Massachusetts Institute Of Technology

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Iron is a micronutrient that is essential for nearly every living organism, and maintaining its homeostasis therefore represents a crucial biological problem across all scales: from cells, to tissues, to the whole organism. We propose that central regulation of local iron supply in the adipose tissue is controlled by VAMs (vasculature- associated macrophages), which are resident tissue macrophages that associate closely with the vasculature and perform several key homeostatic maintenance functions within the tissue. While VAMs and other tissue macrophages have been historically linked to host defense/innate immunity, there is a major gap in our understanding of their diverse functions beyond immunity, and how their dysfunction drives the progression of chronic inflammatory diseases such as type 2 diabetes. Gene expression profiling indicated that VAMs express an array of genes involved in iron handling, including iron import, export, processing, and storage. Our preliminary data shows that VAMs take up significant amounts of transferrin - the main iron carrier in blood serum - and also serve as an iron storage depot by housing significant amounts of intracellular iron. Together, we hypothesize that VAMs are the main regulator of white adipose tissue iron content, constantly monitoring the demand and controlling the local supply of iron to other cells. This study aims to elucidate the tissue-level iron handling functions of VAMs by 1) Identifying the primary iron uptake pathway in VAMs. 2) Ascertaining the role of VAM-mediated iron export and storage in regulation of adipose tissue iron content and systemic metabolism. 3) Uncovering the molecular mechanisms for adaptation of adipose tissue cells to perturbations in local iron availability. To answer these questions, we developed novel mouse models that target tissue-resident macrophages with very high specificity, thus overcoming a major barrier that currently exists in the myeloid field. This study will advance our understanding of the multifaceted roles of our immune system beyond immunity, with a focus on achieving deep mechanistic understanding of macrophage-mediated support of tissue physiology and specifically of tissue-level iron homeostasis. It may inform the biology of several diseases characterized by iron dysregulation, including type 2 diabetes, hematological and neurological disorders, and cancer. The results of this work may influence the development of treatments aimed to restore the proper cellular functions of dysregulated resident macrophages. This is a dissertation project which will be conducted in the lab of Dr. Hernandez Moura Silva at the Ragon Institute of Mass General, MIT, and Harvard, which has all the necessary facilities to perform the experiments described and provides an excellent training environment for the investigation that spans across the fields of immunology, physiology, and biochemistry. The proposed training plan will strengthen the candidate’s preparation to a career in research & development in an industrial sector, where interdisciplinary skills are essential.

NSF Awards · FY 2025 · 2025-07

Large language models (LLMs) have led to significant progress in natural language processing (NLP) and artificial intelligence more broadly, and have the potential to become a broad technology with applications across myriad domains. However, current LLMs rely on computationally expensive architectures and algorithms. This CAREER project aims to develop new methods for efficient, architecture-aware algorithms for language modeling that is expected to make existing LLM applications more efficient, enable new applications, and broaden access. To achieve these goals, this project will develop new methods that span the entire training and deployment pipeline, including: (1) architectural primitives that can overcome the computational inefficiencies transformers, (2) efficient training algorithms that will reduce the amount of resources required to train and finetune LMs, and (3) quantization algorithms along with flexible kernels that can better utilize the computational resources of modern hardware. 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 · 2025-07

This project aims to develop mathematical foundations for understanding and improving graph neural networks (GNNs), which are widely used machine learning models for data with graph structures. Such data arises in recommender systems, molecular modeling and a range of scientific and technological domains. While GNNs have achieved notable empirical success, key theoretical challenges remain, including limited expressivity, suboptimal performance on specific graph types, and performance degradation in deep architectures. This project addresses these challenges by building and analyzing principled models that are both expressive and computationally efficient. The research outcomes will contribute to the development of robust machine learning tools for analyzing complex graph-structured data. Undergraduate and high school students will be actively involved through mentoring and educational programs. The project combines mathematical analysis and model design to advance the theory and practice of graph learning. It will pursue three interconnected directions: (1) developing GNN architectures for solving quadratic programs, a broad class of optimization problems; (2) analyzing the expressivity of subgraph GNNs on graphs with bounded cycles, which frequently occur in applications; and (3) designing new approaches to mitigate the oversmoothing phenomenon in deep GNNs. The work will draw on techniques from graph theory, optimization, and neural network theory. These efforts aim to provide a deeper theoretical understanding and practical advancements in graph learning. 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 · 2025-07

