Massachusetts Institute Of Technology

NSF Awards · FY 2026 · 2026-10

Since the first direct observation of gravitational waves in 2015, a new field of astronomy has fundamentally changed how the universe can be explored. A gravitational wave is a ripple in spacetime produced when two extremely dense objects, such as black holes or neutron stars, collide. The detectors that observe these signals now record hundreds of such events per year, and the rate is expected to grow to several events per day within the next few years. Sifting through this flood of data to find the rare astrophysical signals buried in detector noise, and then alerting telescopes around the world quickly enough to capture the light produced by a collision, is one of the most demanding computational problems in modern science. This project develops an open, community machine learning framework that makes gravitational-wave discovery faster, more accurate, and more accessible. The framework will reduce the time between the crossing of a gravitational wave through the detectors and a public astronomical alert to less than a second, make it possible to detect events that traditional analysis methods miss, and lower the computing cost of these analyses by orders of magnitude. The project trains the next generation of scientists, including high-school students, undergraduates, graduate students, and researchers at smaller institutions, by sharing open code, open data, open trained models, and open lessons. It also strengthens shared national computing infrastructure that benefits not just gravitational-wave science but also particle physics, neutrino astronomy, and time-domain astronomy more broadly, advancing the national interest by accelerating discovery and broadening participation in science. The project develops ml4gw, an open-source PyTorch-based machine learning (ML) framework for gravitational wave (GW) data analysis. ml4gw provides Graphics Processing Unit (GPU)-accelerated implementations of the data ingestion, signal-processing, waveform-generation, and inference operations that historically ran on central-processing-unit clusters of the Laser Interferometer Gravitational-wave Observatory (LIGO) Data Grid, and integrates the resulting models into the international low-latency alert pipeline. The award covers three coordinated work packages. The first extends model coverage to long-duration binary-neutron-star and neutron-star-black-hole signals using multi-rate and multi-band processing, integrates auxiliary detector channels through multimodal architectures, and develops state-space and ensemble models together with a shared foundation backbone from which task-specific models can be fine-tuned. The second work package builds production-grade cyberinfrastructure that targets the National Artificial Intelligence Research Resource, the National Research Platform, and the Open Science Data Federation, including an Inference-as-a-Service deployment built on the NVIDIA Triton inference server and on the SuperSONIC service that already supports particle and neutrino physics experiments. The third work package delivers community resources: standardized benchmark datasets with persistent digital object identifiers on Zenodo, versioned reference models on Hugging Face, comprehensive documentation and tutorials hosted on Read the Docs, containerized release artifacts, an upgraded continuous integration system that reduces test runtime by an order of magnitude, and an agent-driven development scaffold for community-led code contribution. Training and outreach activities include hands-on tutorials at international collaboration meetings, an annual hands-on lesson at the University of Minnesota Time-Domain Astrophysics Summer School, and a public machine learning challenge focused on binary-neutron-star detection that builds on a prior challenge that engaged hundreds of teams and roughly a thousand individual participants. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program within the Physics Section of the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Artificial intelligence systems can now generate realistic video, but they still struggle to learn the world knowledge needed to understand how environments change over time, anticipate the consequences of actions, and support decision- making in the physical world. This limitation is a major barrier to building machines that can safely and effectively assist people in homes, workplaces, and scientific settings. By developing learning methods that extract action-relevant structure directly from raw video and other sensor data, this project will help lay the foundation for more capable and adaptable intelligent systems, with potential benefits for robotics, scientific discovery, and other applications that require reliable machine perception. The project will also create open educational materials and mentorship activities that train students across vision, robotics, and machine learning. This project develops a self-supervised framework for video representation learning that separates efficient perception modules from generative world models, enabling the discovery of compact representations of scene state, motion, and action from raw sensory streams without dense human annotation. The research will study learning objectives and architectures that support long-context prediction, planning, and action-conditioned world modeling, while also yielding representations that can implicitly support conventional vision capabilities such as 3D reconstruction, motion estimation, and segmentation through a single scalable learning objective. By unifying perception, prediction, and planning in one framework, this research agenda aims to advance general machine perception for embodied intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT Invasive fungal disease is a significant public health concern, causing an estimated 1.5 million deaths yearly. Candida auris is a particularly formidable fungal pathogen due to its high mortality and multidrug resistance to small-molecule antifungal agents, underscoring the urgent need for new therapies. As an alternative to the traditional antifungal paradigm, we propose to harness the innate immune system to combat fungal pathogens. The immune response typically involves phagocytic clearance of fungi by innate immune cells, like neutrophils and monocytes. However, as in the case of C. auris, some fungal pathogens evade this defense mechanism. Thus, we aim to develop “cellular glues”, multivalent ligands that enhance interactions between fungal and phagocytic cells to promote pathogen destruction. In the R21 phase, we will determine if cellular glues can enhance fungal elimination by phagocytes. In Aim 1, we will generate multifunctional compounds that can simultaneously bind to glycan-binding proteins on C. auris and uptake and signaling receptors on phagocytes. In Aim 2, we will assess our compound's ability to activate phagocytes and promote phagocyte-mediated killing of C. auris. Because our cellular glues are modular, they are ideal for establishing structure-activity relationships that can be used to delineate and optimize the relative contributions of phagocytosis and other immune effector functions in fungal clearance. The R33 phase will be pursued if our milestones are achieved. In Aim 3, we will determine the safety and efficacy of our cellular glues in vivo. The Mansour and Kiessling Groups will collaborate to assess toxicity, biodistribution, and antifungal efficacy in mouse C. auris infection models. Data from these studies will be further leveraged to improve cellular glue efficacy. Simultaneously, in Aim 4, we will optimize our cellular glues to increase their potency and efficacy based on data from the previous Aims. Specifically, we will exploit our modular synthesis to assemble a set of compounds functionalized with diverse glycan ligands. These can be used to optimize the relative binding affinity for C. auris and the phagocyte receptors to promote the desired responses. The overall goal of these studies is to determine if these multifunctional ligands, which can bolster immune cell responses to C. auris, can serve as novel antifungal agents. We anticipate that a successful demonstration of our approach will yield a fundamentally novel and general strategy for directing immune responses against fungal pathogens.

