Massachusetts Institute Of Technology

NIH Research Projects · FY 2026 · 2025-04

Breast milk is rich with bioactive components that are critical to an infant’s development. It is highly recommended that infants ingest breast milk; but, fluctuating maternal hormones and substandard post-parturition health directly mediate breast milk production. Maternal ingestion of small molecule drugs further compounds decreased breast milk synthesis and secretion, and adversely compromises breast milk quality. Although the majority of actively breastfeeding women consume medication or receive therapeutics, small drug molecule transport from maternal plasma to synthesized breast milk remains largely unknown. Important strides in understanding pharmacokinetics in milk-producing mammary glands have yet to occur because of the lack of engineered bioinspired mammary lobe systems that mimic complex in vivo signatures—topographical lobule microcurves, spiked levels of lactogenic hormones, cellular landscapes, and mechanically-driven lobe expansion and contraction. The objective of this proposal is to determine if our established microengineered mammary lobe system, which integrates key physiological characteristics, i.) faithfully mirrors multifactorial breast milk synthesis processes and ii.) could be employed as a versatile screening testbed for evaluating drug and therapeutic safety during lactation. The project is based on the central hypothesis that exogenous stimuli that reflect in vivo mechanisms, such as hormone levels, dynamic mechanical lobe stimulation, and passive transport of small drug molecules, will potentiate differential cellular landscape phenotypes and lead to unique content differences in engineered breast milk. This could develop a new in vitro preclinical model that promotes the cognizance of drugs or therapeutics that are safe to ingest or receive during lactation. We believe this contributes to improving important women’s health issues. Our hypothesis will be tested through the following two aims. Aim 1 will develop a 3D mammary lobe model and determine how in vivo relevant parameters alter physical and molecular mammary cell phenotypes, and regulate the secretion of important breast milk components. Aim 2 will investigate the pharmacokinetics of small molecule drugs or therapeutics that passively diffuse into the engineered breast milk. Nicotine or mRNA encoding for SARS-CoV-2 will serve as a model drug or therapeutic, respectively. We will pursue these aims using an innovative combination of analytical and adaptable techniques from engineering and biological sciences. These include the development of a scalable lobe model, by which the application of physiologically relevant stimuli and compartments can mimic breast milk synthesis and drug distribution. The engineering approaches that we leverage will develop foundational resources for the ongoing efforts revolving lactation and post-parturition health research. The expected outcome of this work will highlight the importance of engineering new microsystems for in vivo mimicry. These platforms can facilitate clinical translation of rapid drug and therapeutic safety screening. The results will have a significant positive impact to women and will encourage the ongoing efforts to support women during their breastfeeding journey.

NSF Awards · FY 2025 · 2025-04

Driven by global energy challenges and advancements in renewable energy, modern grids increasingly integrate variable power sources and electrical loads. The increasing use of batteries, solar and fuel cells necessitate efficient and dense power conversion over a wide regulation range. This CAREER project aims to improve the size and efficiency of buck-boost power converters by leveraging advancements in wide-bandgap semiconductor technology. Moreover, instead of conventional inductive-based energy processing, we will leverage the increased energy density of capacitors to decrease passive component size and switch stress. These approaches result in a framework of condensed buck-boost converters capable of dramatically increased power density and efficiency across a wide range of operating conditions, enabling greater integration of renewable energy systems and improving drivetrains for future electrification of transportation. This work will also bring advancements in power conversion into the classroom with a curriculum focused on next-generation power conversion and lab-based learning opportunities. This project brings reaches out to the broader community through collaboration with the proposing institution's museum, in an effort that involves development of a demonstration on a vision for the future grid and integration of renewable energy, providing children and adults with hands-on experiences related to power and energy systems. In this work, we leverage novel monolithic bidirectional switches, which can replace two back-to-back devices with one device capable of bidirectional voltage blocking. We plan to perform careful calorimetric characterization to better model and design with these devices. Moreover, this work focuses on capacitor-based topologies, due to their increased energy density; however, capacitor loss modeling under realistic operating conditions is also necessary to take advantage of the improved performance. When combined, these models will enable better design and optimization of the proposed family of buck-boost converter topologies. We will also develop improved figures-of-merit related to reliability and volume to more accurately compare these potential topologies with conventional approaches. By designing several hardware prototypes to validate our models and comparisons, we will also tackle practical implementation challenges related to using monolithic bidirectional switches in multilevel topologies, including commutation loop design, gate drive, thermal design, and control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-04

Project Summary Language experience plays a critical role in language development across childhood. However, in studying humans, it is difficult to disentangle the effects of language experience from non-linguistic factors that also change across childhood and adolescence. Recent advances in AI have led to the emergence of large language models (LLMs), which not only are exceptional at producing language, but also share remarkably similar representations with the adult human brain. Building on my extensive prior training in cognitive neuroscience and language development, the proposed research will combine precision functional neuroimaging and computational modeling to systematically test the role of language experience in the development of the brain’s language network. Specifically, I will apply model-to-brain similarity approaches that have been developed in work with adults to characterize developmental changes in how language is represented by the human brain, and test whether LLMs trained on age-matched amounts of language data recapitulate developmental shifts in the linguistic representational space. First, I will use functional magnetic resonance imaging (fMRI) to measure brain responses to a diverse set of sentences in young children (N=12, ages 6-8), older children (N=12, ages 10-12), adolescents (N=12, ages 14-16), and two groups of adults (N=8, ages 20-22; and N=8, ages 28-30). Next, I will train an LLM on different amounts of language data, corresponding to the estimated amount of language experience across the lifetime in each of these age groups. Finally, I will test the critical hypothesis that representations from an LLM will best align with neural representations of the corresponding age group. The proposed research will support my fellowship goals of gaining proficiency in the use of computational modeling, especially LLMs, as a tool in cognitive neuroscience research, and continuing to develop expertise in state-of-the-art fMRI data collection and analysis methods. An additional goal during the fellowship period is to develop practical skills to prepare for being a principal investigator. Dr. Evelina Fedorenko’s lab at MIT, and the department of Brain and Cognitive Sciences more broadly, is the optimal place to conduct both the proposed research and my postdoctoral training: on a lab-, department-, and institution-wide scale, there is enthusiasm, expertise, and ample resources to apply AI and modeling approaches to scientific inquiry. I will take advantage of opportunities within MIT and the greater Boston area’s academic environment to attend multidisciplinary talks, engage in coursework with experts particularly in computational tools, and collaborate with other researchers in related fields.