This award funds a research project that combines extremely rich and detailed data with economic theory to study how the burden of market failures is shared in the economy. Market failures prevent resources, such as labor and capital, from being used in the places where they are most valuable. While it is known that market failures are a feature of all economies and contribute to income differences across countries, the question of which types of households are affected most by these market failures is much less researched. This project makes progress on this gap by developing tools to connect and analyze multiple datasets that provide an extensive set of links in the economy between households and firms, between both households and firms and the government, and between the firms themselves. Understanding which groups benefit and which groups lose out from policies that address such market failures is vital to the design of approaches that maximize the welfare of any nation. The research outcomes could be essential for researchers and decisionmakers in shaping optimal U.S. policies and potentially enhancing the wellbeing of households and businesses. This award funds a research project that develops tools to estimate distortions—markups, markdowns, and taxes that prevents resources being allocated to their best uses—at a highly disaggregated level and trace them to individuals through arbitrary trade, employment, and financial networks. This methodology is applied to administrative data, where the research team can map out the flow of goods and money for the entire economy by linking firm-to-firm networks with firm-to-consumer, firm-to-lender, and firm-to-employee networks alongside ownership registries. These methods reveal how the burden of distortions is shared among households belonging to different regions, demographic groups, skill groups, and income levels. In addition, they enable answers to various questions, such as which distortions do the most to compress and expand the distribution of living standards; what trade-offs are observed in approaches designed to improve the impacts of distortions; and to what degree overlapping distortions necessitate our wide-ranging analysis as opposed to focusing on one specific distortion or sector at a time. The theory and methods are developed in a way that they can be applied to any country with similar data. This project advances knowledge in several ways. First, it operationalizes recent theoretical work on general equilibrium models of heterogeneous agents in distorted economic environments. Second, by assembling a complete empirical mapping of economic relationships between agents in an economy, it measures the distributional impact of the main distortions that are present in an economy (those on labor, capital, output, and intermediate inputs) throughout all sectors. This is relevant because studying the impact of reducing distortions in one specific market is influenced by distortions in other markets. Thus, to fully assess the trade-offs of reducing distortions, one needs to go beyond specific distortions and specific sectors. The results have the potential to significantly reshape how economists perceive the implications of market distortions and the policies responding to them. By addressing who bears the costs and who benefits from market distortions, a key theme for decisionmakers, the findings of this research could lead to optimal policies related to market failures and enhance the welfare of the U.S. population. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The Summer Geometry Initiative (SGI) is an intensive summer program aimed at helping student researchers in the field of geometry processing. Geometry processing addresses algorithms, software, and theory relevant to computing over geometric data. These fields brings together mathematics, computer science, and engineering expertise needed to work with 3D models in a range of applications. The summer program provides opportunities for SGI Fellows to engage deeply with current research and work with experienced mentors, allowing them to engage with the research community and to build new collaborations across institutions and researcher levels (from undergrads to faculty and industry researchers). The SGI is co-located with the Symposium on Geometry Processing conference, a leading venue for this kind of work, further providing opportunities for SGI attendees to learn about and connect to the research community. This grant will support stipends, travel, and registration for about four (4) students who otherwise have limited funding and so might not be able to participate in the summer institute or the conference. Criteria for selection include financial need and growing the size and breadth of the geometry processing community, with a particular focus on providing opportunities for students who would not otherwise be likely to get involved in the field. Prior sessions of the summer program have had demonstrated impact on alumni's graduate work and careers, while elements of the summer program will also be made more widely available through a talk series and tutorials with accompanying 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.

NSF Awards · FY 2025 · 2025-06

Biodiversity is vital to the economy and future innovation opportunities, and we are currently witnessing an unprecedented loss of biodiversity. To better understand and hopefully mitigate this loss, networks of ground-level sensors, satellites, drones, and community scientists are deployed to collect natural-world data at unprecedented scales. There is valuable scientific information stored in these raw data, the vast majority of which are as-yet inaccessible due to the time and resources needed to process the data by small groups of relevant human experts. Computer vision (CV) will prove crucial to facilitate efficient extraction of scientific insights from quickly growing repositories of natural world imagery, but in order to realize the goal of global-scale, near-real-time biodiversity monitoring we must develop computer vision approaches keyed to challenges encountered in real world settings. This work formalizes and addresses cross-cutting limitations of current CV methods in the context of global-scale biodiversity monitoring, characterized in the following three research aims: (Aim 1) Robustly identify rare, visually similar, and even novel categories, all challenges separately for CV that co-occur in biodiversity data. We address this compounding challenge by augmenting limited training data and developing efficient active curation systems. (Aim 2) Adapt to new deployments and identify valuable data for specialized tasks. Biodiversity is non-uniformly distributed across the globe, and specialized models improve decision support. We introduce task-specific data selection as a specialization mechanism, and develop methods that adapt to new deployments over time while making optimal use of human effort. (Aim 3:) Share information and reason across modalities to fill data gaps and support scientific discovery. No single modality of biodiversity data captures the entire picture. We will develop data encoders that aggregate and share relevant information across modalities, and build on these encoders by developing interactive scientific AI agents that enable novel discoveries in data. These three research aims will be complemented by the development of cross-disciplinary educational programs that expand capacity at the intersection of CV and Ecology. Our proposed research innovations are necessary to enable robust, scalable CV deployments in ecological settings. Each aim outlined above will contribute towards our ability to deploy models with conservation partners and enable critical computer science/ecology collaborations. This combined effort will not only increase the utility of existing data and methods in the biodiversity domain, but will lead to advances in related application areas (including biology, astronomy, and neuroscience) as well as fundamental CV research challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Probability distributions fruitfully model phenomena in all areas of science and engineering. In the modern age of large-scale computing and "big data", these distributions are incredibly complex and high-dimensional. Sampling is a universal algorithmic primitive used to manipulate and understand such probability distributions. The key challenge practitioners face is designing efficient algorithms which are guaranteed to generate random samples from the correct distribution. This is a fundamental computational task which lies at the heart of a vast array of real-world applications, including the recent revolution in generative artificial intelligence, the design of algorithms which simultaneously operate on user data while protecting user privacy, the study of materials in physics and chemistry, and statistical inference in science more broadly. The goal of this project will be to develop new mathematical tools to analyze sampling algorithms used ubiquitously in practice. The project will focus on a few foundational problems in theoretical computer science which have been open for decades. The insights derived from this research will be leveraged to design new algorithms which are not only fast in practice but also come with mathematically rigorous guarantees on the quality of their output. This research will be conducted in collaboration with a graduate student and integrated into educational materials at the undergraduate and graduate levels. Sampling is a universal algorithmic primitive for manipulating and understanding complex and high-dimensional probability distributions, with applications to materials science, statistical physics, Bayesian inference, differential privacy, fairness, generative artificial intelligence, and more. However, the fundamental challenge practitioners face is designing efficient algorithms which are guaranteed to generate random samples from the correct distribution. This project aims to develop robust mathematical techniques for analyzing Markov chains, which are algorithms ubiquitously used in practice for sampling, optimization, and inference tasks. We focus on three basic problems along the boundary between computationally tractable and intractable, each of which captures a specific barrier in the field: (1) Prove that local Markov chains like Glauber dynamics efficiently sample a uniformly random proper coloring of any graph with maximum degree d as long as q >= d+2. This 30-year-old open problem is central to understanding the worst-case complexity of sampling for general graphical models in statistical physics and machine learning. (2) Show that, in polynomial time, local Markov chains achieve optimal correlation with the ground truth in the stochastic block model of community detection. This is an important problem towards explaining the empirical efficacy of Markov chains in solving optimization and inference tasks despite failing to efficiently converge to stationarity. The insights from this direction will also provide a foothold for designing new inference algorithms which succeed when naive Markov chains fail. (3) Given that real-world instances are typically not worst-case, understanding when and how we can circumvent known NP-hardness barriers for sampling is an important endeavor. A representative average-case problem in this direction is to design an efficient algorithm which samples a uniformly random independent set in a random graph. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-06

