Harvard University

NSF Awards · FY 2026 · 2026-07

This project develops new mathematical and computational tools for integrating high-dimensional datasets with partially shared structures, a challenge that arises across various fields, including molecular biology, precision medicine, business analytics, and economics. When data are collected from multiple sources—such as different individuals, experimental conditions, or technologies—joint analysis can reveal complex patterns that would be missed if each dataset were analyzed in isolation. However, existing methods often struggle to distinguish meaningful signals from noise, particularly when the data are high-dimensional and heterogeneous. This project addresses these limitations by creating a principled framework to uncover and align shared low-dimensional structures across datasets, ultimately enabling more accurate, interpretable, and biologically relevant insights. The project will also contribute to the broader community by developing open-source software tools and offering interdisciplinary training opportunities for students at various levels. This project will build new theoretical foundations and methods at the intersection of random matrix theory, manifold learning, and high-dimensional statistics, and it is closely related to artificial intelligence. Key contributions include new results in random matrix theory for composite and kernel matrices formed from multiple datasets, a Procrustes-based framework for aligning low-dimensional structures in high-dimensional noise, and a kernel-spectral approach for joint nonlinear embedding. These tools will enable more accurate and robust data integration, particularly in single-cell biology, where understanding conserved cellular patterns across different conditions or species is a central challenge. Broader applications include the analysis of electronic health records and other large-scale biomedical or economic datasets. 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

Reliable methods for learning from complex data, central to the field of Artificial Intelligence (AI), are essential for scientific discovery and for decisions that affect national health, prosperity, and welfare. Modern studies often collect measurements on many interacting variables, but standard statistical methods may require simplifying assumptions that are difficult to verify and may miss important relationships in the data. This project will develop a new way to understand such relationships by studying data at the level of the binary digits used by computers to represent information. Working at this basic level will help researchers build tools that are more reliable, interpretable, and broadly applicable across many types of data. The results will support advances in areas such as neuroscience, genetics, engineering, economics, and other fields where scientists need to distinguish meaningful patterns from noise. The project will advance the progress of science by improving the foundations of data analysis, strengthening reproducibility in scientific research, and providing research training for graduate, undergraduate, and high school students. This project will focus on developing the Binary Expansion Group Intersection Network (BEGIN) as a framework for statistical learning from data bits. The framework will construct graphical models directly from binary representations of data and will use ideas from binary expansion and abelian group theory to study conditional dependence, which describes how variables are related after accounting for other variables. The project will establish theory and methodology for testing and modeling conditional independence using data bits. This foundation will then be used to develop bit-based methods for causal inference and interpretable machine 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 2026 · 2026-06

Nontechnical description: Manipulating electromagnetic waves in space and time is a foundational capability of modern science and engineering. At visible and near-infrared wavelengths, electromagnetic fields interact strongly with atoms and molecules, enabling scalable control in quantum hardware as well as advanced techniques in molecular and neural imaging. This CAREER proposal aims to develop new integrated-photonic hardware—specifically, large-scale, high-speed spatial light modulators—that will enable next generation quantum control hardware and advanced imaging platforms. Beyond quantum and imaging applications, the photonic subsystems developed in this effort will contribute to emerging photonic interconnects that support large-scale machine learning across geographically distributed data centers—a growing national priority as AI models and datasets increase in scale. Through partnerships with industry, the PI will accelerate translation of these photonics technologies into practical systems. Students in the project will receive interdisciplinary training at the intersection of photonics, quantum engineering, and computational design, and the team will engage in mentoring and outreach activities for K–12 students in Cambridge and Boston public schools. Technical description: This research program aims to redefine spatial light modulator architectures through the direct integration and co-design of a solid-state gain laser array, high-speed electro-optic modulators, a two-dimensional beam-emitter array, and flat metasurface lenses. The resulting integrated-photonics platform will generate and modulate large arrays of optical beams in free space, with each beam individually controlled by on-chip modulators and an injection-locked laser array. Extending the operational wavelength of this system from the telecom band into the visible—and potentially ultraviolet—regions will enable scalable optical interfaces for atom-based quantum hardware and high-resolution imaging modalities in neuroscience. In addition to quantum and imaging applications, the development of the underlying photonic subsystems (integrated lasers, modulators, and beam-emitter arrays) will directly advance next-generation photonic interconnects for large-scale machine learning, particularly in distributed, multi-data-center training environments. Together, these innovations establish a unified photonic architecture that supports high-speed, large-scale optical control across quantum science, AI hardware, and biomedical imaging 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 · 2026-06