NSF Awards · FY 2025 · 2025-03

Graphs encode relationships between items. Data in the form of graphs is widespread across the sciences, technology, and everyday life. Examples include social networks indicating friendships between individuals, protein interaction networks capturing compatibility of proteins, and flight networks containing links between airports. Graph data has the potential to play an integral role in machine learning systems, yet it remains unclear how to best make use of this data. The goal of this project is to develop a mathematical foundation to guide the principled use of graph data in a wide variety of machine learning tasks. Due to the basic nature of the research, potential downstream applications of new algorithmic insights span a multitude of applied domains; an example is improved therapeutic drug discovery through accurate predictions of drug efficacy with significantly fewer costly and time-consuming experiments. The project involves the mentoring of both PhD students and undergraduate researchers. The results generated from this project will be integrated into advanced undergraduate and graduate courses on probability and algorithmic statistical inference. Many real-world networks are accurately represented by graphs with underlying geometry: for instance, in a social network, the nodes are associated to a list of features (e.g., the individual's location, age, hobbies, personality, etc.), and edges between pairs of nodes are formed as a function of their unobserved feature vectors. The project develops principled methodology for optimally exploiting structure in the latent space underlying a graph in order to carry out machine learning tasks. The first of three project components characterizes how the geometry of the underlying space affects structural or combinatorial properties of the graph. The second component studies the problem of recovering latent feature vectors from an observed graph, and aims to derive optimal algorithms for a wide variety of latent space graphs. The third component investigates semi-supervised learning with graph data, and aims to find computationally efficient algorithms achieving the best possible prediction accuracy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This Faculty Early Career Development Program (CAREER) award supports research and education focused on the foundations of the next generation artificial intelligence (AI) for engineering design. It focuses on establishing deep generative models (DGMs) that are designed to handle challenges specific to engineering design at different scales, complexity, and disciplinarity. Engineering-focused DGMs hold the potential to quickly generate product designs, impacting quality, cost, and innovation across nearly all major design and manufacturing applications, from automotive and aerospace engineering to sustainable energy solutions and smart infrastructure, while democratizing product creation. The broader impacts of this research include significant advancements in how products are designed and manufactured, leading to reduced production costs, enhanced product customization, and quicker time to-market—factors critical to national economic competitiveness and prosperity. The intellectual merit of this CAREER award advances first-principle theories in DGMs by integrating three key areas: (1) The creation of deep generative modeling frameworks, specifically diffusion models, that utilize historical optimization data. This research advocates a shift from the traditional DGM focus on solely optimized designs to learning from the entire evolutionary trajectory of designs, thereby enhancing the precision in generating designs. (2) The exploration of algorithms to leverage invalid data—designs that fail to meet specified requirements. By establishing DGMs for different types of valid and invalid designs, this strategy allows for training on smaller datasets, addressing one of the fundamental limitations of existing DGMs. (3) The integration of multimodal data, including parametric, graph, and image design representations using multimodal contrastive learning, to synthesize data across diverse modalities, addressing challenges related to fine-grained design differentiation and the absence of labeled data. Education and outreach efforts will include: (1) initiating an industry-academia collaboration group; (2) developing a professional course designed to disseminate cutting-edge research in deep generative models to industry professionals, enhancing their understanding and capabilities in applying these technologies; and (3) comprehensive outreach initiatives targeting high school students and the broader public, aiming to inspire and educate a diverse audience about engineering-focused AI. These efforts are designed to foster a broader understanding and engagement in STEM and inspire and train the next generation of engineers and designers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-03

PROJECT SUMMARY/ABSTRACT: Overcoming cancer drug resistance is a major unmet clinical need across many cancer types, including difficult-to-treat diseases such as gastroesophageal (GEA) and ovarian cancer. In principle, it should be possible to prioritize therapeutic regimen(s) given the molecular and cellular classification of such tumors. However, two bottlenecks prevent sustained progress. First, biopsies from refractory disease are challenging to routinely obtain and evaluate, both for clinical and research purposes. Second, even when such material is procured, it remains challenging to accurately predict therapeutic sensitivities from baseline tumor and immune profiling. Further, intra-tumoral heterogeneity can render distinct biopsies taken from the same patient tumor discordant. In this revised IMAT proposal we capitalize on the clinical observation that upwards of 40% of GEA and 70% of ovarian patients develop peritoneal carcinomatosis and ascites, a plentiful, heterogenous and molecularly representative sample format that is easily accessible since routine drains occur during patient care. However, in standard practice, such drains are discarded with little detailed evaluation or analysis - and given highly variable tumor purities, bulk profiling is rarely informative. To address these challenges, we have developed the first generation of AscitesPredict, a zero passage ‘ex vivo tumor biosensor’ technology that uses single cell technologies to evaluate cell identities and therapeutic sensitivities during a 5 day period in which viability is preserved. AscitesPredict utilizes high dimensional label-free single cell image-based morphological profiling and machine learning applied to brightfield microscopy which can be collected continuously. Thus far, we have developed AscitesPredict by profiling over 35 samples, made initial assessments of technological reproducibility, and have optimized initial workflows. In this revised proposal we take the next steps to harden the technology to prepare it for wider deployment in research settings to support preclinical drug development and in clinical settings as a functional diagnostic. Our goals will be achieved via two Specific Aims. First, we will evaluate real world technical and biological performance characteristics. Second, we will demonstrate broader applicability of drug response measurements by enabling the evaluation of new classes of therapeutic agents including those that impact immune and stromal cells and improve the predictive accuracy of a wider diversity of cellular death pathways beyond apoptosis to support preclinical therapeutic studies. At the conclusion of this project, the technology will be poised for wider deployment across research settings and be sufficiently hardened to enable the launch of a third phase of evaluation clinically as a companion diagnostic.