NSF Awards · FY 2026 · 2026-06

This grant renews funding for NSF's Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) for the next five-year period. IAIFI is a research institute which is centered at MIT but which involves four Boston-area universities (MIT, Harvard, Northeastern, and Tufts). The over-arching goal of IAIFI is to enable physics discoveries and advance foundational artificial intelligence (AI) through the development of novel AI approaches that incorporate first principles from fundamental physics. This research is extremely timely and intrinsically cross-disciplinary. On the one hand, AI is transforming many aspects of society, including the ways in which scientists are pursuing groundbreaking discoveries. Indeed, for many years, physicists have been at the forefront of applying AI methods to investigate fundamental questions about the Universe --- for example, AI played a key role in the discovery and study of the Higgs boson, the last missing ingredient in the Standard Model of particle physics whose discovery generated a Nobel Prize. On the other hand, further progress in such physics research will require and can help to foster a revolutionary leap in AI, as both the complexity of physics problems and the size of physics datasets continue to grow. By bringing physics and AI researchers together, IAIFI continues to stimulate developments in both directions. IAIFI will continue to develop and deploy the next generation of AI technologies, based on the transformative idea that artificial intelligence can directly incorporate physics intelligence. IAIFI researchers are using these new AI technologies to tackle some of the most challenging problems in physics, from precision calculations of the structure of matter to gravitational-wave detection of merging black holes to the extraction of new physical laws from noisy data. IAIFI researchers are also transferring these technologies back to the broader AI community, since trustworthy AI is as important for other applications of AI in society as it is for physics discovery. To further cultivate human intelligence, IAIFI will also continue to promote training, education, and outreach at the intersection of physics and AI. In this way, IAIFI will continue to advance physics knowledge –-- from the smallest building blocks of nature to the largest structures in the Universe --- while at the same time galvanizing AI research innovation. More technically, by combining revolutionary advances in deep learning from AI with the time-tested strategies of deep thinking from physics, IAIFI researchers will continue to gain a deeper understanding of our universe and of intelligence itself. IAIFI’s Foundational AI research infuses physics principles into AI to create state-of-the-art AI innovations, particularly in the subfields of representation learning, robust AI, and reinforcement learning. IAIFI researchers are developing AI techniques that can be used across a variety of applications while also using physics principles to better understand AI itself. IAIFI is also impacting theoretical physics by utilizing AI tools and techniques to enable physics discovery through the acceleration of theoretical physics calculations, especially in relation to nuclear/particle physics, quantum field theory and string theory, and quantum many-body physics. IAIFI research also has substantial impact on experimental physics at major NSF-funded facilities, including the Large Hadron Collider and the Laser Interferometer Gravitational-Wave Observatory, as well as at various neutrino experiments. Finally, IAIFI’s impact on Astrophysics --- a data-rich field that will significantly increase its data volume over the next decade --- is at the cutting edge of developing techniques for analyzing an unprecedented amount of information. Developments in this field can be used for applications ranging from image classification to data interpretation to anomaly detection, particularly in relation to dark-matter searches, structure formation, and multi-messenger astrophysics. To facilitate cross-disciplinary collaboration, IAIFI research is organized by cross-cutting themes: Representation/Manifold Learning; Generative Models; Uncertainty Quantification/Robust AI; Physics-Motivated Optimization; and Reinforcement Learning. IAIFI is also leveraging broader impacts activities to recruit and train AI+Physics talent and to build a vibrant AI+Physics community, thereby empowering a new generation of experts in AI+Physics equipped with a common language to advance this exciting new field. These activities include hiring a group of postdocs each year as IAIFI Fellows, as well as supporting a cohort of graduate student Fellows each year, with a focus on interdisciplinary AI+Physics research. IAIFI also organizes an annual IAIFI Summer School and Workshop, weekly talks, industry partnerships, and various public engagement activities. IAIFI will also continue to engage with students from across the U.S. directly through official programs run by our universities and by NSF. IAIFI has also established and continues to support an interdisciplinary PhD program in physics and data science, and is regularly developing courses at this intersection, including a free digital course available on the MITx platform. Finally, IAIFI will increase collaboration more directly with global experts in order to serve as a nexus point for AI+Physics research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary DNA damage has been implicated as driver of cell senescence and human disease, yet almost nothing is known about the true spectrum of DNA lesions that occur in cells and tissues. Similarly, defects in DNA repair are causally linked to human diseases such as premature aging and neurodegeneration, yet the actual substrates for DNA repair enzymes in vivo are unknown and have been assumed from in vitro studies. Complicating our understanding of DNA damage and repair are the potential for myriad highly reactive adduct-competent metabolites from endogenous and exogenous exposures and the emerging recognition of DNA adducts as epigenetic regulators of transcription. Here we propose an interdisciplinary collaboration bringing multiple convergent technologies to bear on defining the interplay between DNA adducts and DNA repair and specifically how this affects DNA replication stress, senescence, and cellular aging. In two aims, we propose studies to (1) lay the groundwork for defining the true spectrum of DNA adducts in human cells, (2) identify adducts that accumulate in senescing cells and drive replication stress, (3) assess how environmental exposures alter the adduct landscape and drive senescence, (4) identify DNA repair pathways for key subsets of these adducts, and (5) understand how loss of repair pathways drives cell senescence and phenotypes of aging. The proposed studies make use of mass spectrometric “adductomics” to discover and quantify DNA lesions and an in vivo host cell reactivation assay (FM-HCR) to measure the capacity of all major DNA repair pathways. As model cell systems, we created a library of DNA repair gene knockouts (KO) in RPE-1 retinal epithelial cells and IMR90 and BJ-1 fibroblasts, each lacking one of seven BER enzymes involved in initiating DNA repair at replication forks. We will apply these tools in two Specific Aims. In Aim 1, we will define repair pathways, replication stress signatures, and activation of the DNA damage response for endogenous and toxicant-induced DNA adducts. These studies compare endogenous sources of DNA damage and exposure to low doses of well-studied toxicants (MMS, H2O2) to test the idea of DNA repair enzymes as epigenetic regulators of gene expression and to define the physiological substrates of DNA repair enzymes. In Aim 2, we assess DNA repair capacity and adduct load as drivers of the senescence-associated secretory phenotype (SASP). These studies (1) determine if accumulation of DNA adducts during senescence is due to decreased DNA repair capacity or increased metabolism, (2) identify senescence-dependent DNA adducts, (3) quantify changes in replication stress during the evolution of senescence, and (4) quantify the effects of toxicant exposures on the senescence time course. The proposed studies provide the first glimpse into the broad spectrum of DNA adducts in human cells and the true substrates for DNA repair systems. In future studies, the results from these human cell models will be readily translated to DNA repair-deficient mouse models and to human tissues to discover disease-driving DNA adducts.