With support from the Chemical Structure and Dynamics (CSD) program in the Chemistry Section, Professor Kang-Kuen Ni of Harvard University is investigating coherent control in reactions of extremely cold molecules where the quantum state of the reactants can be carefully prepared and reaction outcomes accurately measured. So far, researchers have shown that certain properties, like nuclear spin and quantum coherence, are preserved during reactions that start from very specific quantum conditions. However, it’s still unknown whether this holds true when the molecules begin in more complex or arbitrary quantum states. To find out, Professor Ni and her team will design special experiments—"reaction interferometer"—to test what happens when molecules start from a variety of quantum states, including those with deliberately added phase controls. This will help them understand when quantum coherence is maintained or lost, and what factors control the behavior of nuclear spins during the reaction. The studies could also include the possibilities of engineering chemical entangled pairs for quantum communication or as a new mechanism of chemical reactions for biological signaling and regulation. Students and postdoctoral researchers working on this project will gain comprehensive training in quantum science knowledge and techniques. Professor Ni’s research group also gives undergraduate students a chance to be involved in cutting-edge scientific work. Coherent control of reactions at the quantum state level has long been a goal in chemistry. Recent advances in ultracold molecule techniques now enable quantum state-selective preparation of reactants and detection of products in unconventional species such as bi-alkalis. Remarkably, nuclear spin coherence can survive the atom-exchange reaction 2 KRb → K₂ + Rb₂, making this system a promising platform to demonstrate coherent control and probe the role of quantum phases in reactions. Professor Ni and her students will build a reaction interferometer that splits a cloud of reactant molecules, imprints a relative phase via microwave driving, and then recombines the clouds to measure collision outcomes. By preparing nuclear spin superposition states with varying phases, the experiments will reveal how coherence and quantum phases influence reactions. This work offers a new window into quantum coherence, nuclear spin dynamics, and the potential to control reaction outcomes through phase manipulation. The project bridges atomic physics and physical chemistry, encouraging cross-disciplinary collaboration. It will also train early-career researchers, preparing them to contribute to the workforce and advance the frontiers of quantum 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 · 2026-06

Satellite images of the tropics commonly show large regions of rain and deep convective clouds moving along the equator, crossing the Pacific over the course of a week or two. The size and propagation speed of the cloudy regions are linked to planetary-scale equatorial waves but the theory does not involve moisture, clouds, or precipitation. As a result, important questions remain about how large-scale wind patterns and gentle rising and subsiding wave motions produce deep convective clouds and rainfall. The extent to which clouds influence these waves remains uncertain. These systems, known as convectively coupled waves (CCWs), highlight the importance of interactions between large-scale atmospheric dynamics and convection. A related question is how convection interacts with the larger and more slowly evolving Madden–Julian Oscillation (MJO). Improving understanding of these processes will enhance our ability to anticipate weather patterns associated with these systems, including heavy rainfall, hurricanes, and other high-impact events. This project also advances the development of artificial intelligence and machine learning approaches to improve analysis and prediction of complex atmospheric processes. Work supported here takes a novel approach to convective coupling research by developing a three-part research methodology combining high-resolution simulations with AI/ML techniques. In the first step wave-convection interactions are simulated using two high-resolution atmospheric models: SAM, the System for Atmospheric Modeling (see AGS-2218827) and SPCAM, the superparameterized Community Atmosphere Model (see AGS-0425247). In the second step the model simulations are used to train a nonlinear neural network model which builds on an earlier linear model developed by the Prinicipal Investigator of this award. The third step uses model order reduction to distill the behavior of the neural network into a simpler model in which the variables are physically meaningful and the model can be used to develop and test hypotheses for wave-convection coupling. One question to be addressed is the role of convective "memory", meaning the extent to which the the slow propagation of CCWs and the MJO depends on the time evolution of organized convection over the course of a few hours. 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 · 2026-06

This award supports research in arithmetic geometry, a field that studies number systems using geometric and algebraic methods. These tools help understand the structure of equations and their solutions, with applications in areas such as cryptography, secure communication, and digital verification. The project investigates questions involving Galois representations and algebraic cycles -- central objects in the modern mathematical understanding of arithmetic -- and also supports the training of graduate students in advanced research settings. The research will examine problems related to Galois representations and algebraic cycles. Specific directions include structural analysis of mod p reductions of crystalline representations, the study of algebraic cycles through prismatic cohomology, and exploration of de Rham analogues of the Mumford–Tate conjecture. The project also addresses questions in the arithmetic of Shimura varieties and the independence of l in Weil–Deligne representations. These efforts are grounded in current methods in p-adic Hodge theory and align with current directions in number theory and arithmetic geometry. 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 · 2026-06