NSF Awards · FY 2025 · 2025-02

The NSF Workshop on the Future of AI and the Mathematical and Physical Sciences (AI+MPS) will be held March 24–26, 2025 at MIT, bringing together researchers in the Mathematical and Physical Sciences (MPS) who are successfully integrating Artificial Intelligence (AI) tools for accelerating scientific breakthroughs. AI is a rapidly growing field with pervasive applications across a broad range of scientific disciplines. The link between AI and Science is becoming increasingly inextricable, as evidenced by the recent 2024 Nobel Prizes in Physics and Chemistry. This workshop will explore how to best leverage the potential of AI for scientific discovery, as well as optimize opportunities to impact the development of AI using scientific insights. Through surveys and discussion at the Workshop, an interdisciplinary group of experts aims to gain an understanding of the key challenges, gaps, enablers, and opportunities for research in these fields to capitalize on and contribute to this new technology. The Workshop will be organized around the MPS disciplines of Astronomical Sciences, Chemistry, Materials Research, Mathematical Science, and Physics. Within each discipline, the Workshop will address the importance of AI, as well as gaps and opportunities for developing and implementing AI tools and techniques. Cross-cutting AI themes will also be discussed across disciplines, such as AI techniques with interdisciplinary potential and consistent benchmarking. Around 65 participants will directly participate to the discussion (evenly distributed between MPS disciplines and including computer science) and input will be solicited from a broader pool of researchers across the MPS divisions via surveys in advance of the Workshop to jump start discussions. The Workshop will provide the opportunity for cross-pollination and transferable learning across disciplines and the opportunity to develop a perspective and recommendations spanning the MPS research portfolio. The outcome of the Workshop will be a white paper that reflects the breadth of perspectives in the MPS community, outlining the current state of AI in each discipline, the potential impact of AI in each discipline, and a roadmap for pursuing strategic interdisciplinary goals. 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

The behavior of objects at the scale of atoms, or smaller, is well described by the laws of quantum mechanics. The behavior of large objects, such as the earth or the galaxy, is well described by Einstein’s theory of gravity. However, these two descriptions of our universe do not appear to be fully compatible, and a challenge of contemporary physics is to find a way to bridge this seeming incompatibility. One difficulty with doing this is the fact that the gravitational force between atomic-scale objects is too weak to measure. To overcome this problem, the PI and his students are planning to perform experiments on centimeter-scale objects, which have significant gravitational interactions, with a precision that approaches the limits allowed by quantum mechanics. Additionally, the PI will author a textbook that uses an inter-disciplinary approach to the theory and practice of realistic quantum measurements. This textbook will help train the next generation of quantum engineers. The research program will extend recently demonstrated techniques to fabricate multi-centimeter-scale, mass-loaded, high-stressed torsional suspensions with mechanical quality factors of at least 100 million. The angular displacement of such a torsional oscillator will be measured and controlled in a quantum-noise-limited manner. The torsional pendulum will be used as a “probe mass” to detect the gravitational force of a driven, milligram-scale “source mass”. Techniques will be developed to simultaneously isolate gravitational interaction and realize quantum-limited and quantum-noise-evading operation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

ABSTRACT Waste clearance in the central nervous system is impaired in many neurological diseases; its dysfunction likely accelerates the pathological accumulation of misfolded proteins that causes many dementias and hinders the efficacy of therapeutics designed to remove these proteins. There is a need for therapies that fortify and promote the brain’s own clearance mechanisms, but many of these mechanisms are still unknown due in part to the lack of noninvasive tools such as magnetic resonance imaging (MRI) to measure clearance dynamics in individual patients. Waste clearance in the brain relies on CSF flow, which has been demonstrated in response to arousal changes, cardiac-driven arterial pulsations, respiration, and neural activity, with hemodynamics often considered as a driving force. However, it is unknown to what extent the viscoelastic brain tissue motion may also influence CSF flow, in part because measuring tissue motion requires in vivo measurements and an intact cranium for unaltered intracranial pressure to observe brain motion in its natural state. Now, there is an opportunity to use sensitive MRI “motion-encoding” tools to quantify tissue and CSF motion and measure blood dynamics regionally at the level of a cortical gyrus to investigate how blood and tissue dynamics impact CSF flow. We propose to develop a novel MRI method to capture hemodynamics, brain tissue displacement, and CSF flow concurrently and at high spatial resolution, facilitating hypothesis testing of CSF flow mechanisms in vivo. To capture tissue displacements on the order of tens of microns, we propose to adapt the Displacement ENcoding with Stimulated Echoes (DENSE) method to our ultra-high magnetic field strength MRI scanner and extend it to measure hemodynamics concurrently. Our method is novel because blood, tissue, and CSF dynamics have not yet been measured concurrently in vivo, or at the targeted spatial scale of a cortical gyrus. We will measure these dynamics during the cardiac cycle and respiration, and in response to neuronal stimulation driven by a visual stimulation task, as each of these physiological processes are hypothesized to influence CSF dynamics. Given the concurrent measurement of all compartments, this method may be used in future studies of spontaneous neural dynamics, such as during sleep. Understanding flow mechanisms of CSF may reveal opportunities for therapeutic intervention to promote waste clearance in the aging brain. The applicant’s long-term goal is to lead an academic research team that is focused on applying novel neuroimaging techniques to critical questions in neurodegenerative disease. The proposed training plan centers on MRI physics and acquisition, advanced spatiotemporal analysis of neuroimaging data, research design and experimental methods, the pathophysiology of neurodegenerative disease, and professional development in science dissemination and leadership. The proposed research plan will take place at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, which is renowned for neuroimaging methods development and clinical translation and will further support the applicant’s training and career development.