Project Summary Magnetic resonance imaging (MRI) is a widely used medical diagnostic tool. Nearly 50% of MR scans are of the head to diagnose ailments, injuries, and diseases including: 1) stroke; 2) other cerebrovascular conditions (e.g., aneurysm and atherosclerosis); 3) brain injury (concussion, TBI, hematoma); 4) tumors; 5) neurodegenerative disorders (e.g., multiple sclerosis, Alzheimer’s, epilepsy; 6) headache; 7) infections (e.g., meningitis). Standard MRI scanners, installed in special MRI rooms, operate with fields of ≥1.5 T. However, <<1 T scanners have been finding some niche applications in bedside and intrasurgical and interventional situations. Although some of these scanners are compact, and may be “portable,” they have a serious limitation of relying on permanent magnets and therefore suffer from low image quality. For example, Hyperfine, with the 0.064-T permanent magnet, markets a portable MRI system for patients in the hospital bed, which allows for critical decision-making in some clinical settings, but its images would never be considered to be of general diagnostic quality. In this project we introduce, for the first time, a portable superconducting 1.0-T head-dedicated MRI magnet, a field that yields proven diagnostic image quality. With the proposed innovative magnet technology incorporated, we believe that this superconducting head MRI will revolutionize point-of-care diagnostics, e.g., in triaging traumatic brain and other injuries at sports events, concerts, disasters, battlefields, and Hospital at Home. The specific aim of this 5-year project is to develop and demonstrate a portable head-imaging MRI magnet system. During operation, our proposed MgB2 superconducting 1.0 T magnet will be free from an external power supply and a refrigeration system. We will use off-the-shelf devices in a separate rack for MRI electronics; and use a custom designed head gradient coil cooled by a closed-loop water-to-air heat exchanger to dispense with a power-consuming water chiller. For our proposed portable 1.0-T MRI unit, we plan to use a detachable “cryocirculator” that circulates cold working fluid, and most importantly for portability, that can be readily coupled to or decoupled from the magnet system, in contrast to a conventional cryocooler that is mechanically attached to the magnet system. Another unique feature of our system is a volume of solid cryogen, e.g. solid neon, in the cold chamber that adds enough thermal mass to the magnet, enabling it to maintain its field over a period of, for this system, ≥8 hours, plenty enough for this portable MRI system, uncoupled from its cryocirculator, to perform its mission before it needs recooling.

NIH Research Projects · FY 2026 · 2025-06

Phosphorylated biomolecules play essential roles in human physiology, health, and medicine. Biological tar- gets for phosphorylation include nucleosides, lipids, amino acids, peptides, and proteins. A recent discovery is that protein oligo- or polyphosphorylation is an important post-translational modification, spurring researchers to synthesize chemical probes containing oligophosphate chains of specific lengths as tools to enable exploration of the human polyP-ome. This development exposes the need for well-defined chemical reagents to enable phosphate chains of a desired length to be conjugated to an organic molecule of interest. Previously we developed reagents for diphosphorylation, new methods for triphosphorylation and tetraphosphorylation, and now we will develop new reagent and methods for pentaphosphorylation and beyond. A related important innovation is the development of methods for covalently linking two (different) organic molecules by an oligophosphate chain. We will also de- velop improved strategies for synthesis of oligophosphate-organic molecule constructs having non-hydrolyzable P-C bonds. Our innovations will emphasize P(V)-based methods as these have the potential to be more efficient than existing P(III)-based ones. We will continue our collaboration with the Raines lab in which we are studying the potency of nucleoside oligophosphate constructs as inhibitors of RNase A as a model enzyme system; we plan to study the effect of increasing the oligophosphate chain length to include hexaphosphate, heptaphosphate, and even longer chain lengths. Small molecule ribonuclease inhibitors are valuable biochemical tools for studies of RNA for which success often relies on shutting down all ribonucleolytic activity. We are also targeting new con- structs bearing an electrophilic warhead linked to a nucleoside by an oligophosphate chain for covalent attachment of ligands to proteins. We are developing methods for attaching clickable moities to oligophosphate chain ends, to enable oligophosphate-organic molecule conjugates to be further attached to peptides or proteins. This is the basis for an exciting new collaborative project with the Raines lab on the decoration of proteins with oligophos- phate groups to render their surface polynegative for packaging inside of lipid nanoparticles for delivery into cells. The enzyme inhibition studies are complemented by state-of-the-art quantum chemical studies of protein-ligand interactions, studies carried out with theoretical and computational chemist Giovanni Bistoni (U. Perugia). To improve our protein crystallography capabilities we have started a new collaboration with the Drennan lab. We onboarded an exciting collaboration with the Fielder lab (FMP Berlin) to investigate the conjugation of oligophos- phate chains to polypeptides/proteins using our reagents amd methods, with the objective of elucidating the impact of these modifications on their structure and function, as well as the roles that endogenous oligophosphorylation may play in the natural regulation of enzymes. This has led us to develop new imidazolide oligophosphorylation reagents that are tolerant of water and highly selective for oligophosphate chain elongation of phosphoproteins.

