qualitative studies and controlled experiments in four areas: (1) investigations of current threat modeling practices in real-world settings, (2) experiments assessing the impact of task design, such as, the level of system specification detail, (3) experiments assessing the study environment's effect, including participants' security expertise, and (4) comparisons of measures used to assess threat modeling performance. The results are being combined into research guidelines for human-centric threat modeling, which can be used as a reference for future researchers to help them develop more reliable results to improve threat modeling practice and software security generally. 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

Pichia pastoris is a food-safe yeast. It is widely used as a model organism for basic biological research and in a variety of biomanufacturing applications. Nevertheless, how to maximize the productivity of this microbe is not well understood. This project will focus on understanding how this yeast allocates its resources for protein synthesis. The results can be used to maximize productivity. The outcomes could enable significant advances in food, biomedical, and scientific applications of this yeast. Optimizing transcription and translation initiation rates to enhance recombinant protein biosynthesis have been well-studied. Controlling translation elongation has not. Using specific codon usage patterns to regulate translation elongation will be evaluated. The relative supply-demand constraints of individual codons will be assessed. This will inform coding schemes that co-maximize expression and host fitness/robustness, advancing the idea of “host-aware” synthetic biology. Outcomes will show how codon choice in heterologous genes can be exploited to simultaneously enhance expression level and minimize competition with native essential genes, which in turn enhances cellular fitness and volumetric productivity. This work leverages the expertise of the Nair lab (in systems & synthetic microbiology) with that of Adlakha lab (in engineering fungal secretion systems) and Rode lab (in chemical biology). The Nair lab will focus on optimizing production of cytosolic proteins, the Adlakha lab will focus on secreted proteins, and the Rode lab will optimize immobilization of secreted proteins for cell-free biocatalysis. Work will proceed in parallel in India and the US. Exchange visits for trainees are planned to further strengthen collaboration and co-mentoring. This project involves a collaboration between researchers from the United State and India. It is jointly supported by the US National Science Foundation and the Department of Biotechnology of the Government of India (NSF-DBT). 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

In an increasingly digital world, the preservation of information security and privacy becomes more challenging yet remains essential to assure. First and foremost, private information is increasingly stored on computing systems and networks, and secondly such systems face a growing array of hardware and software level vulnerabilities. These vulnerabilities may be further exacerbated by the rising capabilities of machine learning algorithms, quantum computing, and the increasing sophistication of malicious actors. This research project will pioneer novel types of secure hardware technologies which are fundamentally rooted in optics and photonics rather than digital electronics. This research will advance our fundamental understanding of how to design and implement such technologies and will ultimately enhance our ability to construct and deploy safer and more secure computing systems in the future. Additional benefits to this research include the training of skilled future workers in science, technology, engineering, and math disciplines – particularly through developing expertise in the semiconductor industry and aiding workforce development in this technologically and strategically important field. This research project will investigate the use of integrated silicon photonics for realizing new types of ‘physical unclonable function’ (PUF), an important hardware security primitive which can form the basis for security applications such as secure key generation, storage, and exchange. Specific goals of this research project include: (1) Investigating the performance and information capacity limits of unclonable photonic circuits. Mapping and understanding trade-offs in the photonic PUF design space relating to the degrees-of-freedom, bandwidth, footprint, fabrication sensitivity, environmental sensitivity, design approach, and measurement technique. (2) Developing fundamental techniques for extracting and/or generating digital key material and signatures from the physical properties of photonic circuits and PUFs and the photonic signals or spectra they create. (3) Exploring dynamic optoelectronic PUFs based on tunable photonic integrated circuits. Quantifying enhancement in information capacity and studying stable vs. unstable regimes of key generation, storage, and recall. Pioneering new concepts relying on optoelectronic feedback to blur the boundary between the optical and electrical hardware to further enhance security and versatility for optical key generation. The proposed research will advance our knowledge in the design, implementation, characteristics, and phenomena associated with an emerging class of low symmetry photonic structures and circuits based on moiré crystals and quasicrystals – and do so in a platform with great technological significance. Beyond the technical contributions, this research promises broader societal impacts by fostering the development of innovative hardware security technologies. These advancements will help counter emerging security threats, such as those posed by quantum computing, machine learning, and malicious actors within unsupervised supply chains, thus promoting societal well-being and security. This project is jointly funded by ENG/ECCS/CCSS program and the Established Program to Stimulate Competitive Research (EPSCoR). 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 GreenFjord-FIBER project is a scientific study that uses high-tech measurements to understand how Greenlandic glaciers flowing into the ocean change between winter and summer. The investigators are focusing on the points where the ice meets the ocean. With special sensors measuring vibrations and temperature, the investigators aim to measure where and when icebergs detach from the glacier and fall into the ocean. The investigators also want to find out where warm ocean water is melting the ice and if there is a connection to the speed at which the glacier flows into the ocean. The measurements will help us learn more about how Greenland's glaciers will change in the future and what that might mean for sea levels around the world. In collaboration with a local Greenlandic teacher-trainee, the scientific results of the study and their implications for local communities will be disseminated in Greenland’s classrooms. The proposal aims to test the hypothesis that a regime change in melt source regions occurs during the spring-to-summer transition period, shifting from temperature-dominated melt and calving to sub-glacial discharge-dominated processes. To test this hypothesis, the investigators will deploy a new optical fiber instrument at the calving front of the Qajuuttap Sermia tidewater glacier in Greenland. This instrument will measure acoustic and temperature data to better understand the effect of sub-glacial melt and discharge on calving. The project will generate novel measurements from Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) systems, making a compelling proof-of-concept for glacier-ocean interface observations, an environment that is notoriously treacherous for equipment and personnel. The inclusion of broadband instruments on land, seismometers on the ocean bottom, and conductivity, temperature, and depth (CTD) casts from boats will provide important benchmarks against the measurements from DTS/DAS. The project will support two early-career researchers, one postdoc, and an undergraduate in a University of Washington hosted summer research program. In collaboration with a local Greenlandic teacher-trainee, the scientific results of the study and their implications for local communities will be disseminated in Greenland’s classrooms. 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