Insects act as harmful pests that spread disease, but they are also vital for growing crops. Because of this, insects deeply affect human health and wealth. Many insects find their mates using airborne chemical signals called pheromones. These signals are often unique to a single species, and insects need them to reproduce. Blocking these chemical signals is a promising way to manage insect populations. Rather than using broad bug sprays, disrupting their mating is precise and safe for the environment. Scientists do not fully understand how insects sense these mating chemicals. However, a better understanding of this process will help us build new tools to control pests on a large scale. This CAREER project will explore the details of chemical communication from atoms to behavior, focusing on insects that are harmful to farming and logging. The long-term goal is to design new chemicals that can boost, block, or confuse mating signals in target species. The project will also bring this research into the classroom by creating learning activities and online resources. These tools will improve science and artificial intelligence literacy and prepare students for jobs in biology and biotechnology. Overall, this work supports the nation by advancing chemistry, biotechnology and neuroscience, and it creates new ways to protect American farming and ecosystems. This project advances NSF’s priorities in Biotechnology, Advanced Manufacturing and Artificial Intelligence. The goal of this research is to elucidate the structural and cellular basis of chemical specificity in insect pheromone receptors, focusing on moths and fruit flies of agricultural relevance. Aim 1 will determine how lepidopteran (moth) pheromone receptors achieve high chemical specificity for pheromones exhibiting only subtle variations in functional group, unsaturation position, or stereochemistry. To accomplish this, cryogenic electron microscopy (cryo-EM) will be used to solve high-resolution structures of selected pheromone-bound receptor complexes, with structure-derived atomic hypotheses subsequently validated through targeted mutagenesis and comprehensive functional assays. Electrophysiological recordings, which provide temporal information, will be used to evaluate how divergent pheromone chemistries modulate receptor activation and inactivation kinetics. Concurrently, structure-guided chemical design will facilitate engineering of novel modulators targeting the receptor binding pocket, and their efficacy in disrupting pheromone tracking will be quantified using in vivo behavioral assays. Aim 2 will focus on Drosophilids, which include fruit flies of agricultural importance. This aim will interrogate how accessory proteins within pheromone sensory neurons orchestrate ligand delivery and turnover. Electrophysiology, live-cell fluorescence imaging assays, and biochemical measurements will be used to evaluate the role of critical components of pheromone sensing neurons: the pheromone-binding protein LUSH and the membrane protein SNMP1. Biochemical and biophysical tools including native-tissue pull-downs, co-immunoprecipitation, and high-speed atomic force microscopy will probe transient protein-protein interactions between SNMP1 and pheromone receptor complexes in membranes. Together, these studies will yield a complete mechanistic model of insect pheromone sensing, establish validated molecular targets and generate chemical strategies for selective, behavior-based management of agricultural pests. 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 · 2026-06

This grant supports research to enable understanding and design of swarms of string-like soft robots, capable of moving around their environment independently, or of tangling together to operate as a collective. The project seeks to create programs for the individual robots to follow so that, when in their entangled state, they can collectively move in response to external stimuli and transport or manipulate objects. For example, a swarm of individual robots might separately search an area for a designated object. Upon locating the object, the robots could rapidly retrieve it by forming an entangled collective to pick up the object, encapsulate it in a ball, and roll to the desired destination. This ability to act separately over a wide area but to come together to gain additional functions when needed, could one day enable important new applications in environmental monitoring or security surveillance. This work is inspired by the remarkable behavior of California blackworms, which form entangled 'blobs' under certain environmental conditions, but can disentangle almost instantly when appropriate. Like the blackworm, the entangled robots will be designed to to adjust their collective mechanical properties, such as stiffness, through the responses of the individual components, potentially surpassing the known locomotion and manipulation capabilities of the biological organism. In addition to the contributions to robotics, this work will provide insights into prediction and control of material properties in other physically entangled systems, such as polymer networks. The work seeks to develop pneumatically actuated high-aspect-ratio elastomeric robots with integrated sensing, and implement methods by which entangled groups of such robots demonstrate locomotion with rolling and peristaltic gaits, transportation and manipulation of external objects, and navigation in constrained environments such as mazes. Robots look to possess sensory capabilities of light sensing, proprioception, and tactile/proximity sensing. Initial control policies for the entangled groups will be based on known principles underlying collective locomotion in the California blackworm. These policies seek to subsequently seed machine learning procedures to further refine the collective behaviors. The results will be used to characterize principles underlying collective motion primitives and the ways they can be composed. The hardware aspect of the work looks to advance the state of the art in fabrication of soft robots, pushing the limits of geometries that can be achieved, developing new forms of integrated sensing, and providing new strategies for achieving desired behaviors with inherently imprecise robots. The algorithmic aspect of the work looks to identify new methods for engineering emergent systems: designing and verifying dynamic systems of interacting agents, which reliably achieve collective goals while adapting to each other and to uncertain and changing environments. 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 · 2026-06