NSF Awards · FY 2025 · 2025-06

From mobile networks and satellite systems to Internet of Everything (IoE) devices and Wi-Fi, seamless wireless connectivity continues to grow at an unprecedented pace. The demand for faster and more reliable wireless communication networks puts immense pressure on the available radio-frequency (RF) spectrum. Expanding into the FR3 band (7-24 GHz) alongside traditional sub-7 GHz frequencies is crucial to enable wider coverage, higher capacity, and faster data rates to support critical technologies and applications across various industries. However, as more devices and services rely on wireless communications, networks become increasingly crowded, leading to various types of radio interference among different systems. As spectrum environments become more dynamic and interference levels continue to rise, advanced interference-immune RF electronics hardware is needed. The techniques proposed in this project aim to address these challenges by introducing innovative microelectronic hardware solutions that can enhance the performance and resilience of future wireless systems in highly dynamic and congested spectrum environments. The new techniques can quickly adapt to interference, ensuring robust operation even under severe interference conditions of various forms. This project seeks to investigate RF (including mm-wave frequency bands) silicon-integrated radio front-ends with high dynamic range for out-of-band (OOB), harmonic, and spatial interference, enabling interference detection, mitigation, and hardware adaptation for improved system operation in shared, congested, and contested spectrum bands. Different use cases, from low-power IoE devices to high-performance communication systems, are considered. Microelectronic circuits and systems will be developed to support sub-7 GHz bands and FR3 band (7-24 GHz). The research efforts are delineated into the following three thrusts: i) A low-power harmonic-resilient software-defined radio (SDR) prototype will be demonstrated, which is enabled by a fully passive harmonic-reject mixer topology that provides harmonic rejection at its input, output, and all internal nodes. Furthermore, a compact low-power clocking scheme will be incorporated in the design which will allow seamless operation of the SDR in the presence of harmonic interferers with sub-milliwatt static power consumption. ii) Highly selective harmonic-resilient front-ends with enhanced RF selectivity concurrently targeting OOB and 3rd- and 5th-order harmonic interferers. iii) A spatial blocker-tolerant digital multiple-input multiple-output (MIMO) receiver for operation within the 7-24 GHz frequency band with RF-domain spatial notch filtering using a widely tunable nonreciprocal phase shifter will be developed. An autonomous notch-steering control loop with a fast settling time is presented to be integrated for interference sensing and system adaptation. Research tasks of this project include analysis, simulations, integrated circuit implementations, and demonstrations of the final prototype. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Hydrogen plays a crucial role in many technological fields including aerospace and nuclear fusion. Understanding how hydrogen moves through materials is a complex challenge that affects the design, safety, and performance of key technologies, such as fusion power plants and hydrogen storage systems. Unfortunately, existing tools for modeling hydrogen transport are either proprietary, expensive, or lack the necessary basis in physics, making it difficult for researchers and engineers to access and use them. This project supports the development of FESTIM, an open-source software tool that enables accurate hydrogen transport modeling, making advanced simulation capabilities widely available to scientists, engineers, and educators. By fostering a global community of users and contributors, this project lowers barriers to innovation, supports workforce development, and accelerates scientific discovery in hydrogen-related fields. This project, funded by Pathways to Enable Open-Source Ecosystems (POSE), establishes a self-sustaining Open-Source Ecosystem (OSE) for FESTIM, ensuring its long-term usability and impact. Key activities include expanding FESTIM’s contributor community, improving developer on-boarding resources, strengthening industry and academic partnerships, and implementing governance structures to sustain future development. The project also enhances FESTIM’s usefulness through better documentation, training workshops, and an improved software distribution system. FESTIM’s development is guided by best practices in open-source software, ensuring reliability, reproducibility, and broad adoption across multiple disciplines. By providing a high-quality, community-driven modeling tool, this project empowers researchers worldwide to tackle critical challenges in hydrogen transport which will accelerate advancements in energy and materials 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.

NIH Research Projects · FY 2025 · 2025-06

Project Summary A detailed understanding of the functional, anatomical and molecular architectures of brain cells and their brain-wide organization is essential for interrogating normal human brain function and disease states. Extensive efforts have been made toward mapping brain cells through various approaches, resulting in invaluable databases that are yielding new insights. However, we still lack comprehensive human brain atlases that capture multi-level properties of individual cells while adequately representing human demographic diversity and individual variability. The goal of this proposal is to create fully integrated three-dimensional (3D) human and non-human primate (NHP) brain cell atlases at subcellular resolution by simultaneously mapping brain-wide function, structure, and high-dimensional features (e.g., proteomic, transcriptomic, spatial, morphological, microenvironment and nanoscopic information) of cells acquired from the same whole brains with complete coverage of all brain regions. Our team will accomplish this by seamlessly integrating multimodal data from state-of-the-art functional (for NHP brains), structural, diffusion MRI, and multiscale 3D proteomic/transcriptomic imaging technologies. Using the scalable technology pipelines, we will perform proteomic and transcriptomic imaging of (1) human brains acquired from a large number of demographically representative donors and (2) functionally characterized non-human primate brains. The proposed work will create the most comprehensive 3D human and NHP brain atlases to date, with unprecedented resolution and completeness. The atlases will establish inter-species homologies essential for the translation of insights derived from animal models to humans, and aid in identifying the cellular and molecular underpinnings of human cognition and susceptibility to disease. Additionally, the unprecedented, multiscale multi-omic datasets acquired from a large number of demographically representative human donors will enable population level inter-individual variation study and allow baseline characterization of cellular and subcellular features, providing an immediately useful reference frame for the research community to study disease-specific changes in cell composition, spatial distribution, and subcellular architectures. Finally, our team will work closely with existing BICAN centers to maximize synergy and utilization of scarce brain materials and enable true multimodal data integration.