NSF Awards · FY 2026 · 2026-05

This project will provide travel support to help U.S.-based researchers (graduate students, postdocs, and tenure-track faculty without NSF support) attend the Seventeenth Algorithmic Number Theory Symposium (https://www.antsxvii.org/), to be held July 6 to 10, 2026 at the Bernoulli Institute in the Netherlands. This award will provide participants with the opportunity to present their work and interact with leading researchers in computational number theory, cryptography, and related fields. Research in these areas is fundamental to our digital security infrastructure, including the security and privacy of electronic communications and financial transactions that take place over the internet. Several major breakthroughs in cryptography and quantum computing were first announced at previous editions of this conference. The Algorithmic Number Theory Symposium (ANTS), held biennially since 1994, is the premier international forum for new research in computational number theory. It covers algorithmic aspects of number theory, including elementary number theory, algebraic number theory, analytic number theory, geometry of numbers, arithmetic algebraic geometry, modular forms, and finite fields, as well as applications of number theory to post-quantum cryptography and quantum information science. As in prior years, the program is expected to consist of 4-5 invited lectures, 20-30 contributed papers, a poster session, a session for shorter contributions, and a business meeting. The program committee will award the Selfridge Prize to the best contributed paper. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Recent years have witnessed rapid progress in building large-scale quantum computers, the existence of which could make current cryptographic tools for protecting online communication, financial transactions, medical records, and government systems ineffective. This project studies how to obtain security in a future where powerful quantum computers exist, whether they are first used by attackers, offered for customers through remote computing services, or eventually become widely available services. The project will develop the foundations needed to protect data and computation in each of these settings and to identify new security capabilities that quantum information science may enable. The results can help secure communications, commerce, and health care against future attacks, while also making it safer for individuals and organizations to rely on remote quantum services. The project pursues four connected research directions in the foundations of quantum cryptography. First, it will study the real capabilities of quantum computers for breaking cryptosystems by improving algorithms, concrete resource estimates, and error-correction methods, with the goal of determining when large-scale code-breaking attacks may become practical. Second, it will broaden the basis of post-quantum cryptography by constructing advanced cryptographic primitives, such as homomorphic encryption, from alternatives to lattice-based hardness. The project will also advance this field by developing proof techniques that remain sound against quantum attackers. Third, it will investigate how a classical user can delegate computation to a quantum server and still verify correctness, including studying the minimal assumptions for verifiable delegation, and constructing succinct and publicly verifiable proofs that a quantum computation was carried out correctly. Fourth, it will study uniquely quantum capabilities, including quantum money, quantum copy protection, quantum one-time programs, and related forms of quantum program obfuscation. Together, these activities aim to deepen the theory of secure communication and computation in a quantum world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

Synthetic Genetic Controller Circuits for Transcription Factor-Directed Differentiation PI: Domitilla Del Vecchio1,2,3 co-I: James J. Collins2,3,4,5 co-I: Thorsten Schlaeger6 1Department of Mechanical Engineering, MIT; 2Department of Biological Engineering, MIT 3Synthetic Biology Center, MIT; 4Broad Institute of MIT & Harvard; 5The Wyss Institute 6 Stem Cell Transplantation Program, Boston Children’s Hospital PROJECT SUMMARY The ultimate goal of this project is to create synthetic genetic circuits that accurately control the level of cell fate- specific transcription factors (TFs) autonomously in response to cell state changes. The underlying hypothesis is that the level and timing of expression of critical TFs dictates the efficiency of cell conversion protocols and the quality of produced cells. Here, we focus on the differentiation of human induced pluripotent stem cells (hiPSCs) into hemogenic endothelial cells (HECs) from which all hematopoietic stem and progenitor cells (HSC/HPCs) arise. Current methods to derive definite HECs (dHECs), which have the potential to produce adult-type lymphoid cells and HSCs, remain largely inefficient and are also difficult to execute and scale, and, as a consequence, exhibit high degrees of variability in out- comes between different labs, hiPSC lines, and even between replicate experiments.These problems hamper analysis of the underlying developmental processes and pose formidable obstacles to clinical translation of hiPSC-derived blood cell products since ensuring the safety and cost-effectiveness of the product necessitates high differentiation efficiency and consistency. Prior work has demonstrated that SCL (S), LMO2 (L), GATA2 (G), and ETV2 (E) TFs, when expressed in mesodermal cells, activate dHEC gene regulatory networks (GRNs) across species but also that efficient forward programming to dHECs requires discovery and subsequent implementation of both optimal expression levels and tim- ing for the TFs. Yet, conventional methods for TF-mediated cell fate programming generally rely on indiscriminate overexpression with little control on cellular TF levels and without cell state sensing. This is largely due to our inability to precisely control TF profiles during cell fate programming, and this limitation has prevented discovering optimal tra- jectories and subsequently enforcing them. Here, we propose synthetic genetic controller circuits that overcome this hurdle. In Aim 1, we create genetic circuit designs that set TF levels and use them in an efficient in vitro differentiation protocol to discover the optimal combination of S, L, G, E levels and timing. In Aim 2, we develop a circuit architecture, based on a novel TET1-enabled positive feedback system, to prevent epigenetic silencing of genetic circuits once de- livered to hiPSCs. In Aim 3, we make our genetic controller circuits enforce autonomously the optimal SLGE TF levels found in Aim 1 in response to the hiPSC-to-mesoderm transition. We achieve this by a new autocatalytic ADAR-based RNA sense-and-respond system, which senses the mesoderm marker Brachyury (TBXT) and enforces user-defined TF levels in response to it. We anticipate that this process, by being autonomous as opposed to manual and by enforcing optimal TF trajectories, will result in a more efficient, repeatable, and robust hiPSCs to dHECs conversion protocol, thereby helping fill the gap to clinical translation. Although in this project we tailor the genetic circuit designs to controlling SLGE TFs after sensing mesoderm-specific transcripts, the designs can be readily modified to express different TFs in response to any other cell type- or state-specific transcript. Therefore, we believe that the synthetic biology technology that we will establish will have broad impact on any other cell fate programming as well as on cell-or gene-therapy projects where expression levels and timing, as well as resistance to silencing, are important.