NIH Research Projects · FY 2026 · 2025-02

Project Summary/Abstract Enzymes with iron-sulfur (Fe-S) clusters in their active sites catalyze myriad transformations relevant to human health and disease. Identifying disease targets and designing inhibitors requires an in-depth understanding of their reaction mechanisms, which in turn requires both molecular-level characterization of resting states and intermediates as well as comparison with structurally and functionally faithful synthetic models. This project entails both advancing the synthetic modeling chemistry of Fe-S proteins and developing new methods for improving the information content of spectroscopic data on Fe-S proteins. In the context of modeling the chemistry of Fe-S enzymes, we will synthesize clusters whose geometric structures closely resemble those of enzymatic intermediates and whose spectroscopic data can be compared with the data acquired on natural systems. In addition to using models in this manner (i.e., for structure elucidation), we will formulate hypotheses regarding the electronic structures of these intermediates and the factors that dictate their diverse reactivity patterns, and we will test these hypotheses by harnessing the exquisite tunability of synthetic chemistry to rationally altering the clusters’ properties and reactivity. Overall, this part of the project will combine synthesis, spectroscopy, and mechanistically oriented reactivity studies to shed light on the mechanisms of Fe-S enzymes. Regarding our biological work, we first note that although 57Fe Mössbauer and 57Fe ENDOR spectroscopy can be exceptionally powerful in characterizing intermediates of Fe-containing enzymes, the high nuclearity of Fe-S clusters can limit the usefulness of such techniques because the signals arising from multiple metal sites can be challenging to resolve. This is true of resting states and is especially problematic when analyzing mixtures of reaction intermediates. Moreover, it is often impossible to map spectroscopic responses to specific sites in the geometric structure, and this severely limits our understanding of the chemical bonding—and therefore the reactivity—of biological Fe-S clusters. We propose to address these challenges by developing methods for incorporating 57Fe into specific sites of biological Fe-S clusters. Doing so would make Fe-S proteins as spectroscopically tractable as mononuclear Fe-containing enzymes, and would thereby greatly enhance our mechanistic understanding of Fe-S proteins. In addition to developing the methodology, we will use the site- selectively labeled samples generated in this project to study reactive intermediates and inhibitor-bound states of Fe-S enzymes of relevance to human health and disease.

NSF Awards · FY 2025 · 2025-02

This Cyber-Physical Systems (CPS) project supports research that investigates accelerating the design of controllers for large-scale engineering systems, focusing on the application of artificial intelligence in transportation. Traditional design methods often rely on simulations that do not accurately represent real-world complexities, leading to an inefficient and costly process of collecting data, calibrating models, and testing controllers. This project aims to bridge the gap between simulated cyber environments and real-world physical operations by utilizing extensive offline datasets and offline reinforcement learning. Specifically, the research team will harness data derived from millions of vehicle mile data collected on the I-24 MOTION open road testbed in Nashville, Tennessee. By developing efficient and adaptive control systems, such as improved cruise control for vehicles, the project seeks to enhance safety, reduce traffic congestion, and improve overall riding comfort. The anticipated result could be a tenfold reduction in societal-scale transportation system design cycles, leading to significant societal benefits in emissions reduction, air quality improvement, and transportation costs. Moreover, the project will contribute to education by offering courses that equip students with the skills needed to deploy these innovative systems, thereby preparing them to tackle future societal challenges. The collaborative project will perform research that explores critical questions surrounding the deployment of offline reinforcement learning in societal-scale cyber-physical systems in transportation. The research attempts to addresses three key challenges: first, ensuring that controller designs align user preferences with system objectives; second, effectively processing and extracting useful information from vast time series datasets; and third, significantly reducing the number of iterations required in the design process. To achieve these aims, the multidisciplinary research team will develop novel reward functions informed by inverse reinforcement learning principles to encourage user participation. Additionally, advanced methods will be employed to explore the rich data generated by the open-road testbeds. The implementation of hybrid reinforcement learning strategies will facilitate real-time interactions of deployed controllers, enhancing design efficiency. Validation of the controllers will occur through extensive testing with vehicles on the open road, using the I-24 MOTION framework to ensure practical reliability and safety. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY Congenital heart defects (CHD) affect ~1% of all births and are the leading cause of morbidity and mortality in infants. Notably, over 50% of Down Syndrome (DS) infants, the most prevalent autosomal abnormality caused by trisomy 21 (T21), are born with a CHD, with septal defects most common. The pathways that contribute to cardiac defects in T21 are poorly understood. The critical region of human chromosome 21 associated with CHD harbors genes coding for extracellular matrix (ECM) as well as cell-ECM and cell-cell adhesion proteins, among others. Using T21 hiPSCs derived from DS individuals, our preliminary data show increased ECM, tissue stiffening, and altered ECM-cell interactions in iPSC derived cardiomyocytes compared to isogenic controls. We also observe precocious differentiation including changes in cell cycle, contractility, and nuclear morphology. Based on compelling preliminary data, we hypothesize that disruption of ECM during heart development leads to downstream effects on cell signaling and to cytoskeletal reorganization that impacts nuclear architecture and gene expression programs. We will test this hypothesis using 3D cardiac models, providing a unique opportunity to investigate critical pathways at the molecular, cellular, and tissue level. In Aim 1 we test the idea that a maladaptive ECM-integrin circuit impacts costamere formation and downstream signaling in cardiac models. In Aim 2 we will test the idea that ECM alterations can impact nucelar architecture and genome organization through cytoskeltal reorganization. Understanding mechanobiological regulation of heart development in T21 will fill a major knowledge gap and is expected to reveal new molecular mechanisms that contribute to CHD. More broadly, this work has the potential to identify therapeutic modalities that minimize DS- associated phenotypes as well as provide a broader solution to a range of diseases involving ECM alterations including age-related degeneration, fibrosis, and cancer. The fellowship training plan capitalizes on the well-established expertise of the Boyer lab in cardiac gene regulatory mechanisms and Dr. Boyer’s extensive mentorship experience, as well as the input of leading experts in the fields of nuclear architecture, cell adhesion and ECM. Dr. Boyer’s lab at the Massachusetts Institute of Technology in both the Biology and Biological Engineering Departments is an ideal research and training environment, facilitating ample opportunities for collaboration and the ability to pursue creative and challenging projects. This project will allow me to develop new skills and expertise that will provide the strongest foundation for my development as an independent researcher.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY/ABSTRACT Acute respiratory distress syndrome (ARDS) is a severe form of lung injury with significant public health implications due to severe morbidity and mortality. The need to utilize existing data to inform prospective research and clinical decision making was emphasized during the COVID pandemic, when ARDS became a leading cause of death, and clinicians were forced to operate outside of existing evidence. `Dynamic treatment regimes' (DTRs) are rules for making treatment decisions sequentially at multiple time-points based on a patient's evolving history. Most relevant treatment strategies for ARDS are DTRs. DTRs may be evaluated in randomized trials, however it is infeasible to conduct randomized trials testing all DTRs of interest. This grant proposes `target trial emulation' from observational data using `g-methods' for confounding adjustment to address multiple gaps in our knowledge about ARDS care. Target trial emulation with g-methods is essentially the gold standard for causal inference about DTRs from observational data, but the approach is underutilized in the critical care setting. We will also explore generating personalized DTRs based on machine learning derived phenotypes. The investigation will utilize two large datasets—including the Medical Information Mart for Intensive Care (MIMIC) IV database and the eICU collaborative research database—representing a wide geographic and demographic spectrum. We will specifically address questions surrounding initiation, duration and dosing of steroids among patients with ARDS in the following clinical aims. Using target trial emulations and g-methods, we will: 1) estimate effects of early and sustained steroid use compared with delayed, abbreviated, or no-steroids regimes across a range of doses in patients with ARDS, 2) estimate the effects of dynamic strategies for steroid initiation based on evolving markers of disease severity in ARDS patients, and 3) identify the effects of steroid strategies across cohorts defined by joint ARDS and sepsis status. We will utilize multiple datasets to assess stability of findings across centers. The project represents a collaborative effort between experts in critical care medicine (with a specialty in mechanical ventilation), critical care data science, and causal inference. Our results will address important gaps in clinical knowledge about treatment of ARDS and inform the design of future randomized trials. Our study designs, code, and constructed cohorts will also provide valuable templates for other researchers to use in future observational studies, which we hope will broadly improve the quality of evidence from observational data in critical care.