NSF Awards · FY 2025 · 2025-06

This I-Corps project focuses on the development of affordable and widespread access to cell therapies. Current cell therapies are already extremely effective at treating certain cancers, with 85% remission for liquid tumors. However, manufacturing complexity and the high price of necessary raw biologic materials drive up the price, with less than 7% of eligible patients receiving this lifesaving treatment. This technology enables autonomous multi-step differentiation of stem cells into therapeutic cells, eliminating the need for constant monitoring by skilled scientists and expensive biologics during production. This innovation reduces overall costs by over 50%, enabling widespread adoption of numerous cell therapies. Current cell therapy manufacturing demand is valued at $22.5 billion. As cell therapies address more diseases, the manufacturing bottleneck is going to extend to applications in solid tumors, regenerative medicine, aging, biotechnology research, and synthetic biology. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of artificial neural networks within living cells to enable precise control of cellular behavior. The technology includes two key innovations. First, it implements analog computation in cells through silencing-resistant biological circuits that mimic neural network architectures, allowing cells to process complex information and make autonomous decisions. Second, it leverages a novel artificial intelligence (AI) architecture that can both predict circuit behavior and generatively design new circuits to achieve desired cellular functions. The approach has been shown to enable sophisticated control of cell state transitions while maintaining long-term circuit stability. Initial results demonstrate a 50% improvement in yield for stem cell differentiation into therapeutically relevant cells compared to conventional approaches. 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 · 2025-05

Project Summary/Abstract Proper spatiotemporal gene expression is fundamental to all biological processes. Cell-type-specific gene expression programs are driven in large part by enhancers, which are cis-regulatory sequences that determine when and where genes are activated in a cell-type-specific manner. Enhancers often have to regulate genes across large genomic distances, but the molecular mechanisms underlying long-range enhancer-promoter (E- P) interactions remain unclear. Cell-type-specific regulators are also often regulated by enhancer clusters, which have been proposed to activate target genes via a condensate mechanism. The goal of this proposal is to understand the mechanistic basis of individual and clustered enhancers in human cells. The MYC locus is uniquely suited to address questions about enhancer function because it is regulated by different enhancers in different cell types. In several cancers including lung and endometrial cancers, the respective MYC enhancers are focally amplified to form enhancer clusters, which is thought to drive MYC overexpression. In Aim 1, super-resolution live-cell imaging (SRLCI) will be employed to simultaneously visualize MYC E-P interactions and nascent transcription over time in living lung and endometrial cells, which utilize different enhancers located ~450kb and ~800kb downstream respectively. To further validate that the inferred models from SRLCI are accurate, perturbations to key molecular players will be performed followed by SRLCI. By integrating 3D genomics and polymer simulations, the proposed research will generate quantitative models of how different enhancers regulate the same promoter. In Aim 2, the hypothesis that enhancer clusters activate transcription via a condensate mechanism will be tested. Additional enhancers will be introduced in both lung and endometrial cell lines to generate enhancer clusters, and SRLCI will be performed to determine the mechanism underlying enhancer clusters. Similar to Aim 1, 3D genomics data will be incorporated to generate polymer models of how enhancer clusters impact chromatin structure and E-P interactions to drive transcription. Together, these aims will provide a comprehensive and quantitative mechanistic understanding of how dynamic E-P interactions regulate transcription in two different cell types. The proposed research will be conducted in parallel with career development training to develop the necessary skillsets required to achieve the applicant’s goals of becoming an independent PI at a research institution. In addition to experimental and computational training, the applicant will receive mentorship from both sponsors on oral and written scientific communication, leadership, lab management and responsible conduct of research. This will be complemented by the world-class academic environment in MIT’s Department of Biological Engineering, Department of Physics and the Broad Institute of MIT and Harvard, where the applicant will be able to take advantage of the wealth of resources to expand her conceptual understanding of transcriptional regulation and learn to approach biological questions from an interdisciplinary, holistic viewpoint.

NIH Research Projects · FY 2026 · 2025-05

PROJECT SUMMARY: The ability to imitate others is a hallmark of human cognition and culture, and underlie many aspects of our remarkable ability for thought and language. Songbirds have been established as a powerful model system through decades of research, allowing for a mechanistic understanding of how neural circuit dynamics control the learning and production of complex sequential behaviors. Songbirds acquire their songs through an imitation process reminiscent of human speech acquisition using a well-delineated set of brain regions. By listening to their parents (or tutor sing), young birds form an auditory memory—a template. By comparing their own highly variable babbling vocalizations to this template, young birds gradually refine their own song using a trial-and-error reinforcement learning mechanism that requires a dopaminergic performance signal. Songbird vocal imitations can have remarkable fidelity, often matching the tutor song with striking acoustic and temporal precision. How is this template memory stored, in particular the temporal structure? How is the template compared to the ongoing babbling vocalization? And how does the auditory circuit form an error signal? In our previous work, we found that the timing of song production is controlled by sequential neural dynamics within a premotor region known as HVC. HVC has also been hypothesized to play a role in the formation of the auditory memory used for imitation. This proposal aims to link these two functions within a single framework. We hypothesize that, akin to its role during song production, HVC acts as a clock during tutoring, forming a temporally precise memory of the tutor song that is actively recalled during motor production to guide learning. In preliminary experiments, we have found that thermal cooling of HVC during tutoring causes birds to subsequently develop songs that are faster than their tutor (Aim 1). Furthermore, preliminary experiments show that tutoring produces sequential patterns of neural activity in HVC, which we hypothesize are stored as dynamic sequential ensembles that form the ‘clock’ of the tutor memory (Aim 2). HVC also projects back to the auditory cortex, and we hypothesize that this projection transmits a copy of sparse sequential activity that serves to predictively cancel the auditory signal at each moment in the sequential ensemble. Preliminary experiments have revealed the song-specific adaptation and error responses predicted by this model (Aim 3).