Sea-level rise will affect millions of people in coastal communities within the next several decades. Accurate predictions of how quickly it will rise is challenging because it depends on many different processes and how these processes interact with and feedback on each other. One process that may play a surprisingly large role is the effect of small swirls and eddies (only a few feet across) of warm water that control the rate of ice melt at the near-vertical cliff faces of the world’s marine-terminating (tidewater) glaciers. At these glaciers, ice flows directly into the ocean and melts underwater or calves icebergs. Melting of the ice produces freshwater that flows out near the ocean surface and drives a return flow that draws in deep warmer ocean water toward the glacier. According to current theory, increasing the rate of ice melt increases the strength at which warmer ocean water is pulled in towards the ice face, which further enhances the melting. The details of this process - particularly the small-scale dynamics near the ice face - have never been measured because the calving ice cliffs are too dangerous to make measurements. Here we propose to use a highly specialized underwater robot (a remotely operated vehicle, or “ROV”) with state-of-the-art optical and acoustic instruments to observe the melt rate and the processes that control it. One of the novel aspects is the use of “melt stakes” - 6 ft long rods that will be driven into the glacier face by the ROV and monitored continuously to determine the melt processes. These stakes then provide a frame of reference for our ROV to make a suite of detailed measurements of the shape of the glacier face, the dynamics of the currents adjacent to it, and how the ice-water interface evolves. At the same time, we will observe the local ocean environment in the fjord - the currents, salinity and temperature - which are the main ingredients we need to predict ice melt in larger-scale and climate models. Our analyses will combine field data with a high-resolution fluid-flow model that recreates the conditions along the ice with realistic water properties. The combination of model and data will be used to refine our melt predictions and verify these directly using our observed measurements. At the end of the project, we will be able to extend our results to estimate how much melt is occurring for tidewater glaciers around the globe, and how this may change in time. Beyond this importance to society and the scientific community, this grant provides broader impacts across several levels: (1) mentorship and support for two early career women (2) support for three graduate students in interdisciplinary ice-ocean studies, (3) experiential opportunities, funding, and mentorship for 45 senior-year undergraduate students, whose capstone projects will directly contribute to this project while being supervised by our gender and culturally diverse team of engineers and technical staff, (4) classroom experiments showing buoyancy and convection to engage K-12 students and the general public, and (5) two teams of high-school women will additionally be involved and make observations through Girls in Icy Fjords expeditions. Melting at the ice-ocean interface of marine-terminating glaciers influences the rate of mass loss from the world's ice sheets. In addition to contributing to sea-level rise, details of the melt process dictate the depth at which fresh meltwater enters the ocean (which in turn affects ocean circulation on a variety of scales) and alters calving rates. Existing theory suggests that the rate of submarine melting along these ice faces is set by the strength of subglacial discharge. However, recent observations find unexpectedly high melt rates over broad sections of glacier termini, even outside discharge plume areas. The observed order of magnitude discrepancies between observed and predicted melt rates suggests the presence of energetic dynamics elsewhere along the ice face that drive near-ice turbulent flows. We hypothesize that this discrepancy arises from differences in the rate-controlling physics within the boundary layers. Current turbulent transfer coefficients were derived from stable boundary layers. Yet on vertical glacier ice faces, boundary layers have strong buoyant forcing and marginal stability that likely produce dynamics not captured by laboratory or idealized models. Because buoyant meltwater fluxes provide kinetic energy for near-boundary outer flows -- and because enhancement of those flows leads to enhanced melting -- there is potential for strong positive feedbacks in the dynamics. As a result, small errors in the melt parameters or the parameterization functional form can have significant consequences to the total melt calculation. No studies have yet to make observations immediately next to near-vertical ice faces, or measure melt dynamics with the resolution necessary to investigate these dynamical feedbacks. This grant supports the development of a first-of-its-kind network of coordinated underwater acoustic, optical and in-situ unmanned sensors to be deployed at LeConte Glacier, Alaska. Using methods that meld glaciology, oceanography, and robotics, these systems will collect the first geophysical observations of the turbulent boundary layer at a near-vertical glacier face. Specifically, we will directly measure velocity, salinity and temperature through a buoyancy-forced near-vertical boundary layer and relate these to observations of the subsurface ice morphology (e.g., slope, roughness) across several spatial scales. By combining these data with high-resolution realistic simulations, we will characterize the dominant contributions to boundary layer turbulence and explicitly relate these to local melt rates. Our ultimate goal is to determine what parameters need to be measured (e.g., fjord u,T,S) over what time and space scales, as well as what assumptions can be made in order to connect dynamics from the small-scale ice interface to the large-scale ocean and glacier forcing. This grant builds an observational capacity that does not exist at present. Measurements will span a sufficient range of the parameter space (in ocean temperature, velocity variance and ice morphology) for us and others to test existing and advance new melt models that underlie many ice-ocean community models. This award is co-funded by the Arctic Natural Sciences Program and the Physical Oceanography Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Non-Technical Summary: Electronic devices rely heavily on semiconductors, materials that act as electronic switches, allowing the movement negative and positive charges (electrons and holes) only under certain conditions. Inorganic semiconductors like silicon and gallium nitride, which do not contain carbon, face some important disadvantages: they are rigid, heavy, and unsuitable for large area-production. They also have limited adjustability in their properties. In response to the challenges that these materials face, organic semiconductors" have achieved commercial success, particularly in OLED televisions and displays. Despite these advances, chemists and materials scientists still struggle with designing molecules with improved properties. This challenge is caused by the difficulty in predicting how organic molecules will arrange themselves into a crystal lattice because of the large number of weak interactions (non-covalent interactions) between the molecules. Prof. Samuel Thomas at Tufts University and Prof. Mu-Ping Nieh at the University of Connecticut are collaborating to address this challenge through this project, supported through the Solid-State and Materials Chemistry Program in the Division of Materials Research. The investigation deepens understanding of how strategically designed non-covalent interactions between organic semiconductor molecules can influence their arrangements in crystal lattices, and the properties that result from these arrangements. Furthermore, their research seeks to enhance control over the properties of organic semiconductor materials by combining different types of interactions between molecules. Beyond addressing a fundamental hurdle in the design of organic semiconducting materials, the project also broadens participation in STEM through by supporting undergraduate research students each summer the Visiting and Early Research Scholars Experience (VERSE) program at Tufts. Technical Summary: The basis for this research, supported through the Solid State and Materials Chemistry Program in the Division of Materials Research, is the hypothesis by Prof. Samuel Thomas at Tufts University and Prof. Mu-Ping Nieh at the University of Connecticut that the electrostatic force between fluorinated and non-fluorinated groups in these molecules plays a critical role in determining these arrangements. The first part of this project focuses on improving fundamental understanding of how chemical structure can influence the likelihood of