NSF Awards · FY 2025 · 2025-01

Computer networking provides a foundation for many exciting developments, from artificial intelligence to the control of industrial systems. To keep pace with these advancements, networking hardware is continually evolving. However, these development activities are expensive, requiring significant up-front effort in quality assurance, as bugs are difficult to fix after hardware is manufactured and deployed. As a result, there is a potential for significant economic benefits from improved hardware development techniques that reduce the prevalence of bugs. Formal methods are a long-standing family of techniques offering mathematical proof that system components behave correctly in all possible scenarios. However, formal methods have not previously been applied to prove that programmable networking hardware is correct – the focus of this project. The project impact is reduced cost of finding and fixing bugs, through a hardware project’s whole lifecycle, from initial design through deployment in the field. This project develops novel tools for implementing and proving the correctness of programmable networking hardware, starting with examples in the style of Intel's Tofino switches. Formal proofs of functional correctness (i.e., for every possible set of input packets, the expected output packets will be produced) are carried out in the general-purpose Coq theorem prover. The focus of the proofs is on justifying the correctness of complex optimizations that allow switches and other networking hardware to process packets very quickly. Most relevant optimizations improve performance by increasing concurrency through a variety of mechanisms. These optimizations can be justified as separate algebraic rewrites (i.e., transformations) within larger proof module hierarchies. By the end of the project, the aim is not just to prove that switches are correct, but to derive optimized switches semi-automatically from non-optimized switches, where engineers assemble lists of optimizing rewrites to be performed. Any switch produced through such a flow is correct by construction, removing the need to apply time-consuming and incomplete techniques like testing and code auditing to find bugs after the fact. To support the adoption of the new methods in industry, tutorials at major networking research conferences are planned, to demonstrate the derivation of realistic switches, introducing all required formal methods content. 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

Many real-world settings involve the interaction of multiple agents with diverse goals and differing amounts of private information. These range from military and security settings, to auctions, to networks. Developing technology to find optimal behavior—also known as equilibrium—in these interactions has the potential of enabling more economically efficient auctions, enhance strategic reasoning in situations of conflict, and improve our ability to predict the evolution of complex multiagent systems. This proposal aims to advance our theoretical and practical understanding of equilibrium computation, improve the efficiency of computing various equilibrium notions, and enable the development of high-precision and practical methods across a variety of settings. This project also includes a comprehensive plan for incorporating the research into undergraduate and graduate courses, preparing students to tackle interdisciplinary challenges at the interface of optimization, game theory, and computer science. This project addresses several fundamental gaps in our current understanding of equilibrium computation and learning dynamics in multiagent interactions ("games"), with a bias towards focusing on techniques that will enable the construction of new state-of-the-art algorithms for equilibrium computation at scale. Concretely, it tackles four key technical challenges. 1) It refines the understanding of learning dynamics in games, employing tools from nonlinear dynamical systems to construct state-of-the-art algorithms with optimal regret guarantees. 2) It investigates the efficiency of computing equilibrium notions, such as correlated equilibria and its variants, in structured games like imperfect-information extensive-form games. 3) It seeks to overcome the limitations of current low-precision methods by developing practical algorithms for high-precision equilibrium computation. 4) It clarifies the computational complexity of nonconvex games by examining the role of constraints and extending recent advances. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Control over stereo- and site- selectivity in bond formation events is of pivotal importance in organic synthesis and catalysis. There has been intense research to develop biomimetic catalysts to address these needs. One approach is the engineering of natural enzymes to accept non-native substrates or operate with non- natural reactivity. These biocatalysts have found application in various fields, including the large-scale synthesis of pharmaceuticals. However, introducing completely new-to-nature reactivity remains challenging. Another approach involves the development of small molecule peptide catalysts, which can be easily synthesized and designed to contain non-natural residues. Despite this advantage, reported small molecule peptide catalysts derive most of their structural diversity from individual residues and not the sequence. Thus, there exists a gap in the “middle ground”: peptide catalysts that have sequence-derived chemical diversity and are enriched by non-natural residues to confer the desired reactivity. The proposed research aims to leverage automated fast-flow peptide synthesis (AFPS) and machine learning to access this untapped space and develop peptide catalysts for stereo- and site-selective C–C bond- forming reactions. The AFPS platform enables precise and rapid synthesis of mid-length (~40 residues) peptide catalysts that contain non-natural residues. Machine learning will guide our exploration of the vast chemical space covered by this combinatorial peptide space. In one research direction, we will synthesize peptides that bind transition metals (Pd-OACs) and catalyze haloselective cross-coupling reactions. Neural network (NN) models will be used to correlate sequence to selectivity, inform catalyst design, and lead to iterative improvement of site selectivities. Another research direction focuses on developing peptide organocatalysts for stereoselective C–C bond formation through the Morita-Baylis-Hillman (MBH) reaction. This approach involves incorporating non-natural residues comprising thiol, phosphine, and azole functionality, and leveraging transfer learning to expand the reaction to new substrate classes. The proposed research offers an innovative blueprint for developing peptides that catalyze C–C bond formation reactions with pinpoint accuracy. It aims to pave the way for on-demand development of peptide catalysts for various reactions, leading to advances in organometallic chemistry, machine learning-guided catalyst discovery, and ultimately medicinal chemistry. The training plan and environment permits the design and study of the peptide catalysts with the Pentelute lab (MIT), Buchwald lab (MIT), and Gómez-Bombarelli lab (MIT). The proposed studies will be performed with the equipment, resources, and facilities available in these labs.