NIH Research Projects · FY 2026 · 2026-05

Neurons and other brain cells employ signal transduction networks to convert cellular inputs into cellular outputs. For example, brain cells receive inputs (e.g., neuromodulators such as dopamine and norepinephrine) which trigger the production, entry, and/or release of a diversity of intracellular messengers (e.g., Ca2+, cAMP), engage proteins that enduringly change cellular state (e.g., kinases such as PKA and MAPK), and drive changes in the cell (e.g., plasticity, changes in gene expression, and synthesis or release of other molecules). Ideally, one could image different parts of a signaling cascade in the same cell, so that the relationship between different signals could be understood, in the brain of an awake behaving animal, without requiring a biology group to purchase expensive new hardware. To enable essentially arbitrary numbers of signals to be imaged at once, with ordinary microscopes, we propose a radically different way to image many signals in the same cell – simply place different reporters at different, randomly located, but stable, places throughout a living cell. Then, while the cell is alive, each punctum of reporter will report the signal measured by that reporter, at that site. Our first paper on such spatially multiplexed imaging, published in Cell (and featured on the cover) in 2020 (Linghu*, Johnson*, et al., Cell, 2020), reported a modular protein design that uses self-assembling peptides, fused to existing genetically encoded indicators of cellular signals, and to reporter-identifying epitopes that could be stained for, later. Such self-assembling peptides cluster the reporters at random, stable points throughout cells, resulting in what we call signaling reporter islands (SiRIs). Live imaging can proceed with a simple microscope, to image many things at once. Then, the specimen is preserved, and epitopes that distinguish the indicators can be stained, to identify the reporter at each punctum. SiRIs are ideally spaced close enough to sample the relevant biology, but far enough to be resolved by a microscope. We now submit this first grant aimed at extending the SiRI toolbox to become powerful, simple toolbox for neuroscientists. Specifically, we will (Aim 1) using generative AI, protein design, and end-to-end screening and validation techniques, create 10 novel SiRIs, validating the resulting sensors in both efficacy and safety, and sensor quality, in cultured mouse hippocampal neurons and mouse hippocampal brain slices; (Aim 2) design, optimize, and validate SiRI for 7-transmembrane protein-based fluorescent reporters of neuromodulators and other cellular inputs (mSiRI); and (Aim 3) optimize, and validate, SiRI/mSiRI for in vivo usage, creating a pipeline for expressing, and imaging, SiRI reporters in the awake mouse cortex, and further validating SiRI reporters on two signaling pathways (the astrocytic lactate shuttle, and GPCR signaling cascades triggered by norepinephrine). Our goal is to deliver to the neuroscience community a powerful, easy to use method systematically imaging signal transduction cascades, to understand how they work. We will share all tools freely as we have in the past, with our previous tools in use by many thousands of scientists around the world.

NSF Awards · FY 2026 · 2026-04

This award provides participant support for the conference “The Gross–Zagier Formula, 40 Years Later,” which will take place at the Massachusetts Institute of Technology from August 3-7, 2026. The Gross–Zagier formula is a fundamental result in the mathematical field of number theory relating a special solution of a certain degree-three Diophantine equation to the vanishing of a special function called an L-function. This result has provided some of the strongest evidence to date for the conjecture of Birch and Swinnerton-Dyer for elliptic curves, one of the seven Millennium Prize Problems. The conference will feature lectures by experts covering a broad range of recent advances connected with the Gross–Zagier formula, its generalizations, and the work it has inspired. Most participants will be early-career researchers who will benefit from the conference’s stimulating environment and fresh perspectives. New directions have recently emerged surrounding fundamental questions connecting special values and derivatives of automorphic L-functions with algebraic cycles, the method of Euler systems, and period integrals of automorphic forms. This has inspired applications to generalizations of the Birch and Swinnerton-Dyer conjecture for higher-dimensional algebraic varieties. The conference will showcase an array of advances, including those related to Kudla’s program, the Gan–Gross–Prasad conjecture, and the relative Langlands program. Some lectures will be survey talks outlining different branches of current research related to the Gross–Zagier formula, with the aim of introducing the main themes to early-career participants. More information about the conference can be found at https://math.mit.edu/events/gross-zagier/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This NSF CAREER project aims to develop foundations that allow autonomous systems to learn from data while reliably respecting safety constraints. AI-enabled vehicles, robots, and infrastructure controllers are moving into public spaces and critical services, where rare mistakes can cause injuries or cascading disruptions. Many modern learning components provide limited guarantees: they may violate constraints, fail when conditions differ from training data, or scale poorly when many decision makers must coordinate. The project will bring transformative change by making safety and reliability a built-in property of learning-enabled autonomy—from individual neural networks, to learning-based controllers, to large multi-agent systems—thereby promoting the progress of science and advancing the national health, prosperity, welfare, and security. This will be achieved by combining hard-constrained learning architectures, run-time uncertainty monitoring, and scalable decentralized decision-making tools. The intellectual merit of the project includes new mathematical theory and efficient algorithms for constraint-satisfying learning, uncertainty-aware and uncertainty-averse decision making, and safe coordination in multi-agent systems. The broader impacts of the project include safer autonomous transportation and robotics, more reliable and energy-efficient operation of engineered systems, open-source tools and datasets, and integrated education and outreach that strengthen K–12 through graduate training and engage the public through interactive demonstrations. Technically, the research comprises three coupled thrusts. Thrust 1 will create Hard-Constrained Neural Networks (HardNet), which add a differentiable projection layer so input-dependent constraints are satisfied by construction during training and deployment. HardNet will be embedded in reinforcement learning (RL—learning by trial and error) to produce certifiably safe policies, and in boundary control of partial differential equations (PDEs—models of distributed physical processes) by enforcing Lyapunov stability conditions as hard constraints. Thrust 2 will develop model-agnostic run-time uncertainty metrics for pre-trained perception and representation models using neighborhood-consistency tests and scalable curvature “sketches” (efficient sensitivity summaries), enabling detection of out-of-distribution inputs without costly retraining. These signals will guide adaptive “uncertainty-aware” and proactive “uncertainty-averse” policies, and robust adaptive safe RL that adjusts online rather than relying on pessimistic worst-case design. Thrust 3 will scale these guarantees to many agents via decentralized safe multi-agent RL that maintains controlled-invariant safe regions using Hamilton–Jacobi reachability (computing safety envelopes), federated RL that personalizes collaboration under heterogeneous dynamics, and online incentive mechanisms that satisfy global constraints under incomplete information. The methods will be validated in robotics, transportation, and building-energy testbeds and disseminated through open educational materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract The small intestine, central to nutrient absorption like amino acids and lipids, houses highly responsive Lgr5+ intestinal stem cells (ISCs) in the crypt bottom. Over the past decade, our group and others have utilized the mouse intestine to investigate how dietary interventions (fasting, high fat diet, mitochondria pyruvate shuttle inhibition, and high cholesterol diet) impact ISC fate decisions. Although much focus has been on ISCs, the small intestine is a complex environment that includes a variety of non-epithelial cells including resident immune cells that coordinate ISC function and maintenance. In particular, tissue-resident immune cells produce and secrete the cytokine interleukin-22 (IL-22), which is known to be a critical regulator of epithelial homeostasis. Amino acids constitute many nutrients in various foods. However, little is known about how specific amino acids impact ISC proliferation and intestinal immune-stem cell interactions. My research has uncovered that the amino acid cysteine controls ISC function through two mechanisms: 1) by directly activating PPAR-CPT1A-HMGCS2 mediated ketogenesis in ISCs via mTORC1 suppression, and 2) by indirectly boosting IL-22 production by CD8β+ T cells through activation of epithelial Coenzyme A (CoA) biosynthesis. The aims of my proposal focus on elucidating both stem cell-intrinsic and extrinsic mechanisms by which cysteine enhances ISC-mediated repair after injury. Specifically, I plan to: 1) determine how cysteine regulates ISC self-renewal and differentiation in intestinal homeostasis and injury through the control of ketogenesis; 2) determine the cysteine metabolic pathways that contributes to ISC mediated repair after injury; 3) determine how CD8β+ T cells mediate the cysteine response in ISC-mediated repair after injury. This career development K99/R00 award will be essential to my training and provide significant support as I transition to an independent investigator. It will protect me to receive the comprehensive education and training through the robust MIT/Harvard system, coupled with the exceptional research resources, fruitful partnerships will uniquely position me to embark on an unparalleled journey as a rising independent investigator in stem cell metabolism research.