observing cofacial interactions between fused heterocyclic ring systems, which are common in high performance organic conjugated materials, and fluorinated arene pendants. The PIs are testing the hypothesis that that cofacial electrostatic complementarity determines whether ArF-ArH interactions occur in crystal structures of a series of arylene-ethynylene molecules with fluorinated benzyl ester pendants, even while dispersion interactions comprise most of the interaction energies. The second phase of this project focuses on determining the extent to which adding different discrete non-covalent interactions, such as hydrogen bonds or chalcogen bonds, can reinforce or preclude these stacking interactions. To accomplish this interdisciplinary project, the PIs combine their expertise in organic synthesis and design, X-ray crystallography, powder X-ray diffraction, optical spectroscopy, and computational chemistry. By advancing fundamental knowledge in this area, this research develops new strategies for designing next-generation organic optoelectronic materials. Additionally, the project broadens participation in scientific research by supporting two visiting undergraduate students from a diverse applicant pool in their initial research projects each summer. This includes comprehensive support with professional development modules, social events, and housing accommodations to create a supportive and inclusive research environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. This Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by addressing interactions among social systems, natural environment, and built environment and is within the Resilient Infrastructure NNA focus area. Critical infrastructure services (CISs), such as water, transportation, energy, communications, public health, and waste, are essential for the well-being and economic livelihood of Alaskan communities. However, providing these services is challenging due to the extreme and changing climate, as well as the remote nature of hub communities and Alaska Native villages. We do not currently understand how CISs are interconnected in Arctic communities; however, we do know that these interconnections are sources of both resilience and vulnerability. Furthermore, the different CIS organizations are complex, are responsible for people’s lives and safety, and have characteristics that we must understand further. This project explores how CISs support each other (e.g., increasing broadband in rural Alaska enables telehealth) and create challenges (e.g., local supply chains often delay infrastructure repairs). Unlike most rural communities in the contiguous US, in Alaska, the air service network is denser than the roadway network. In turn, hub communities—i.e., communities that can be reached by commercial airplanes or ports—are critical for surrounding, remote villages as they provide services including delivery of fuel and workforce support. This project considers the interface between urban hub communities and neighboring rural Alaska Native villages, exploring how challenges in hub towns cascade to villages. In collaboration with three hub communities in Alaska, the interdisciplinary research team integrates systems engineering, organizational sciences, civil engineering, and public health to improve the provision of CISs, not only benefiting the towns themselves, but also Alaska Native villages. This project aims to architect infrastructure interdependencies within hub communities and at the interfaces between urban hubs and rural Alaska Native villages. In doing so, this work paves the way for future research by providing new empirical data and creating a set of management approaches that can help communities immediately improve their CISs. Leveraging semi-structured interviews with CIS stakeholders, operational data collection, and collaborative stakeholder workshops, Phase 1 of the project identifies and maps CIS interdependencies in hub towns. This phase uses fuzzy cognitive mapping and causal loop diagrams to bring together stakeholders’ expertise and perspectives. Phase 2 analyzes how the provision of CISs in hub towns cascade to neighboring villages by assessing end-users’ perceptions towards their infrastructure services and how they use services in hub towns. End-users’ perspectives are captured through semi-structured interviews and transportation demand surveys. As CIS organizations in Arctic communities are complex, and are responsible for people’s lives and safety, they are considered High Reliability Organizations (HROs); thus, this project uses HRO Theory to provide a solid bridge between social and technical issues. Phase 3 evaluates the organizations involved and provides stakeholders with a concrete assessment of where they stand and how they can become more robust organizations, and thus a more resilient system of organizations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project transforms engineering education by leveraging "meaningful failure" as a promising approach to learning and teaching. Failure is an inherent part of human life and learning processes, and early failure is often prerequisite step on the path to successful learning. However, typical engineering education currently punishes failure, which disincentivizes innovation, exploration, and risk-taking, ultimately resulting in engineers who are less prepared to tackle complex global challenges. By understanding students’ unique experiences during moments of academic failure, this project supports students taking risks and learning from setbacks, developing the skills and mindsets to embrace failure as a meaningful experience in their learning. Our research involves the use of biometric data, observations of classroom dynamics, and psychosocial assessments to better understand how each student experiences failure on a physiological, cognitive, and social level. We will use these data to develop new educational tools and strategies that will provide immediate, tailored interventions connected to individual student needs and experiences. This research will support the development of a workforce ready to persist past ubiquitous failure experiences in engineering to address tomorrow’s challenging engineering problems. Further, this research aligns with the goal of creating inclusive and equitable learning environments that can adapt to the diverse needs of all students. The project will explore meaningful failure in engineering education contexts by developing personalized learning strategies and pedagogical tools. The proposed research has three goals: identifying real-time failure profile signals, understanding how learners' responses to failure are individualized, and determining necessary changes in pedagogy and assessment to support personalized responses tolearning from failure. The research involves a multi-pronged data collection approach, including laboratory experiments using video and biosensing modalities (EEG, EDA, ECG), classroom observations, surveys, and interviews with educators and administrators. A convergent team from five institutions, with expertise in cognitive neuroscience, learning sciences, AI, and psychosocial theories of learning and development collaborate to create individualized failure profiles. These profiles will integrate multi-modal data sources to formally represent each learner’s unique cognitive, affective, and behavioral responses to failure. The project will culminate in the development of pedagogical tools and strategies to support personalized learning and resilience – increasing retention and success rates in engineering fields and pioneering a shift in engineering education towards valuing learning from failure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Computational Geometry is the theory of algorithms for geometric problems. Since many of the computations associated with both real and simulated physical systems are geometric in nature, research in computational geometry has been fueled by applications in numerous fields, such as computer graphics, geometric modeling, molecular biology, sensor networks, engineering design, robotics, machine learning, machine vision, data mining, and statistics, to mention just a few. All of these areas are at the forefront of current research activity in computational geometry. The Fall Workshop on Computational Geometry is a two-day meeting of researchers, mostly junior. It is a no-fee meeting whose reach can be thus larger than the major conferences. The Fall Workshop in Computational Geometry series was originally established in 1991 and has since then been held almost every fall. This award will support around 30 US-based students attending the 31st Annual Fall Workshop on