NSF Awards · FY 2025 · 2025-05

For decades, scientists have been trying to harness the unique properties of quantum physics—the science of extremely small particles—to develop revolutionary technologies. This research merges biology with quantum physics to build devices that can manipulate individual particles of light with unprecedented precision, potentially transforming fields from fundamental physics to medical diagnostics to supercomputing. Because conventional semiconductor manufacturing of wafer-scale transistors for silicon-based computing is limited in its ability to pattern quantum materials, radically new manufacturing approaches are needed. While DNA, the genetic molecule of life, is classically known for its role in storing and propagating genetic information, an alternative use involves its structural, self-assembling properties that enable scalable, low-cost and environmentally friendly manufacturing at the nanometer-scale. This project establishes a sustainable, energy-efficient manufacturing framework that uses DNA to position quantum materials on silicon chips with extraordinary accuracy and versatility. This innovative approach bridges molecular biology and semiconductor fabrication, creating pathways to quantum technologies that may enable computers capable of solving problems in minutes that would require conventional computers thousands of years to complete. Beyond these scientific and technical advances, this work provides rich educational opportunities for students across multiple disciplines. The project actively engages students through partnerships with community colleges and workshops, while developing hands-on curricula to prepare students for careers in the emerging bioeconomy, an area identified as a national priority. This project develops a groundbreaking manufacturing approach to overcome fundamental limitations in quantum device fabrication. Currently, the semiconductor industry uses lithography—a technology for etching patterns onto silicon substrates to manufacture computer chips—but these methods fail to position quantum materials with the nanometer-scale precision needed. The project seeks to develop a sub-10 nanometer manufacturing framework for photonic quantum technologies through two complementary research thrusts. The first thrust focuses on spatially organizing individual quantum dots and quantum rods with sub-10 nm precision using DNA templates patterned on wafer-scale surfaces. A novel Cavity-Shape Modulated Origami Placement (CSMOP) technique is developed to position DNA templates with high fidelity and minimal background binding. Computer-aided design tools guide the patterning process, enabling predictive placement of quantum materials. The second thrust applies this approach to fabricate functional single-photon sources by integrating colloidal quantum emitters into photonic cavities coupled to waveguide circuitry. This research incorporates scalable biomanufacturing of DNA templates through bacterial fermentation and silicification of the resulting structures to ensure their long-term stability and performance. The integrated approach that spans the self-assembling biological molecule DNA with top-down lithography overcomes fundamental limitations of each technique on its own, establishing a general framework for quantum device fabrication that can be extended to other quantum materials including molecular qubits for transformative sensing and computing applications. This project is jointly funded by the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences, and the Division of Chemical, Bioengineering, Environmental, and Transport Systems and Division of Electrical, Communications and Cyber Systems in the Directorate for Engineering. 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 · 2025-05

Recent studies show that striatal astrocytes can affect and be affected by the ambient dopamine in the striatum. Electrophysiological studies show that striosomes also have reciprocal effects on dopamine, with powerful direct projections onto the dopamine-containing neurons in the substantia nigra pars compacta (SNpc) such that they can shut down dopamine neuron activity and then evoke a rebound excitation, likely playing a critical role in state transitions of behavior. In accordance with these data, we have found preliminary evidence that the striatal astrocyte activity and striatal dopamine activity are correlated tightly with each other while mice perform decision- making tasks. Large transients of astrocytic activity, with timescales on the order of seconds, occur around the transition of behavioral states from task engagement to non-engagement or vice versa. Prompted by these findings, we propose here to investigate whether striatal astrocytes, in cooperation with the striosomes of the striatum, exert a facilitative role in state transitions by modulating dopamine signals in the striato-nigro-striatal loop. We will ask whether astrocyte activity causally affects learning, state transitions of behavior, from engagement to non-engagement, from strategy shifting to judging cost-benefit options, and whether astrocytes track changes associated with striatal dopamine release and activities of striatal projection neurons in millisecond and second timescales. We propose here to seek the possible role of striatal astrocytes in transitions of behavioral states via modulation of the dopamine signals transmitted along the striato-nigro-striatal loops. The striatum is one of the core brain regions to implement reinforcement learning and make transitions from an exploratory/learning phase to an exploitation/habitual phase of behavior. The striosomes, which are neurochemically specialized striatal compartments, have been shown to be specifically involved in the development of habitual behavior and recently in switches between unengaged and engaged states of behavior. This then poses the question “how can striosomes make a transition in a behavioral state?”. We will also approach the issues of whether these astrocytic modulations are exerted locally in the striatum as well as, our main focus, through the striato-nigro-striatal loop, by modulating activities of strioSPNs and dopamine-containing neurons in SNpc. We will, for this work, combine many advanced state-of-the-art methods including optogenetics, chemogenetics, intersectional viral and transgenic mouse line approaches, novel enhancer-based cell-targeting methods combined with photometric imaging and the use of chemosensors for registering dopamine release. This work is novel and at the front edge of new concepts of neuromodulatory mechanisms in controlling cognitive processing in the brain. Thus, the proposed work is crucial for and aligns with the goals outlined in the Notice of Special Interest (NIMH) regarding neuro-glial interactions by directly addressing profound issues about key dopamine-related astrocyte involvement in functions affected by mental health disorders.