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY/ABSTRACT The development of pharmaceutical compounds and their late-stage analogues is crucial for the advancement of human health through the synthesis of novel pharmaceuticals. Arene rings are especially prevalent in pharmaceutical compounds, making their derivatization highly desirable. Arene epoxidation, once considered a deleterious side product of metabolism, has the potential to be reimagined as a useful mechanism of arene activation under this proposed training plan. Notably, small molecule catalysts able to achieve this transformation are lacking within the literature, necessitating the development of a novel catalyst. Vast data sets within the literature for alkene epoxidation can be utilized as a starting point for catalyst design, but this abundance of data can be challenging to manipulate alone. Furthermore, machine learning has emerged as a valuable tool for the analysis of large data sets, and increasing interest has been applied towards its use for catalyst design and development. Utilization of transfer learning models would therefore enable known literature data to be applied towards a novel catalytic system. Achievement of this catalyst design through machine learning would unlock a platform for activation of a diverse array of arene building blocks for synthetic and medicinal chemistry. Therefore, the specific aims of this proposal are in alignment with the goals of the NIH in the advancement of human health and society. The first Aim of this proposal is to design an arene epoxidation catalyst using transfer learning from native enzyme reactivity. Aim 2 endeavors to optimize the initial catalyst through transfer learning from olefin epoxidation, and Aim 3 will utilize a recommender model and transfer learning to achieve the derivatization of the arene oxide intermediate into value-added products. This research proposal aligns with the fellowship goals by requiring new skills in the area of computational chemistry, machine learning, and catalyst design that complement my previous training in synthetic organic methodology. The Elkin lab provides the ideal research environment to complete this training, as their research focus lies in the area of machine learning for the development of predictive models for transition metal catalysis and total synthesis. Furthermore, additional guidance from Professor Steve Buchwald will be provided, and he has expressed his support and dedication to the scientific and professional development goals of this training plan. The Buchwald lab’s extensive expertise in ligand design and catalyst development will be instrumental in the execution of this training plan, especially in my catalyst optimization campaign. With the goal of securing an academic position at a research university in the future, the Elkin group with the additional support of Professor Buchwald will represent the ideal training grounds for this career goal. Furthermore, MIT is one of the most productive research institutions with ample resources and equipment in order to achieve the proposed research training plan.

NIH Research Projects · FY 2025 · 2026-03

The epigenome controls cell type-specific gene expression, establishing the diversity of cell types in the human body. However, over time, the epigenome becomes dysregulated, which promotes aging. Despite the tight link between epigenetics and aging, the mechanisms that preserve the epigenome in young cells and why these mechanisms degrade over time remain poorly understood. Polycomb-mediated gene repression maintains cell identity by silencing the genes that specify other cell types. As facultative heterochromatin, Polycomb is highly dynamic during development, enabling differentiating stem cells to rapidly alter gene expression programs. However, Polycomb switches from being flexible during development to becoming a stable mechanism of repression throughout adulthood. Understanding Polycomb regulation is key to advancing our knowledge of aging, as disrupting Polycomb components alters lifespan across various organisms. How Polycomb repression is maintained in terminally differentiated cells remain unknown. However, studies in embryonic stem cells indicate that spatial organization of repressed sites is crucial, with Polycomb-repressed regions forming ultra-long-range loops to sustain silencing. While these loops were thought to be solely mediated by Polycomb complexes, preliminary work from the applicant shows that cohesin and CTCF (which facilitate long-range enhancer-promoter loops) also mediate repressive loops in embryonic stem cells. In the F99 phase of this proposal, performed at MIT, the applicant will use computational methods developed by the Mirny and Dekker labs to determine whether cohesin and CTCF-dependent looping is a broad regulatory mechanism of Polycomb repression. Aim 1.1 will identify Polycomb targets in embryonic stem cells that derepress when cohesin or CTCF is lost and Aim 1.2 will use mechanistic polymer modeling to link cohesin and CTCF’s roles in 3D looping activity to Polycomb repression. In Aim 1.3, machine learning and polymer modeling will predict how gene expression in different cell types, particularly mature hepatocytes, respond to site-directed CTCF perturbations. These insights will propel the applicant’s transition to aging research, where she will test whether enhancing cohesin activity can protect Polycomb repression in aging mouse livers (Aim 2). The K00 phase will also use cutting-edge and single-cell experimental techniques to measure genome re-organization as Polycomb becomes dysregulated during the normal aging process. In addition to training in machine learning and hepatic chromatin, the applicant will gain expertise in aging research during the F99 stage through lab visits, conference attendance, and a course on aging and its diseases. This study will advance our understanding of aging by comprehensively investigating a new mechanism of Polycomb regulation. Rejuvenating the epigenome is a promising strategy for reversing cellular aging, and this work will determine if targeting the 3D genome offers a new approach.