Computational Geometry, November 15-16, 2024, at Tufts University, Medford, Massachusetts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The tremendous growth of wireless data traffic over the past decades is expected to accelerate even more in future due to increasing demands for high-speed wireless connectivity, ubiquitous network access, and end-user experience. Sub-terahertz (THz) communications, defined as above 100 GHz, are envisioned as a key technology to enable the needed wireless terabit-per-second links by leveraging the hundreds of gigahertz bandwidths available at sub-THz bands. A major challenge in sub-THz bands, caused by higher propagation loss with increasing frequencies, is the limited communication distance. An emerging technology that promises to improve wireless coverage is the active reconfigurable intelligent surface (active-RIS) that consumes low power and provides efficient control of the reflected signals in both phases and amplification. Realizing this potential will require substantial research in hardware design and prototyping of wideband RIS operating above 100 GHz, as well as novel communication and network algorithms for active-RIS-aided wideband systems, together with experimental evaluation and validation of such unique sub-THz networks with active RIS. This project focuses on the 142 GHz frequency band as a front-runner for the first sixth-generation (6G) spectrum to be allocated above 100 GHz and a top choice for future Wi-Fi spectrum allocations in the years to come. The project consists of three intertwined thrusts. The first thrust is to design and prototype a wideband liquid crystal-based RIS with a wide angular range of tunable reflection operating at 142 GHz. Starting with a design for passive RIS as the proof-of-concept at this high frequency, an active RIS design will then be realized using amplifier-integrated LC-based substrate-integrated waveguide, enabling high tunability for each RIS element. The second thrust is to design robust and efficient algorithms for optimal control of the active RIS coefficients including frequency-dependent phase shift and amplitude amplification. Novel algorithms leveraging unsupervised graph neural networks and reinforcement learning will be used to capture the underlying network interaction and to provide strong scalability and generalizability. The third thrust is to perform extensive validation using the NSF-funded open-source ray-tracing simulation tool “NYURay” for active-RIS-aided sub-THz channel simulations. In addition, the prototyped passive and active RISs will be used to conduct on-site wireless propagation measurements utilizing the wideband sliding correlation channel sounder to create a site-specific hybrid channel model for RIS-aided communication. Through various education and outreach activities to broaden participation in computing, this project will foster knowledge sharing and contribute to industry and regulatory advancements in THz communications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In order to be successful, engineers need to work in diverse collaborative teams. However, it is common for inequities to arise in teamwork that hurt the performance of the team. These inequities can take many forms. One team member might have fewer opportunities to share ideas. Another team member might be interrupted more often. Yet another team member might receive fewer or different tasks to complete. These types of inequities hurt team dynamics and student learning. There are technological tools available to help teams address these problems. Existing tools include online teamwork support and careful team-building. These strategies can be effective but only work before or after teams interact. Teams need in-the-moment support, especially if they are struggling. Social robots may be able to provide that support they need with models of equity. Past research in social robotics has defined equity as all members participating equally. However, a team with equal participation might be very inequitable. For example, one team member could be rejecting another team member’s ideas when they speak. This project will create an improved model of equity that can be used with a social robot. The robot will then be able to make student engineering teams more equitable. Overall, this project will help make student engineering teams more effective. It will also improve our understanding of what makes student engineering teams fair. This project helps increase the pool of engineers who can contribute to society. The knowledge gained from this research may benefit team interactions with different teams. This could have strong impacts on diversity, equity, and inclusion across STEM fields. Collaborative teams are common in the modern engineering workplace. Learning how to work well in a team is a critical skill for engineers to learn since they take on complex problems. One common challenge teams face is inequities in communication and task allocation. Engineers need to learn how to address inequities in their teams in order to be successful. Some teamwork tools already exist to support teamwork and address inequities. However, these tools rely on team members’ opinions and do not provide real-time feedback. In this project, the research team will design a social robot to promote equity in teams. This social robot will be able to observe interactions in the team to detect inequities. Then, the social robot will intervene during team meetings to address the inequities. First, the research team will observe human engineering design teams. They will use these observations to build data-driven models to detect inequities. Next, the research team will build a machine learning model to determine when a robot should intervene in a team. This model will choose when the robot should intervene based on the inequities it can detect. Also, the research team will explore what behaviors a robot can express to promote equity. These robot behaviors are new and have not yet been tested on robots. Finally, the research team will integrate the computational model with the tested robot behaviors. They will test the integrated system in an initial small-scale user study. This study will show the positive influence of the robot’s ability to promote equity in teams. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by developing and implementing professional development for Learning Assistants (LAs) to improve their facilitation of student learning. Learning Assistants are undergraduate students who support student group work in an active learning STEM class in which they have prior content experience. In addition to their presence in STEM classes, they also participate in weekly preparation meetings with the instructional team and in a pedagogy course. There is strong evidence that the implementation of LAs benefits student learning in STEM classes, particularly for students from marginalized groups. The LA pedagogy course is the flagship of the LA model that distinguishes it from other near-peer teaching models. However, pedagogy course lessons are often based on research in K-12 classrooms, as research on facilitation of student learning at the college level is only recently emerging. To address this mismatch of applying K-12 facilitation strategies at the college level, this project plans to implement a new LA pedagogy course sequence based on evidence from recent LA research to support LAs facilitating in student-centered, diverse, and international ways that benefit student learning across a variety of STEM disciplines. The goals of the project are focused on design and implementation of the pedagogy sequence (Goal 1) and research of the impact of the pedagogy sequence (Goal 2). Goal 1 encompasses development of LA-facing materials, instructor-facing materials, and alternative designs for greater flexibility for adopters. The project aims to make the pedagogy sequence applicable to diverse settings and benefit many racially marginalized students’ learning in STEM classes within and, through dissemination, beyond the institutions involved in this project. To achieve this, the project will implement the pedagogy sequence in three different types of institution: a public Hispanic-Serving institution, a private predominantly white institution, and a public Predominantly Black institution. Collaboration with an Advisory Board will focus on an extension to the two-year college environment. Through dissemination to the 571 institutions with LA programs in the LA Alliance, the materials created will reach many LAs and correspondingly impact many more students. Goal 2 includes investigation into how the pedagogy sequence changes LAs’ reflections and their facilitation practices, how this change impacts student learning in STEM classes, and how the STEM class environments influence LA learning. Within Goal 2, the project also intends to identify critical elements of the pedagogy sequence. The project plans to use this knowledge for research-based revision of the pedagogy sequence. This project is the first to study change of LA facilitation practices through LAs’ experience in the pedagogy course and their concurrent practice in STEM classes. While previous work has looked at LAs’ uptake of language from pedagogy course concepts, the impact on their actual practice in STEM classes has not been studied to date. Through a sociocultural framework and data source triangulation including documentation of the implementation, surveys, interviews, and recordings of LA-student interactions in STEM classes, the project intends to gain rich qualitative insight not only into change in LA practice, but also into the impact it has on student learning in various STEM disciplines. When LA programs include richer and more contextually relevant professional development for LAs, this will make LA programs more impactful in supporting student success. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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 2024 · 2024-10

This Faculty Early Career Development Program (CAREER) project will support research to create a new class of made-to-order, 3D-printable soft robots with the capability to tolerate levels of structural deformations that would disable conventional approaches to computing and control. The goal of the project is to be able to rapidly design, fabricate, and deploy a highly customized fleet of robots to respond to unique and urgent missions. These robots would be able to, for example, navigate small, winding spaces in cave systems or debris fields, as might be required for robotic search-and-rescue or exploration. The synergistic use of mechanical intelligence, embedded fluidic circuits, and flexible electronics will enable these new robot capabilities. Mechanical intelligence - the use of robot geometry and material properties to adapt to unexpected conditions - can significantly reduce the amount of computing capacity needed for the robot to accomplish its goals. Fluidic logic uses the movement of fluid in flexible channels within the robot body to convert signals from contact sensors and other external stimuli into commands that turn robot actuators on and off. Fluidic logic can be directly built into the robot body and bend and twist without losing function. Finally, communication and control functions that are best performed electronically will be implemented using flexible and stretchable electronics with a high tolerance for dynamic deformation. The resulting robots will be able to implement sophisticated functionality, while undertaking severe shape changes as needed, to traverse otherwise inaccessible spaces. Comprehensive educational activities incorporate and complement the research, including a new hands-on undergraduate course on printable robotics, and an outreach program to public high schools in Worcester County. This project will create robot architectures that can be quickly 3D printed using additive manufacturing techniques, to produce inexpensive robots that can crawl, jump, swim, and dive through confined spaces, and which can be rapidly customized to incorporate mission-specific details. The research goal is to 3D print robots with integrated fluidic state machines that respond to fluidic sensors and control fluidic actuators. A new class of complementary fluidic logic gates and electro-fluidic memory elements will be developed from multi-stable flexing beam structures with integrated linear actuators and fluidic tubing. Flexible electronic circuits and electro-fluidic interconnects will be integrated into the robots using conductive inks and elastomers. The role of electronics will be minimized and limited to selecting fluidic functionalities, functionalizing fluidic sensors and actuators, and writing programs into fluidic memory. The program will deliver a comprehensive robot architecture for terrestrial, underwater, and amphibious robots, including designs, fabrication processes, modeling and control methodologies, and software. The project will maintain a continuously evolving robot component library and will seek to build a community of researchers and potential users by sponsoring a sequence of increasingly challenging benchmarking scenarios inspired by the Tham Luang cave rescue in Thailand in 2018. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. Amidst the global shift toward green and sustainable urban transportation systems, substantial investments have been made in infrastructure to facilitate the adoption of electric vehicles (EVs) in the United States. The placement of an EV charging station (EVCS) potentially has significant broader impacts on peoples' mobility, activity patterns, and visitation to nearby businesses during charging sessions. This provides an opportunity for policy makers to support local businesses (e.g., cafes, restaurants, grocery stores), particularly small and medium-sized enterprises, which play a pivotal role in maintaining community health, especially in vulnerable communities. This SAI project tackles the question of how and where to best place EV charging stations to ensure they not only meet the needs of drivers but also boost the economic resilience of small businesses and promote social equity. The project integrates theory and methods from computational social science, urban resilience, behavioral science, and complex systems to address a pressing societal need -- the equitable, resilient, and sustainable deployment of EVCSs. This project leverages large-scale datasets including mobile phone GPS, charging station usage data, and real-world intervention experiments to understand the broader social and economic impacts of EVCS placement on mobility, social dynamics, and the resilience of businesses. This complex systems approach introduces a new paradigm of infrastructure development and management that significantly extends the scope from individual behavior to social and economic community-wide effects, offering a more comprehensive understanding of the EVCS ecosystem. The optimization and visualization platform will enable agencies and businesses to evaluate hypothetical deployment scenarios, promoting a multi-dimensional approach to infrastructure design. The open-source and public-facing platform ensures that its benefits are not confined to the academic realm but are extended to diverse community stakeholders, reinforcing the project's commitment to inclusive and comprehensive urban 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 2024 · 2024-09

Ambiguity, uncertainty, and confusion (AU&C) are inherent in human experience, at small scales and large, in everyday social interactions and in professional expertise, in activities ranging from students collaborating on a physics problem to a team of EMTs responding to an accident or a physician determining a diagnosis from imprecisely communicated symptoms. Despite its prevalence, we lack systematic understanding as to how people engage with AU&C. This is not surprising. The ways in which humans manage these processes as individuals and in groups involve highly complex phenomena lasting from minutes to years, characterized by changing information, social context, and immediacy. Studying AU&C by collecting and analyzing data in controlled and natural settings is currently not possible at the scale required to make progress in this domain. Addressing this gap is a Grand Challenge requiring the insight of experts from the social sciences, data sciences, and engineering. This project focusses on AU&C in the context of science, technology, engineering, and mathematics (STEM) education. Current educational practices frame AU&C as liabilities to avoid, thereby inducing stress and often behavioral impasses when encountered. Laboratory and classroom research have shown that AU&C can be framed as an exciting and motivating challenge with additional positive effects on learning traditional content. Motivated by such results, these researchers will study the dynamics of AU&C during STEM learning in individuals, small groups, and classes over broad time scales. This project will provide fundamentally new capability and insight into the dynamics of AU&C in STEM environments Bringing together experts in Learning Science, Cognitive Science, Modeling Sciences (math, signal processing, statistics, and machine learning), and Engineering (systems and sensors), these researchers will undertake a convergent approach to