CIHR Grants and Awards · FY 202526 · 2025-05

Heart muscle cells can respond to injury and divide in newborn humans, but as these cells get more mature, they lose the ability to regenerate. My research investigates the signals that instruct heart cells to mature, allowing them to gain the characteristics that make for a functioning adult heart but lose the ability to divide after an injury. My initial evidence suggests that hydrogen peroxide acts as a molecular switch, coordinating multiple signaling pathways to promote heart cell maturation. Discovering how this switch works will help us to understand how to promote regeneration and repair the injured heart. Keywords: HEART DEVELOPMENT; HEART REGENERATION; REACTIVE OXYGEN SPECIES; GENE REGULATION; METABOLISM

NSF Awards · FY 2025 · 2025-05

The Millstone Hill Geospace Facility is a crucial observatory for studies of the near-Earth space environment or geospace. For nearly 6 solar cycles, the Facility has been an anchor for mid-latitude and subauroral geospace research, the latitudinal region where most of the US population lives. A multitude of processes occur in this region that can have adverse effects on critical infrastructure including communication and navigation networks, power distribution networks, and pipelines. The mission of the Facility is to observe and enable scientific studies of these important space plasma processes and phenomena. The Facility currently supports several critical and unique capabilities that include the large-aperture high-power Millstone Hill incoherent scatter radar, and the Global Navigation Satellite System based total electron content analysis system for worldwide observations. The project will support ionospheric and upper atmospheric research at the frontiers of geospace science. The expected advances include (1) deeper understanding of subauroral plasma and neutral dynamics, with focus on subauroral polarization stream, storm enhanced density, ionospheric conductivity and impacts of penetration electric field on the mid-latitude and low-latitude ionosphere; (2) investigations of links between the lower and upper atmosphere during periods of anomalous stratospheric polar vortex activity; and (3) studies of multi-scale structure of traveling ionospheric disturbances. The project will also train and motivate a new generation of space and radio scientists, engineers, and technicians through focused research opportunities, community interactions, and general public outreach. The project will also maintain and operate the Madrigal Geospace data system. Finally, this project will provide a strategic resource for the development of next generation radio science technology through (1) supporting community use of software radio systems for geospace radio science instrumentation; and (2) contributing to community professional development through the Millstone Affiliates program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-05

Regenerative abilities are widespread, suggesting deeply conserved roles in biology. However, these abilities vary dramatically across species: understanding conserved and novel mechanisms enabling varying degrees of regeneration has important implications for regenerative medicine. An enduring medical challenge is the ability to regenerate extensively damaged nervous systems. This presents unique problems, bridging fields and levels of analysis, from the molecular mechanisms of neural development to the neural activity dynamics driving behavior. Here, we propose to study fundamental principles linking neurodevelopmental processes to the functional organization of neural circuits to ask how they could enable network-scale regeneration. Our strategy is to use a new animal model, Clytia, that is small, transparent, and capable of regenerating entire genetically ablated neural subnetworks. This affords a novel, high-resolution platform to study regenerative processes at their interface with systems-level organization. In preliminary work, we used whole-animal calcium imaging and single-cell RNA- sequencing (scRNAseq) to identify intermingled subnetworks with distinct functional organizations. We found that inducible, genetic ablation of a particular subnetwork led to loss of a specific behavior. However, this subnetwork and behavior completely regenerated within days. These observations have raised exciting questions: how is network injury sensed and translated into regenerative programs? Where do newborn neurons come from and what rules guide them to their final positions? Are all subnetworks regenerative despite their dramatically different architectures? Do regenerative abilities shared between subnetworks reflect numerous solutions to the problem of network-scale regeneration or common, enabling principles? In Aim 1, we propose to systematically ablate genetically defined neural subnetworks to determine which are capable of regeneration, allowing for a powerful, within-animal comparative approach to understand enabling mechanisms. In Aim 2, we focus on intermingled subnetworks controlling feeding versus swimming, both of which are regenerative. We will determine which stem cells give rise to newborn neurons of each type and test models linking neuronal origins to migration patterns to the ultimate position of newborn neurons in regenerated networks. Lastly, in Aim 3, we will use scRNAseq and genetic manipulations to examine molecular mechanisms by which network damage is sensed and initiates these regenerative programs. Together, we expect this work to provide fundamental insights into rules that connect processes such as injury sensing, migration, and functional integration to systems-level organization to enable regeneration.