NIH Research Projects · FY 2026 · 2026-02

Specific Aims: NAMs Technology Development Center for Women’s Health, “NAMs TDC-WH” The “NAMs TDC-WH” brings together an international multidisciplinary team, from basic scientists to clinical practitioners, lower the barriers for developing new drugs to treat a spectrum of gynecology disorders ranging from endometriosis to heavy menstrual bleeding and polycystic ovary syndrome (PCOS). This overarching goal will be facilitated by an existing well-funded core infrastructure at MIT in Center for Gynepathology Research that links clinicians, engineers, and scientists in academia and industry to build living patient avatars for endometriosis, adenomyosis and other gynecology diseases (Figure 1. The living patient avatars that will be rigorously tested and translated in the NAMS TDC-WH have been developed since their inception as “combinatorial NAMs”. Computational systems biology and bioinformatics analysis of clinical data (deep phenotyping and -omics) guided the design and operation of microphysiological systems (MPS) to capture key biological phenomena involved in disease and response to drugs, with performance of the MPS benchmarked against in vivo data. For this proposed project, we expand the computational and the clinical teams to capture a more comprehensive picture of the patient population, including genetic diversity and evolving aspects of the pain phenotype. We focus primarily here on combinatorial NAMs for female reproductive tissues, but include liver and other organ systems, recognizing the systemic nature of these diseases and the requirement that any drugs developed with gynecology tissues as a target require metabolic and safety assessments in a systemic manner. We test the hypothesis that pain phenotypes in humans relevant for endometriosis can be captured adequately via the combinatorial NAMs approach. The aims of the TDC-WH are to (i) elevate these technologies to the rigorous criteria needed for regulatory activities related to planning and interpreting human clinical trials for these drugs (ii) further strengthen the ties to the clinical phenotyping, genetics and bioinformatics communities whose input are needed to design physiologically relevant NAMs and benchmark them against in vivo for specific therapeutic targets (iii) translate the NAMs into use via example pre-clinical use cases in collaboration with Pharma. Finally, in addition to the technologies that have already been commercialized or in the process of being commercialized, we aim to translate the new platform technologies under development into wide availability through commercial partners. This will be accomplished through a comprehensive spectrum of education and outreach activities, including videos, hands-on tutorials, and translation of alpha versions of technologies for early feedback on user experience.

NSF Awards · FY 2026 · 2026-02

Entangled materials—such as polymer networks, textiles, and steel-cable structures—are found across length scales and exhibit remarkable mechanical properties driven by both fiber properties and the complex ways fibers entangle and self-contact. However, their behavior remains difficult to predict and design due to the lack of simple models capturing their intricate geometries and physical interactions. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will address that gap by developing quantitative metrics of entanglement through experiments and microscopy across scales. These metrics will connect entanglement geometries to physical properties, enabling the creation of simplified digital network representations of complex entanglements. These representations will guide the design of future entangled materials with user-defined properties. The project will provide open-source tools and data to support scalable design and optimization of fabrics, textiles, and knits, particularly at industrial scales. Broader impacts include educational integration of network science across institutions and a public art exhibit that will focus on visualizing networks, aiming to raise awareness of network-science-driven materials engineering. The project will establish a closed-loop framework for describing entangled matter using physical networks, correlating structural features with mechanical performance, and using these insights for targeted design. It consists of three unified thrusts that combine theory, computation, and experimentation. To span multiple length scales, the team will use testbeds made of 3D-printed textile architectures and woven metamaterials. Quantitative mechanical measures of entanglement will be obtained both experimentally and numerically. This data will inform the development of network models in which filaments are converted into skeleton and contact networks with geometric and topological attributes. These models will then be used to optimize entanglement geometries for desired performance using graph neural networks and gradient-based refinements. The result will be new material prototypes with engineered entanglements and mechanical properties, along with a broadly applicable design methodology for entangled filament-based materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY Over 7 million Americans suffer from Alzheimer’s Disease (AD), presenting a major health burden to patients and their caregivers, and claiming over a hundred thousand lives annually. AD remains without any effective cure or treatments, despite decades of research into small molecule- and antibody-based therapeutics. In contrast, next-generation therapeutic nucleic acids (TNAs) such as short interfering RNAs (siRNAs), anti-sense oligonucleotides (ASOs), and messenger RNAs (mRNAs) have major potential to treat AD due to their ability to modulate the expression of nearly any gene in any cell-type, if only they can be delivered safely and effectively to target brain cells that reside across the blood-brain barrier. Non-viral vectors are a promising new class of delivery vectors that could in principle transport TNAs to specific regions and cell types of the brain for potent, durable gene modulation to revolutionize clinical treatment of AD and other neurodegenerative and neurological disorders and diseases. The intrathecal administration route has emerged as a leading technique for delivering TNAs as it bypasses the intractable problems associated with blood-brain barrier crossing and is well-suited for clinical translation. However, there is a lack of non-viral delivery technologies that are well tolerated with wide therapeutic windows, can incorporate several different TNA modalities including both oligonucleotide and long nucleic acid cargos, can be repeatedly administered, are simple to manufacture to treat large patient populations, and can target specific regions and cell types of the CNS. Nucleic acid nanoparticles (NANPs) are a unique, new non-viral vector that address many of the desired performance properties. This project seeks to develop NANP formulations that distribute to specific CNS regions and cell types after intrathecal administration to deliver therapeutic siRNAs and mRNAs for downstream treatment of AD. Success of this research would establish a non-viral vector for these therapeutic modalities that can be used to treat AD and a range of neurological and neurodegenerative disorders of the CNS beyond AD.