NSF Awards · FY 2025 · 2025-01

This project builds and tests new technology to help people with severe speech and motor impairments better control computers and use them to communicate. Such impairments make it very hard to use voice, touch, keyboard, and other common ways of interacting with computers. One alternative that can help is "single-switch" interaction, where systems allow people to use the muscle control they do have to simulate clicking a single button. For instance, Stephen Hawking famously composed lectures and interview responses with his computer by twitching his cheek; other interfaces use blinking, or puffing air into a special sensor. However, these single-switch interaction methods, and the interfaces built based on them, are much slower and more error-prone than voice, touch, or keyboard interfaces. In this project, the research team collaborates with people with severe disabilities to develop technology to make it faster, easier, and more accurate for this set of people to communicate and use computers. The key general idea is to use additional information about a person's context such as where they are looking, what they have done recently, and other clues to their intentions to help computers guess, and suggest, complicated communication goals based on simple single-switch interactions. To meet these goals, this project leverages machine learning and a novel user interface to synthesize four currently under-utilized information sources. First, users are likely to be looking at a target when they select it; unlike methods that use eye gaze as a pointer, such passive information requires no conscious eye effort from the user and may provide information even when a user's gaze is not precise enough for selection by itself. Second, modern large language models can often anticipate what a user is interested in writing and can likely learn to leverage contextual clues (e.g., who the user is writing to or topics suggested by a communication partner) with only a small number of training examples. Third, patterns of behavior across users and time can offer clues; for example, similar users may provide information about a new user, or a user might exhibit different behaviors at different times of the day. Fourth, sequences of noisy input actions can jointly inform a desired user action such as writing a word. The project combines these four sources of information with a user's single-switch input to improve speed and accuracy of text entry and computer interaction tasks. This project's innovations are designed and tested with users with disabilities in an extensive, multi-pronged set of participatory design activities and experiments. Participatory design with this set of users is particularly challenging due to their communication speed. This project therefore investigates a novel combination of participatory design methods in which stakeholders provide feedback via an asynchronous messaging platform, in co-design interviews, and via surveys designed to be easily completed with single-switch interaction techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This I-Corps Hub develops a New England regional innovation ecosystem to provide training to scientists and engineers that supports the translation of critical emerging technologies. The Hub is a consortium of universities spanning the New England region, and many of these researchers are located in rural areas lacking access to the resources found in a robust innovation ecosystem. The Hub aims to elevate this region by connecting I-Corps participants with curated resources and mentoring to enhance place-based innovation across New England. The Hub strives to ensure the participation of all Americans to have a positive impact on local economies, generate good-paying jobs, revitalize manufacturing, and accelerate new industries. In addition, the Hub will proactively recruit participants from institutions that are not Hub partners. I-Corps training will form the foundation to grow an entrepreneurially-minded, professional Science, Technology, Engineering and Mathematics (STEM) workforce and develop new products and services to benefit society and increase regional and U.S. competitiveness. This I-Corps Hub project is based on the development of a regional network that provides Science, Technology, Engineering and Mathematics (STEM) researchers throughout New England with I-Corps entrepreneurial training, mentoring, and support. The goal is to promote and accelerate the translation of fundamental research to products and services that improve societal well-being and drive economic growth. While participants from all STEM disciplines will be recruited, emphasis will be on those benefitting local communities, such as BlueTech, Forestry, Sustainability, and Biotech/Life Sciences. The Hub will deliver I-Corps training on a large-scale and high frequency to researchers throughout the region via both online and in-person courses reaching higher education colleges and universities. This training will be accomplished by co-teaching using a regional instructor pool from all partner institutions with continual skills development. In addition, the number of researchers participating in I-Corps is expected to grow, generating significant new deep tech ventures while increasing the odds of commercial success and societal impacts. Outreach will be a primary activity of the Hub to ensure that all researchers in New England have the opportunity to participate in I-Corps and benefit from this training. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary Photoactive agent-based theranostics play an important role in the development of innovative approaches for tumor diagnosis and treatment. Society stands to benefit greatly if methods to detect and treat cancers at an early stage are enhanced, which often depend on our ability to effectively visualize and send localized energy to specifics regions of the body. The central theme of the proposed project involves the rational design, synthesis, and molecular and photophysical characterization of cationic carbone-boracycles as far-red and near-infrared (NIR) photoactive agents. The innovative approach involves utilizing carbone ligands as both σ- and π-donors, which impart remarkable stability to cationic boron centers, ensuring the molecules remain stable in biologically-relevant conditions and offering advantages for targeting and treating tumors. Various boracycles are being targeted with unique ring fusions and sizes such that they have appropriate electronic structures to make them suitable for potential applications in phototheranostics. Specific aim #1 focuses on the synthesis and functionalization of cationic boron heterocycles that emit in the near-infrared region, enhancing fluorescence/luminescence imaging capabilities and facilitating photodynamic therapy (PDT). This includes the synthesis of stable versions of conjugated cationic boron, nitrogen-acene derivatives and non-conjugated anthanthrene bis-cations. Specific aim #2, which is independent of aim #1, focuses on the development of non-emissive carbone-azulenes as biocompatible NIR absorbers. The hypothesis is that the addition of the azulene unit will permit maximum absorption, such that all of the absorbed light energy will be transformed into heat for potential applications in photothermal therapy (PTT). In aims #1 and #2 the synthesis will be guided by computational chemistry and the photophysical data will be simulated to obtain detailed information regarding the electronic transitions that result in the optical properties. Specific aim #3 involves sophisticated optical measurements to compare the photophysical properties of the newly synthesized compounds, especially those that have undergone nanoencapsulation. These measurements include including absorption and emission, quantum yield measurements, and fluorescence lifetimes. The goal is to screen for compounds with suitable energy properties for biological screening at the MIT Broad Institute. This interdisciplinary approach, combining main-group chemistry, photophysics, and photobiology, highlights the project's innovative nature and potential for groundbreaking advancements in bioimaging and cancer therapy.