NSF Awards · FY 2025 · 2025-05

To use a web service today, Internet users must often reveal their private information to the web-service provider. For example, Internet users upload their photographs to online photo albums, reveal their interests to web-search engines, and disclose their favorite websites to Internet service providers. Sending sensitive data to web-service providers is a serious privacy risk: the provider could lose the user's data in a data breach, decide to sell it later on, or be forced to disclose it to a foreign government. At the same time, web services are indispensable. Thus, computer users currently have no choice but to hand over their sensitive data to web-service providers and to suffer the accompanying privacy risks. This project will develop a new suite of privacy-protecting web services that never see or process any unencrypted user data. This project's goal is to make it possible for everyday Internet users to enjoy the tremendous benefits of today's web while shielding them from the accompanying privacy risks. In addition, the educational aspects of the project will focus on the development of undergraduate content and an openly available textbook to teach security and systems in tandem. This project consists of three parts, each dedicated to the development of a different private web service. The first part focuses on private machine-learning inference: allowing a client to evaluate a large server-side machine-learning model on its private data (e.g., the client's photos) while revealing no information about the client's private input data to the machine-learning service. The second part focuses on private search: allowing a client to search over a server-side corpus of billions of documents (e.g., web pages) while revealing no information about its search query to the search engine's servers. The third part focuses on private web browsing: allowing a client to browse a web of hundreds of millions of text-based pages while revealing no information about which pages it is reading. Building each of these three private web services will require new technical tools. In particular, this project will develop a suite of new low-level cryptographic primitives, including new high-speed protocols for private matrix multiplication, new protocols for private nearest-neighbor search in high-dimensional vector spaces, and a new cryptographic primitive, distributional private information retrieval, which allows a client to privately fetch data from a remote database server at relatively low cost. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This research will lead to a better understanding of how the cell surface is organized, which could lead to breakthroughs in developing biotechnology, treating diseases, and improving our overall understanding of how cells communicate and function. This project exploring how cells carefully organize proteins and lipids on cell surfaces will advance biological discovery and have a broader impact on biology education. For undergraduates, this project will enrich the existing curriculum by offering undergraduate students an opportunity to participate in the research. This research will also support the training of graduate students, providing hands-on experience in cutting-edge techniques and critical thinking skills, and preparing them for careers in academia, industry, and beyond. This project will also support a brand-new initiative to teach cell biology educators about emerging topics related to our research. In the past decade, scientists have begun to appreciate an important mechanism of sub-cellular organization, the formation of membrane-less “biomolecular condensates” through liquid-liquid phase separation. The goal of this project is to understand how protein and lipid molecules coordinate the formation and function of condensates at the plasma membrane. The research will use biochemical reconstitutions of condensates to understand how phosphoinositide lipids and adaptor proteins function together to regulate signaling. Although phosphoinositide lipids are a minor component of cellular membranes, they play a central role in regulating membrane form, function, and dynamics within cells. The specific aims of this project are to determine the impact of membrane phosphoinositide content on condensate formation, size, composition, and function; determine how the recruitment of specific adaptor proteins impacts local phosphoinositide lipid composition within condensates; and determine how specific protein enzymes control local phosphoinsitide lipid composition within condensates. This research will reveal fundamental mechanisms by which proteins and lipids may coordinate the formation and function of membrane-associated condensates throughout the cell. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Cybersecurity is imperative for protecting the nation’s sixteen critical infrastructure sectors, from the electric grid to water utilities. Cybersecurity protects supply chains and safeguards and privacy of personal data on the Internet. Economic growth in the United States is also increasingly tied to the effective management of cyber risks. Insurance is one key method of managing such risks. Focused on the private insurance sector, the Cybersecurity, Insurance, and Resilience Center for Understanding and Innovation in Technology (CIRCUIT) is an intellectual and practical hub for empirical research on cybersecurity modeling and its applications in managing critical infrastructure, mitigating catastrophic risks, and understanding evolving threats. By improving how organizations anticipate, prevent, and recover from cyber incidents, CIRCUIT reduces the financial burden on taxpayers, who often bear the cost of cyber disasters and infrastructure recovery efforts. Through research, education, and outreach, the center develops tools and strategies that protect both public and private interests, ensuring that critical services remain operational and secure. The U.S. economy relies on value creation through entrepreneurship, innovation, and the knowledge economy. Consider that more than 85% of the value of leading companies is tied up in intangible assets, also known as the knowledge economy. These assets require robust intellectual property and data protections, making cybersecurity both an industry need and a policy imperative. Using this planning grant from the Industry-University Cooperative Research Centers (IUCRC) program, CIRCUIT identifies and addresses critical industry needs such as the development of uniform and precise policy language; the selection of performant contracted service providers to buttress policies; the creation of new data on the effectiveness of security controls to support underwriting; the identification of better exclusion strategies; and the automation of cyber risk underwriting with efficient auditing and claim management processes including empirically vetted security controls. These strategies, in turn, reduce the likelihood of costly recoveries after cyber attacks, strengthen national security, and foster economic stability by ensuring that businesses and essential services can recover quickly from cyber threats. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-04

Biogeography of Microbial and Plant-Derived Redox-Active Small Molecules and Combinatorial Effects on Microbial Growth Soil is the source for many antibiotics of clinical relevance and the training ground for the organisms that both produce and resist them. Engineering and managing microbial communities in human health necessitates an understanding of the soil environment where these organisms often originate. My lab is working to determine the combinations of plant and microbial small molecules that microbial communities experience in soils and how this affects their growth and survival across environments. It is now evident that antibiotics produced by soil bacteria and their co-occurring microbial neighbors can shape microbial growth as well as community composition, function, and resilience. However, we do not yet understand the biogeography of these small molecules, i.e. what combinations of natural antibiotics a microbe might typically encounter and how this varies from one microbial community to another. In addition, much work has focused on studying microbe-microbe interactions facilitated by small molecules. Yet, in soils, microbial consortia often live on plant hosts that release natural products capable of sculpting rather than eliminating the microbial community. How does this work? While some plant secondary metabolites do inhibit canonical antibiotic targets (ribosomes, DNA replication, cell wall biosynthesis), many plants also produce low-dose antimicrobial compounds that use reduction-oxidation (redox) reactions to control microbial communities at their roots. We suggest i) that root-associated microbial communities offer an experimentally tractable system to test hypotheses about secondary metabolite biogeography ii) that the production of plant and microbial redox-active metabolites is an expedient entry point for these studies and iii) that plant-produced redox-active metabolites have been understudied as features of the soil environment that may offer insights into how microbes have learned to survive in diverse contexts, including infections. This proposal outlines my lab’s long-term plan to study the patterning and combinatorial physiological effects of microbial and plant redox-active compounds on the growth and development of microbial communities. We will take a two-pronged approach. The first part of our efforts will be aimed at using newly developed screens to identify redox-active metabolites made natively in soils and determine patterns of (co)production. A second area of our work will explore the molecular mechanisms by which known redox-active plant metabolites (coumarins) affect microbes. Iteration between the two approaches will allow us to conduct environmentally informed experiments that test the effects of co-occurring small molecules on microbial communities. Findings from this work will inform the development of therapeutics to irradicate microbial infections and provide tools for the discovery and activation of novel antimicrobial compounds produced by soil organisms.

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.