NSF Awards · FY 2026 · 2026-02

One of the most important applications of fluid dynamics is improving the design of turbomachinery. These devices exchange energy between a fluid and a rotor. They are used in many applications such as marine propulsion, aircraft, and ocean energy technologies. They generate complicated turbulent flows that are difficult to analyze. On top of that, turbulent flows in the atmosphere or ocean interact with the flow generated by the turbomachinery, which is often ignored in engineering analysis. This project will develop new computational tools to analyze turbomachinery flows in realistic operating environments. It will generate accurate models for modern turbines and uncover effects of environmental flows on turbine performance. Results will be checked against wind tunnel and field data. The models generated in the project will be open-source and shared with the public on GitHub. The project will also develop hands-on activities for K-12 students to learn about wind and power generation. The outcomes of the project will help improve U.S. energy infrastructure and encourage students to pursue careers in energy engineering. This project uses scale-resolving large eddy simulations to systematically unravel nonlinear interactions between stratified boundary layer flows, rotor aerodynamics, turbine wakes (momentum and turbulence), and array/boundary layer-scale entrainment and wakes. To guide design and control optimization with accurate predictions, this project develops open-source fast engineering models of these coupled, multi-scale dynamics. The models will be developed using targeted decompositions that first parse rotor aerodynamics from wakes, enabling thrust and power predictions using first principles, and then isolate turbulent wakes from the background stratified, turbulent boundary layer. This project will validate the models using large eddy simulations, wind tunnel experimental data, and field data. The first objective uses scale-resolving large eddy simulations to elucidate the coupled impacts of rotor operation and velocity shear on rotor thrust and power. This project then models synergistic rotor and boundary layer effects on turbulent wakes and the large-scale environmental response to arrays of energy harvesting devices, resulting in an engineering model that couples the predictions of turbine-scale wakes and array-scale entrainment and yields lower error than existing models that neglect or parameterize shear, stability, and turbulence. Finally, the model will be extended, and validated using large eddy simulations, to predict large-scale wakes of arrays. Together, this project produces cross-cutting impact for applications including stratified turbulence and high Reynolds number geophysical flows. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Robots have come a long way in the past decade, but they still cannot reliably traverse or manipulate complex environments in the real world. This Faculty Early Career Development (CAREER) award supports research that looks to address two key reasons for this capability gap by developing 1) simulation tools that can efficiently and accurately capture relevant physics – including deformation, fluid interactions, and orbital dynamics – and 2) control methods that can reason about complex systems while simultaneously offering interpretability and safety guarantees. These advancements look to enable robots that can more efficiently, reliably, and safely interact with their environments and, therefore, bring us closer to a future in which robots are widely deployed to perform dangerous or tedious work. These robots could save lives by performing crucial tasks in dangerous environments instead of humans, and advance science by exploring places that are completely inaccessible to humans like deep oceans, space, and other planetary bodies. The education and outreach components of this award will also help inspire and recruit the next generation of scientists and engineers by directly engaging elementary and middle school students in future space missions. This project addresses two high-level technical goals. The first is to develop better modeling and simulation tools for situations in which sufficient data for reinforcement learning is too difficult or expensive to obtain on hardware. The focus, in particular, will be on multi-physics simulation for the emerging application areas of space and underwater robotics, where current simulation tools are lacking. The second addresses the need for computationally and data-efficient control methods that scale to high-dimensional inputs like vision and tactile sensors and exploit modern parallel computing hardware like graphics processing units (GPUs). To achieve this, the rich intersection between classical data-driven behavioral control – which offers interpretability and a rich set of system-theoretic analysis tools – and state-of-the-art diffusion policy methods from machine learning will be investigated. The education and outreach components of this project will help recruit and train the next generation of scientists and engineers by inspiring students to pursue careers in STEM, mentoring undergraduate student researchers as they enter the field of robotics, and training graduate students in cutting-edge optimization, dynamics, and control 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 2026 · 2026-01

Understanding how mixtures of solid particles and fluids, such as those found in avalanches, volcanic flows, and river sediments, initiate and how fast they propagate is vital for natural hazards and improving engineering models of Earth systems. These particle-laden flows are complex because the solids and fluids interact in diverse ways: sometimes the mixture acts like a solid, other times like a liquid, and often something in between. This project brings together geoscientists, computer scientists, and engineers to develop new models that better capture these behaviors by combining laboratory experiments, advanced numerical simulations, and artificial intelligence (AI). By using AI methods that are designed to be transparent and interpretable, this work not only enhances scientific understanding but also helps build public trust in AI-driven tools. The findings will support a broad range of geoscience applications and improve forecasts of events that can impact lives and infrastructure. Educationally, the project supports a vertically integrated training model, where postdocs mentor graduate and undergraduate students in a collaborative, hands-on research environment. The team will also create publicly accessible AI tools, YouTube tutorials, and organize quarterly seminars to disseminate their advances in AI for geosciences. These efforts will help prepare a new generation of researchers skilled in both scientific computing and Earth science. This project aims to discover and validate an elasto-viscoplastic (EVP) continuum rheology for dense granular suspensions under varying stress conditions, relevant to natural and engineered geophysical flows. Three central scientific questions guide the research: (1) how to represent stochastic force chains in a continuum framework, (2) how to define a rheology accounting for competing fluid–particle and particle–particle interactions, and (3) how to incorporate nonlocal and memory effects in stress evolution formulated using integro-differential equations. The approach integrates laboratory experiments, discrete element simulations, and interpretable machine learning. A novel by-design interpretable AI framework will be developed to discover analytical integro-differential equations for the EVP rheological model, while physics-informed operator learning with Kolmogorov–Arnold networks will enable its reduced-order surrogate modeling for GPU-based numerical solvers. The resulting models will be deployed in a large-scale application involving melt extraction from crystal-rich magmas. Open-source software and educational content will support broad dissemination. Collectively, this project advances both geoscientific understanding and AI methodologies for modeling multiscale, memory-driven systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY/ABSTRACT Antibiotics have improved our ability to treat infections, but antibiotic resistance is now an urgent public health crisis, with global deaths from bacterial infections now exceeding 2 million annually. This rise in resistance demands innovative strategies to combat pathogens effectively. As an alternative to pursuing small-molecule antibiotics, I propose harnessing the body’s own immune mechanisms to combat pathogens effectively. I plan to promote interactions between bacteria and human glycan-binding proteins on immune cells for new antimicrobial therapies. This proposal aims to create “cellular glues” — molecules designed to enhance the interactions between immune cells and pathogenic bacteria, ensuring the effective destruction of the pathogens. I will develop glycopolymers that bind to glycan-binding proteins on the surface of the target pathogen, Pseudomonas aeruginosa (PsA), and glycan-binding receptors on immune cells, specifically antigen-presenting cells. I will assess the ability of glycopolymers to mediate 1) bacteria recognition, activation, and signaling by dendritic cells, critical informers of T cell response and instructors of adaptive immunity, 2) bacteria killing by macrophages, key phagocytic cells that also process and present antigens, and 3) improved innate and adaptive immune responses in vivo. I envision this approach will provide insight into how glycan recognition by human lectins influences immune responses to pathogens. Moreover, this versatile strategy could be expanded to tackle other multidrug-resistant bacteria like Enterococcus faecium and Klebsiella pneumoniae, as these pathogens share carbohydrate-binding proteins that are targetable by this design. A successful demonstration of the utility of this approach would lay the groundwork for directing immune responses against growing threats like fungi or even tumor cells.