understand productive engagement with ambiguity, uncertainty, and confusion (AU&C) as a target in STEM learning and encourage engaging with AU&C in problem solving. The team will develop methods for collecting and analyzing longitudinal data from a diverse population of students using a multimodal data approach that includes behavioral, linguistic, and physiological sensing. Advanced wearable sensors will be developed to inconspicuously collect data for long periods of time, thus allowing for observation of the impact of AU&C on physiological states. Lab studies will examine AU&C in problem solving in controlled settings by individuals and groups. Data from the classroom experience of a larger group of students that includes the lab cohort will be collected for their first two years. The researchers will build computational systems adapted to the needs of storing and accessing large heterogenous collections of human subjects’ data. Working collectively across domains, they will construct state of the art machine learning and natural language processing methods to interpret these data and model AU&C dynamics at the individual, small group, and classroom scales within STEM learning environments. Ultimately, this project will create new methods to study real-time, in-context learning using large language and dynamical models to fuse multimodal data. Results from this work promise to revolutionize how students and trainees learn to recognize and engage productively with complex AU&C. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

We seek to reveal how fish achieve rapid maneuvers, a capability that surpasses even the most advanced robotic systems. Our research focuses on a central hypothesis: that fish use their muscles to dynamically control their body stiffness, the resistance to bending, and, more crucially, damping, the resistance to the speed of bending, a phenomenon that enables them to navigate complex and unpredictable aquatic environments. To test this hypothesis, we will conduct experiments on swimming fishes and measure the mechanical properties of their bodies and isolated muscles, alongside parallel tests using a custom biorobot platform. This synergy between biological and engineering approaches will help us understand whether fish execute fast accelerations and rapid turning maneuvers by dynamically modulating body damping and stiffness. This research will also deepen our understanding of how fish maneuver, including their behavior, the biomechanics of their bodies, and how they interact with the water around them. Our findings will also help us understand how different fish species are specialized for different swimming and adapted to different environments. Beyond fundamental understanding, our research will pave the way for developing extremely agile biorobots, unlocking complex missions previously inaccessible, such as nearshore environmental monitoring, detailed inspection of underwater offshore infrastructures, and non-intrusive studies of ocean biodiversity. By integrating biological insights with robotic design, our research will engage the public and educate future scholars from K-12, highlighting the shared physics underlying fish movement and biorobot design. Fish can turn and accelerate faster than even the most advanced biomimetic robots. Prior work has attributed this extreme agility to the ability of fish to modulate their body stiffness (the resistance to bending), but this has produced limited results in biorobotics. We argue that the modulation of damping (the resistance to the rate of bending) is crucial for performing extreme maneuvers. Preliminary data from mathematical models and swimming experiments suggest that fish cannot achieve agile maneuvers without muscle-induced damping modulation. We plan to examine this modulation by conducting experiments on swimming fish and parallel tests using an advanced biorobot. In vivo swimming experiments will measure swimming performance and muscle behavior, which will be used to perform in vitro tests for measuring muscle power and body flexibility. Biorobotic experiments will measure the effects of dynamic tuning of body damping on maneuvering performance, energy dynamics, and fluid flow patterns. By integrating our robotic and biological findings, we aim to demonstrate that dynamic damping is essential for the extreme maneuverability crucial to the survival of fish. This interdisciplinary research not only paves the way for developing highly maneuverable biorobots but also inspires future BioDesign innovators to move from simple biomimicry to innovations grounded in biological and physical principles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project will create functional modules, controlled using different colors of light, to enable new capabilities for "biobots" -- that is, robots fabricated from biological cells -- including the ability to split apart or join together on command. Specific biobot behaviors will be modeled and designed using predictive numerical simulations, advanced in parallel with the new modules. These new capabilities will be validated through an ambitious sequence of increasingly complex and demanding tasks. For example, a star-shaped biobot might separate into multiple linear biobots to travel through small holes in a barrier and reach an objective, or multiple biobots might join together to form a larger biobot that can manipulate and transport a comparably sized object. While the tasks in this project are intended as a proof of concept, biobots such as these have potential for specialized resource gathering, or for sequestration of hazardous waste. The biobots also serve as an ideal platform for outreach and education in the area of biorobotics. This project uses biophysical simulations and machine learning to design shapeshifting machines in silico and a novel fabrication method to realize them in vitro. The simulations and computational design method significantly decreases design-manufacture cycle times and reduces biomaterial waste. The biofabrication approach enables modularity of form and function, as well as shape change, across several length scales. This also enables the generation of arbitrary geometries with specific topological, geometrical, material, sensory, and motoric arrangements, greatly expanding the morphological and behavioral complexity of biobots. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award funds some of the research activities of Professor Ken D. Olum in the Tufts Institute of Cosmology at Tufts University. Cosmic strings are microscopically thin or possibly even fundamental objects of cosmological length which may or may not exist in our universe. If they exist, they may play a role in the generation of dark matter. Detection of cosmic strings would provide a window into fundamental physics at energies beyond the reach of any accelerator. Observation of a cosmic superstring network could provide a confirmation of the correctness of string theory. The best hope for discovering a cosmic string network is through observation of gravitational waves. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) and other pulsar timing arrays have found strong evidence for a gravitational wave background formed by many sources throughout the universe. These sources could be pairs of giant black holes, but they could also be cosmic string loops. Professor Olum will study the gravitational waves that would be emitted by cosmic strings and compare them with current and forthcoming NANOGrav observations. His work will advance the national interest by promoting the progress of science in the recent and rapidly growing field of gravitational waves and their possible sources, and in learning about fundamental physics at very high energies. Professor Olum will involve graduate students and postdoctoral researchers in the work and thus train future generations of research physicists. The work will further connect studies of the universe with studies of the fundamental laws of nature. More technically, Professor Olum will use the gravitational wave spectra that he and his group have derived from a realistic population of cosmic string loops evolving under gravitational self-interaction to compare with pulsar timing observations from the NANOGrav collaboration (of which he is a full member) and the international pulsar timing array. He will develop a new technique for simulation of axion strings using the Kalb-Ramond formalism, and so improve the calibration of axion dark matter densities. He will study the velocities acquired by loops in reaction to anisotropic gravitational wave emission, called "the rocket effect", to determine how much cosmic string loops cluster in galaxies, which is important for other possible observational mechanisms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