NIH Research Projects · FY 2026 · 2025-01

The mammalian brain is constantly being shaped by input from the body and external world. The brain's plasticity involves changes in gene expression, protein synthesis and activity, synaptic function, and neuronal growth and death. Understanding how these processes are influenced by stimuli, and how they in turn mold profiles of neural activity and behavior, requires an ability to map the dynamics of molecular and cellular-level changes as they occur throughout the life of an individual. We recently developed an imaging method that enables bioluminescent reporter proteins to be monitored throughout the living brain using hemodynamic magnetic resonance imaging (MRI). Our method, called BLUsH, provides noninvasive readouts of reporter expression at submillimeter resolution in individual animals over time intervals ranging from minutes to days, or longer. In initial studies, we have shown that BLUsH can be used to delineate viral tracing patterns and expression of activity-dependent immediate early genes (IEGs) in vivo. These and related approaches permit longitudinal studies of brain- behavior associations that were previously extremely difficult to study. In this project, we will apply BLUsH to address several paradigmatic questions about brain-wide plasticity: (1) What are the spatiotemporal relationships between neural activity and plasticity gene induction? (2) How are experience-dependent changes in the physical structure of the brain driven by molecular and cellular processes? (3) How does repeated exposure to a stimulus or behavior affect characteristics of plasticity phenomena throughout the brain? (4) What features of neuroplasticity are predictive of individual behavioral variability? We will approach these questions through three specific aims: In Aim 1, we will apply BLUsH IEG imaging and conventional functional MRI (fMRI) in animal models of opioid use disorder to measure relationships between neural activity and plasticity gene induction. We will test hypotheses about variation of these processes over experience and among brain regions and individual animals, in experiments that we expect to reflect broadly on the use and interpretation of IEG mapping in neuroscience. In Aim 2, we will evaluate the contribution of neurogenesis and astrocytic remodeling to neuroanatomical change in a mouse model of voluntary exercise. We will assess whether molecular and cellular markers of neuroplasticity predict individual behavioral performance and test hypotheses about involvement of specific brain structures. In Aim 3, we will improve characteristics of the BLUsH system itself, aiming to enable applications of BLUsH in nontransgenic animals and using multiplexed reporters. The new strategies will be validated in the experimental paradigms of Aim 1, and will ultimately enable applications of BLUsH to studies of plasticity and other phenomena in diverse animal models.

NSF Awards · FY 2025 · 2025-01

The Association for Computing Machinery (ACM) - Society for Industrial an Applied Mathematics (SIAM) Symposium on Discrete Algorithms (SODA) is one of the premier annual research conferences that cover the breadth of theoretical computer science. It is a conference of very long standing that has continued to play a formative role in the field; it is also at the leading edge of connections made to other areas. This project aims to increase the impact of this conference on students and postdoctoral researchers, particularly those from under-represented groups, by encouraging and enabling their participation, especially in cases where travel expenses and conference fees would otherwise preclude their attendance. Concretely, this project assists US-based students and postdoctoral fellows in attending the 2025 Annual SODA conference, sponsored by the ACM and SIAM. The coming SODA will take place in New Orleans, Louisiana, January 12-15, 2025. The project plans to support 26 students in attending this conference. It is planned to select the students in such a way that the group of students attending these top-rank research conferences is broadened. By this the impact of Algorithms Research, as well as the participation in Algorithms Research, will increase. 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