NSF Awards · FY 2026 · 2026-01

This program (US-SCAR) supports and strengthens U.S. participation in the Scientific Committee on Antarctic Research (SCAR). SCAR is an international organization established in 1958 to facilitate international collaborations in Antarctic science, and SCAR serves as an advisor to the Antarctic Treaty System (ATS). The U.S. is one of 12 founding member countries of SCAR (there are currently 44 members), and Americans have consistently played a strong role in SCAR leadership. US-SCAR promotes the progress of polar science within the U.S. and the international Antarctic scientific community by facilitating the attendance of U.S. scientists, particularly early career researchers, at the SCAR Open Science Conferences and Symposia on Biology and Earth Sciences. The U.S. National Committee for SCAR is the Polar Research Board (PRB) (US National Academies), and funding for the PRB comes from various government agencies. The PRB budget does not include funding for US-SCAR activities, however. This program absorbs a variety of costs associated with U.S. participation in SCAR, including travel to meetings, enhancing outreach capabilities, and establishing a more effective format to ensure maximum US participation in SCAR initiatives. US-SCAR provides opportunities for U.S. scientists, especially early career researchers, to 1) present their work in an international forum, 2) learn about the most recent findings on all aspects of Antarctic science, 3) establish and strengthen international contacts and collaborations, and 4) be actively involved in the development of new directions and the establishment of new frontiers in the international framework of polar science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The project studies the impact of privacy interventions on the online publishing industry. Widespread concerns over online privacy have led governments worldwide to enact new privacy regulations and have compelled firms to implement self-regulatory data-protection initiatives. While such privacy interventions are often positively received by the public, industry stakeholders have suggested that privacy interventions may reduce the availability of free, ad-supported online content and services. Online publishers and their ability to provide content play a crucial role in democratic societies. The project integrates technical, organizational, behavioral, and economic studies to investigate how the implementation of privacy interventions impacts publishers, their users, and various downstream economic outcomes. The project aims at providing insights for scholars, policy makers, and practitioners toward understanding and designing effective privacy interventions without endangering digital products and services. The project leverages collaborations with media companies granting access to data from a large set of heterogeneous websites. The novel contributions of the project include: 1) the investigation of factors influencing publishers’ compliance with privacy interventions; 2) the creation of an open auditing API for automated remote compliance auditing of publishers’ systems; 3) the analysis of users’ responses to privacy mechanisms to understand the extent to which privacy initiatives translate into usable tools for users; 4) the investigation of the impact of privacy implementations on user engagement and experience; 5) the investigation of how the implementation of privacy initiatives affects publishers’ revenues from online advertising; and 6) the investigation of how the gains from user data are allocated across the stakeholders in the online publishing ecosystem in the presence of privacy initiatives. The collaboration with media companies enables quick transfer of research findings to the industry. The results of the research are incorporated in courses on online privacy and its impact on publishers and society. 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 · 2025-12

This award will fund about five U.S-based students' travel to the Security and Human Behavior (SHB) workshop to be held in June 2025. The goals of SHB are to discuss, in an informal and interdisciplinary setting, issues where security, psychology, and behavior interact. The topical scope is broad: topics that have been covered in the past include the misperception of risk, security usability, deception, and security and privacy decision making. The disciplinary scope is also broad, bringing together security researchers, psychologists, information scientists, economists, computer scientists, philosophers, law scholars, and others. The goal of the workshop is to develop individual researchers and a wider community that can address security issues with a deep understanding of social phenomena related to security decision-making and technologies. Student attendees will greatly benefit from participating in the workshop, where they will interact with research leaders from a variety of disciplines and will be exposed to the most recent developments in information security and privacy research. In particular, the first rule of the SHB Workshop is that everyone who attends participates in the discussion. This will ensure that students are more involved and improve the benefits from attending SHB than many conferences and workshops where they can simply listen. 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 · 2025-12

Growth is a quintessential feature of all living systems; understanding the mechanics of growth is crucial in a wide range of ecological, industrial, and medical settings. However, while there is an increasing appreciation for the significant role of mechanics in defining the growth and form of biological materials, the field has yet to provide a basic understanding of key mechano-morphogenesis processes and their sensitivity to various environmental factors, such as geometrical constraints and nutrient availability. To address this question, this collaborative project takes advantage of a highly tunable biological system that is capable of macroscale growth - bacterial biofilms. Confocal imaging and analysis of the growth process looks to enable detailed observation of various growth phenomena at both single-cell and continuum levels and can measure the influence of environmental factors. The parallel theoretical effort will stem from the derivation of theoretical models that integrate only the essential ingredients by which the biological system evolves seeking to provide an open-ended strategy to explore and expose rules and unexpected phenomena in morphogenesis. The research is likely to have direct implications for our understanding of the development and resilience of bacterial biofilms. The overarching goal of this collaborative research is two-fold: 1) to deepen the understanding of the development of biofilms in constrained environments; 2) to leverage the growth of highly tunable biofilm systems as a generic scheme for biological growth. The approach focuses on the development of theoretical models that are complex enough to contain the essential coupled mechanisms involved in growth and morphogenesis but are simple enough to explain the basic phenomena that may emerge and can serve as tools to expose additional unexpected phenomena. The first two objectives of this work study the separate roles of nutrient transport and mechanical stress using specially designed experimental setups that isolate the specific phenomena of interest in the embedded biofilm system. The third objective further iterates between the theory and the experiments by looking to capture the coupling between the different mechanisms and to explore ranges of response that are beyond reach of the experimental system. The developed models and the conclusions obtained from observations of bacterial biofilms system confined in hydrogels look to be applied to other cellular collectives or biological entities growing under mechanical constraints. These insights could be applied to several biomedical applications and potentially open new directions for studies on embedded biofilms, such as their antibiotic resistance. 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.