It is now recognized that injury to the brain- such as from traumatic brain injury (TBI), is a result of a wide variety of stresses, strains, and strain rates. However, it is not yet known how astrocytes respond to strains across a range of force and strain rate regimes because of challenges associated with access to the tools with the ability to apply small and large forces, at defined distances from astrocytes, in real-time on live cells, with control over strain rates. In this project the investigating team will use controlled, 3D, brain-like cell culture environments and their recently developed ex vivo brain cultures to quantify acute versus chronic response of astrocytes to mechanical perturbation. The project will culminate in defining how astrocytes can activate, permanently or reversibly, in response to low, repeated forces to gain better understanding of how repeated injuries, even at low forces, could put people at risk for brain injuries and long-term disability or death. The fundamental knowledge gained through this research could apply to materials design for better protective gear, monitoring devices for TBI, and better public understanding of forces that could lead to permanent brain damage. Outreach objectives in this project include continued efforts from the Principal Investigators' laboratory to expand engineering research opportunities tailored for high school students that are typically underrepresented in science and engineering. Astrocytes are one of the most important cell types in the brain. There is increasing interest in studying astrocytes, as emerging studies point to their ability to repair the brain after injury. Understanding how astrocytes repair the brain holds potential towards new class of drugs, preventative measures, or protective gear so that people could fully recover from, or even prevent, brain damage. So far, researchers lacked the tools to effectively grow astrocytes in the laboratory or apply the types of injuries to astrocytes that represent what a person would experience during a brain injury. Injury to the brain, such as from traumatic brain injury, is a result of a wide variety of forces and impacts. Traumatic brain injury is diagnosed in nearly 1.5 million Americans every year, and 5.3 million people live with disabilities caused by traumatic brain injury in the U.S. This project will study how astrocytes respond to injury, in real-time, with live cells, in brain-mimicking environments, and with precise control over the injury forces applied. The research team will use a brain hydrogel environment specifically tailored to grow astrocytes and use a new technique to injure living cells and study their response in real-time. These studies have the potential to transform our understanding of how astrocytes sense and respond to force, across a wide range of strains and stain rates. The research will advance understanding of how brain injury occurs and how it leads to long-term cognitive deficits. This award will support outreach efforts to provide opportunities for female high school students, particularly those from historically excluded groups in STEM, to give them biomechanics and mechanobiology related research opportunities, what is necessary to be a successful female engineer, and how local women have used science and engineering to forge a successful career. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Non-technical Abstract: This award will support a planning workshop at the NSF and follow up online meetings, entitled “NSF Division of Materials Research - Biomaterials Workshop”. This proposal requests funding to support an online workshop to identify new directions for biomaterials research and emerging trends in the field. This planning meeting was catalyzed by BMAT in DMR to provide a roadmap for future directions for investment. Inputs based on contributions from the scientific community, as well as consideration for the evolution and revolution in the biomaterials field over the past decade, will be utilized to capture emerging trends and opportunities with biomaterials. The proposed program will involve three phases: (a) An initial collection of topics and inputs from the broader community via an on-line crowd sourcing exercise facilitated by BMAT/DMR, followed by a compilation of these ideas to further focus the set of topics identified in this proposal, for discussion by a subset of experts in the field that will also serve as reporters to drive field defining research on these topics. (b) A one day in person workshop at the NSF with the leads/reporters for each of the main topics to discuss the topics and plans, followed by a series of online zoom meetings as focused short sessions to refine the vision and organize more details and a roadmap to guide the field into the future. (c) A written compilation of the findings into a workshop report for the NSF and the broader community, as well as a summary for publication in a peer-reviewed journal. The process will funnel key fundamental and emerging topics into a blueprint for the NSF Biomaterials Program to help guide investments into research over the next decade. Workshop participants will include active researchers with diverse backgrounds in terms of topical area of expertise in biomaterials, career stage, institution, geography, gender, and ethnicity. While the majority of participants will be from academic labs, industry and government representatives will be invited and play key roles related to biomaterial needs and opportunities for collaboration. These efforts will help ensure the conclusions of the workshop are representative of the biomaterials community as a whole. Technical Abstract: Biomaterials are a foundation for structures, environmental and human health, and interfaces connecting biological components to inert or responsive materials to achieve new features and enhance functional outcomes. These features are derived from a fundamental foundation derived from the physical, chemical and biological sciences, combined with principles from engineering. Biomaterials continue to evolve in new and important ways, via new designs, new sources, new methods of synthesis, new processing methods, and new directions and systems to impact all aspects of human existence on the planet. These evolving and innovative directions for the field of biomaterials continue to probe, control, and achieve new and more refined structures and functions to enable useful interfaces with the biological world. We have identified some initial key topics as starting points for the planning workshop, including Sustainable Biomaterials, Instructional Biomaterials, Advanced Biomaterials, Modeling Biomaterials, Genetic Approaches for New Biomaterials, and Advanced Biomaterials Processing. Embedded in these topics are considerations that integrate the latest advances in biomaterial chemistry and characterization with AI, ML, new genetic tools and related evolving fields. The discussions, reporting and outcomes will be captured in a formal report to the NSF, as well as via peer-reviewed publication for the broader community. The steering committee will report the findings to the NSF, to help catalyze further interest, focus and guidance to program managers as well as future grantees. The workshop report will also serve as a useful tool for industry, foundations and other programs to identify workforce development opportunities, new educational initiatives and related opportunities focused on biomaterials impact for the future. We anticipate the document catalyze cross-governmental and inter-governmental opportunities for investments to continue to grow the field related to the central and fundamental role for biomaterials in all aspects of environment, human functions, and the health and well-being of the planet. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This project organizes and hosts the 2024 Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Principal Investigators (PI) Meeting. The meeting is planned for August 12-13, 2024, at the Le Meridien in Charlotte, NC. CSSI PI meetings provide a forum for PIs to share technical information about their projects with each other, NSF program directors and others; to explore innovative topics emerging in the software and data infrastructure communities; to discuss and learn about best practices across projects; and to stimulate new ideas of achieving software and data sustainability and ensuring a diverse pipeline of CI researchers and professionals. PIs also provide valuable feedback to the program on emerging opportunities and challenges. This year we expect to see the effect of AI on NSF research community and the potential for new cyberinfrastructure. This year we will use 3 poster sessions to enable every attending PI to communicate their research results towards the goals of the CSSI. We will also have 3 panels focused a) on the proposed development of AI Research cyberinfrastructure through the National AI Research Resource (NAIRR ) initiative, b) on ensuring technical and operational sustainability of CSSI supported cybertools, and c) on the development of workforce for these. Breakouts for participants to discuss and provide concrete feedback will complement the posters and panels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The 2024 Cooperative Election Study (CES) is a collaboration of over 50 different university research teams throughout the United States. Collectively this group designs and fields a large sample survey of at least 50,000 American adults. The survey measures demographics, opinions and attitudes, and voting behavior in national and state elections. The very large sample size allows researchers to have sufficient data to study state electorates as well as the entire nation. The survey is used to study who votes and why, and what explains the choices that voters make. Since its inception, the CES has involved more than 100 different research teams and hundreds of faculty and student researchers, and it has conducted interviews with more than half a million American adults. The data from this project are used widely by researchers, journalists, and members of the public to understand American elections and public opinion. Few, if any, data infrastructure ventures in the discipline can boast participation from as many institutions and scholars as CES has facilitated. The survey helps to create and sustain a network of researchers interested in state and national elections, survey design, and public opinion. The 2024 CES survey is developed by a consortium of research teams. Each research team that wishes to be involved in the project purchases a 1,000-person sample survey from the same firm. Each individual team determines half of the questions on its survey. The other half of the content (Common Content) is created by the PIs in collaboration with scholars from the participating teams. Common Content consists of questions that every team would like to measure or questions that are of broad interest and require a very large sample. The project, thus, fields as many surveys as there are teams and also produces a single large sample survey that consists of the Common Content. The survey will be fielded over the Internet, with samples constructed to be nationally representative. Each team will receive the data from its own 1,000-person survey and a dataset consisting of the 50,000+ observations from the Common Content survey. Survey data are validated using voter validation and through comparisons of state level election results to the survey results from the subsamples for each state. The study will also include a separate dataset which will be a re-interview of respondents who participated in both the 2020 and 2022 CES studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Predictive modeling can help practitioners in many fields make high-stakes decisions in a data-informed way. For example, given ultrasound images of the heart, can a model detect valve disease well enough to help physicians recommend follow-up care? Using historical data, can a model help budget-conscious public health agencies prioritize which neighborhoods would most benefit from interventions to reduce opioid overdoses? While machine learning has shown some preliminary progress at such tasks, practitioners often lack the ability to train models to achieve their specific goals. Today’s off-the-shelf methods are often constructed to be easy to train, but this can compromise decision quality when choices made for ease are not aligned with stakeholder goals. This project will develop “decision aware” methods that make it possible to train models to directly satisfy stakeholder goals in several health applications. When detecting heart disease, methods will limit the fraction of alerts that can be false. When predicting opioid overdose events, methods will focus on identifying high-risk neighborhoods. New methods will adapt model size automatically to the available data and be designed to work even when there are few expert-labeled training examples. This award will support the cross-disciplinary training of PhD students and provide immersive research experiences to undergraduates at Tufts University. The project team will publish software and reusable educational modules to help others use decision-aware methods. The project will advance the theory and practice of training probabilistic models for consequential decisions across three directions. First, decision-aware learning methods will ensure that training objectives can be matched to the intended decision-making task, not just a proxy that is more convenient for gradient descent. For binary classifiers, the team will use carefully constructed bounds and stochastic average gradient methods to achieve desired constraints on false discovery rates or false positive rates. For spatiotemporal forecasting of opioid overdoses, stochastic smoothing methods allow training models that can suggest where to intervene by prioritizing a top-k subset of high-risk areas. Second, new limited supervision methods will ensure success even when expert-derived labels are scarce by leveraging easier-to-acquire unlabeled data, even if it differs from the labeled data. The project team will benchmark existing semi-supervised and self-supervised methods and develop decision-aware extensions that are robust to uncurated unlabeled data. Finally, the project will develop methods that can adapt the size of decision-aware latent variable models automatically to available training data, eliminating the expensive grid searches needed to select model sizes in common practice. Technical innovations will focus on sparse approximations and amortizations that can scale model size beyond what is possible with off-the-shelf code today. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Accounting for the diversity of life on Earth requires an understanding of the speciation process. New species are thought to arise as populations diverge and accumulate multiple barriers to interbreeding, such as a change of mate choice, a shifted mating time, and reduced survival of hybrid offspring. A critical question concerns the specific processes and mechanisms underlying the tendency of distinct barriers to build up between populations and operate together during speciation. This project explores three alternative mechanisms, and by focusing on a familiar agricultural pest of sweet corn, agricultural sustainability grows out of the project’s research activities. By recruiting and training a variety of undergraduates to participate in these activities, the project will enhance students’ research skills and open pathways to science and STEM careers. The project’s central objective is to systematically analyze the role of selection, population processes, and genetic architecture for the build up of distinct barriers to gene exchange in the European corn borer moth system. First, the researchers will conduct genome resequencing from across the distribution range to explore the history of species spread and the potential for co-occurrence of distinct barriers through range expansion and population mixing. Second, through population cage experiments and fitness assessments, the project will determine if prezygotic isolation traits also contribute to postzygotic isolation. Third, genomic comparisons of population pairs differing by independent or coincident barriers will be used to detect evidence that associations between barriers are favored by selection. Finally, the researchers will use CRISPR/Cas9 technology to investigate pleiotropic reproductive isolation. This research aims to contribute not only to an understanding of speciation but also to practical applications in agricultural sustainability, environmental health, and educational outreach. Collaborative sampling with growers supports economic profitability and promotes resilient local food systems. Passive monitoring offers early warnings of moth infestations, aligning with integrated pest management principles to manage pests while prioritizing overall ecosystem health. Lastly, outreach initiatives, including a secondary curriculum for a national agriculture education program, aim to spark curiosity about environmental biology and advance evolution literacy. 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.