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Alexander Radosevich of the Massachusetts Institute of Technology is studying inexpensive chemical catalysts composed of Earth-abundant elements, such as iron and phosphorus, that are capable of making and breaking strong chemical bonds. The new catalysts are potentially important because they provide new opportunities for the conversion of common chemical compounds into higher value products. By composing the catalysts of earth-abundant elements, the methods that are proposed under this project are inherently responsive to the demands of sustainability in chemical synthesis. Specific reactions to be pursued in this research include new schemes to convert C-H bonds in organic molecules into a range of desirable products. Researchers working with Dr. Radosevich will receive training in a broad set of spectroscopic, physical, and synthetic chemistry techniques; achieving these training objectives will contribute to a vital workforce that underpins the economic competitiveness of US-based chemical industries. In pursuing these studies, Dr. Radosevich’s laboratory will collaborate with Dr. Sami Lakhdar of the University of Toulouse in France, an expert in photochemical and mechanistic chemistry required to conduct this project, contributing to ongoing US leadership in international scientific engagement. This project is being conducted as part of a joint activity between NSF and the French Agence Nationale de la Recherche (ANR). With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Alexander Radosevich of the Massachusetts Institute of Technology is studying new molecular constructs composed of earth-abundant elements that enable difficult bond cleavage and atom abstraction reactions via proton coupled electron transfer (PCET). PCET reagents commonly suffer from an inverse scaling relationship between acidity and reduction potential (i.e. thermodynamic compensation) that blunts the ability to modify effective homolytic bond dissociation free energy over a wide range. In this project compounds based on P(V) moieties with modular and mutually-independent proton- and electron-management domains will be studied, and an experimental physical, thermodynamic, and mechanistic foundation for their ability to perform PCET reactions will be established. The expected outcomes will include: 1) an improved fundamental understanding of the interplay between structure and reactivity in multisite PCET reactions, 2) a rigorous and evidence-based rationale for the thermodynamic and kinetic selectivity of observed H-atom abstraction reactions, and 3) new practical catalytic methods for the construction and functionalization of small organic molecules of increasing complexity via homolytic bond activation. Taken together, the major impact of this collaborative research will be the establishment of new earth-abundant catalysts exemplified by open-shell P(V) compounds as a useful modality in catalytic PCET activation. This project is being conducted in collaboration with Dr. Sami Lakhdar at the Université Paul Sabatier Toulouse and other researchers in France as part of a joint activity between NSF and the French Agence Nationale de la Recherche (ANR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This award supports research that will enable insect-scale legged robots capable of climbing on inverted surfaces and grasping irregularly shaped objects. Unlike spiders or ants that can climb walls, ceilings, and tree trunks while carrying objects such as leaves and seeds, most similar-sized robots are constrained to move on flat, horizontal terrain with limited capability of picking and releasing objects. This project will create insect-like climbing and grasping capabilities in tiny robots through investigation of fundamental principles of adhesion and lubrication, construction of new adhesion mechanisms, design of new climbing gaits and grasping modalities, and formulation of control methods for these tasks. This research will provide economic and societal benefits through potential applications such as inspection of turbine engines and collective debris removal from pipelines and other cluttered spaces. In addition, active muscle-like adhesion devices developed in this work may find applications in wearable haptic devices or small-scale manipulation systems. This project has broader impacts in the education and training of graduate and undergraduate students. The team of researchers will inspire the next generation of scientists and engineers through the creation of outreach programs involving interactive presentations and robot exhibitions at local museums, hosting students from underrepresented minority communities, developing educational multimedia materials, and organizing laboratory tours for K-12 students. The goal of this project is to achieve reliable and robust climbing and object grasping in insect-scale robots through studying the fluidic interfacial forces at the millimeter-to-centimeter scale. The project will investigate a new adhesion strategy where capillary effects generate large normal forces and lubrication effects reduce friction. This design relaxes the friction cone constraint and further allows the robot to slide along a surface while attached. This approach aims to substantially improve climbing stability and grasping robustness. This work focuses on enabling new microrobotic capabilities through investigating the following three directions: (1) develop analytical models of adhesion and lubrication for microrobotic climbing and grasping; (2) enable inverted and vertical climbing with feedback control; and (3) demonstrate grasping and transport of irregular objects. The outcome of this study will result in an insect-scale quadrupedal robot that can climb on inverted surfaces for over 10 meters. The robot will also demonstrate grasping, in-gripper rotation, and release of irregular objects through the use of a compliant gripper that leverages capillary forces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This research strives to develop a fundamental understanding of the multi-functional response of a new class of engineered materials: three-dimensional woven architected nano-composites. Built using additive manufacturing techniques at sub-micron resolution, these woven architected materials will be highly deformable and will provide electrical responses that change based on the amount of deformation sustained by the materials. This coupled mechanical and electrical behavior gives the materials deformation- and pressure-sensing capabilities, enabling their use as highly sensitive and tunable sensors. The research will prepare this new class of materials for application in high-performance electronic skins - a key technology for next-generation soft robots, prostheses, and bio-electronics. This research will also develop fundamental theoretical and computational tools that will enable prediction of electrical and mechanical properties. The research efforts will be complemented by (i) an educational outreach effort that will introduce mechanics and materials concepts to a broad general audience, (ii) enhancements in undergraduate curricula with novel information and concepts on multi-functional materials, and (iii) a mentoring program focusing on broadening underrepresented-community involvement in the mechanics of materials academic field. This research will determine the material-structure-property relations of 3D-woven architected nano-composites to validate the hypothesis that printable nano-composites with tunable electrical properties such as resistivity or dielectric constant, combined with the complex nonlinear mechanical responses of 3D-woven architectures such as self-contact and entanglement, will provide a new paradigm for the design of high-performance electro-mechanical sensors. Combined experimental, computational, and analytical approaches include microscale 3D printing of functional nano-composites, in-situ microscale multi-axial mechanical testing, in-situ macroscale electromechanical characterization, nonlinear computational modeling and simulations, and a circuit-model-based analysis. These approaches will be used to connect the material and structure of the architected nano-composites to the electromechanical performance of stretchable 3D architected nano-composite sensors. 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 · 2024-12

The project seeks funds to organize a workshop that would discuss a relatively new upper atmospheric phenomenon referred to as STEVE (Strong Thermal Emission Velocity Enhancements). This manifests in the form of a spectacular optical display in the night skies at lower latitudes than the usual auroral regions. It appears as a distinct mauve ribbon that extends to a broad longitudinal region and is confined in latitude. This intriguing feature has aroused the curiosity of several scientists resulting in preliminary studies providing new insights into its characteristics. However, detailed work on its formation, drivers with the magnetosphere-ionosphere-thermosphere (MIT) system is required to solve this mysterious phenomenon. To enable brainstorming discussions, this proposal will provide funds for the third STEVE workshop. Such efforts offer a forum for Geospace scientists to come together, present their recent STEVE-related observations and theoretical work. This provides an excellent opportunity to plan for future experiments, and research activities that will advance scientific knowledge about STEVE, its occurrence and formation mechanisms. It brings professional, citizens/amateurs together, thus enhancing collaborations and initiation of new partnerships. Recent observations have demonstrated that STEVE spans approximately 400 – 800 nm wavelength regions. It is accompanied by extremely narrow and fast-moving plasma flows channels (~10 km/s) within a region of high electron temperatures in the range of 104 K. These features are indicative of intense subauroral drifts (SAIDs). Several investigations have indicated that particle precipitation does not appear to play a role in the STEVE occurrence, which is one of the major differences from auroral emissions. The mechanisms behind the highly structured optical emissions associated with STEVE events often referred to as “Picket Fence,” is a topic of significant debate. While early studies suggest a possible link to particle precipitation, the picket fence spectrum remains partially unexplained and stands out from the typical auroral emissions due to the absence of enhanced emission lines. This has prompted the scientific community to explore alternative generation mechanisms, including parallel electric fields. All these recent scientific advances, which shed light on STEVE, the picket fence, and their possible sources, would not have been possible without the contributions from citizen scientists, who accidentally discovered this phenomenon during their auroral chase. Some photographs taken by amateurs have played a crucial role in determining the altitude of the STEVE and Picket Fence emissions. The third STEVE workshop plans to discuss the role of chemistry in STEVE formation, association with substorms, relationship with SAR arcs and impact on technological structures. 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.