This NSF CAREER project aims to make autonomous systems safer and more reliable by developing optimization tools that are both fast and theoretically sound. Many high-stakes autonomy tasks—building 3D maps from images, planning robot motions, or choosing actions from raw sensor data—are formulated as nonconvex optimization problems that today are typically handled by heuristics that can fail unpredictably. This project will bring transformative change by enabling “certifiable” decision-making: algorithms that return high-performance solutions together with mathematical certificates of global (or near-global) optimality, so practitioners can verify when an answer is trustworthy. This will be achieved by creating a unified open-source toolbox for fast, certifiable perception, and control and validating it on real robotic platforms and public benchmarks. The intellectual merit of the project includes new theory and algorithms that bridge nonconvex autonomy problems with scalable convex relaxations, and methods that tightly integrate optimization with modern learning systems. The broader impacts of the project include open educational resources and software that democratize trustworthy autonomy, integration into undergraduate and graduate courses, and hands-on mentoring and outreach opportunities that prepare students—from high school to graduate levels—for careers at the intersection of optimization, robotics, and AI. Technically, this project will advance the moment and sums-of-squares (moment–SOS) relaxation framework, which converts broad classes of nonconvex polynomial optimization problems into semidefinite programs (SDPs) whose solutions provide optimality certificates, but is currently limited by scalability and weak integration with data-driven learning. Three thrusts address the full autonomy stack. Thrust 1 develops a certifiable structure-from-motion pipeline by using vision foundation models (e.g., monocular depth predictors) to “lift” image measurements into a polynomial formulation, then solving a tight first-order relaxation with fast low-rank SDP methods and differentiating through the solver to fine-tune perception models. Thrust 2 creates real-time certifiable trajectory optimization by exploiting problem sparsity to shrink higher-order relaxations and by designing parallel, GPU-friendly first-order SDP solvers with learned warm starts and step sizes. Thrust 3 extends certifiable planning to perception-based control with learned world models by using polynomial approximation and kernel-based SOS techniques, and by embedding certifiable planners into inverse reinforcement learning to improve world-model training. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Across scientific domains, a fundamental challenge is to understand how systems change across contexts. Examples include biological processes across diseases or word meanings across genres of text. Tensors are a natural framework to study such multi-context systems, generalizing the role of matrices and linear algebra in classical data analysis methods such as principal component analysis. The investigator will design new tensor decomposition algorithms and use them to analyze multi-context data. Special attention will be paid to the theoretical guarantees of the algorithms, to ensure their reliability. The algorithms will be used to improve artificial intelligence models to study variability of gene programs across diseases and of word usage across literary genres. The project will implement new tensor decomposition algorithms and prove results that justify their design and explain their performance. A longstanding challenge in the theory and practice of tensor decomposition is that the higher-order power method cannot find a low rank decomposition: for tensors (unlike matrices) computing a best rank one approximation and deflating (subtracting it off) usually fails: the deflation step leaves the rank unchanged or even increases the rank. The proposed algorithms will address this problem by transforming a tensor to a special basis before computing its decomposition. Numerical analysis and real algebraic geometry will be used to establish the theoretical guarantees of the procedures. The methods will build a tensor decomposition one term at a time. This is more scalable, reliable, and interpretable than computing all terms at once, and yields decompositions that are compatible across ranks. Tensor decomposition enables the simultaneous comparison of data across contexts, without requiring pairwise comparison of contexts or that samples are measured in multiple contexts. The investigator will apply the algorithms to analyze multi-context gene expression data and contextualized word embeddings. 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 · 2026-06

Many important questions in number theory concern the statistical behavior of numbers. For example, a classical such question asks what the chance is that a random integer has no repeated prime factors. The PI will investigate several important open questions concerning the statistical behavior of the integers and related objects of interest. The PI has recently made progress toward these questions by bringing in new ideas from several disparate areas of mathematics. The PI plans to continue investigating these connections and deepening our understanding of them. Simultaneously, the PI proposes to support and mentor mathematicians at different levels of their educational career. The project is to work on the main conjectures of arithmetic statistics over function fields. These conjectures include the Cohen-Lenstra conjectures on class groups of quadratic fields, Malle's conjecture on counting global field extensions with specified Galois group, and the Poonen-Rains conjectures on ranks and Selmer groups of elliptic curves. The main idea is to develop tools in topology to compute relevant stable and unstable homology groups. The project will combine ideas in number theory and algebraic geometry with ideas in topology and higher algebra to make progress toward these conjectures. The PI also plans to pursue projects in other areas, such as the Putman-Wieland conjecture. 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 · 2026-06

The United States is entering a "quantum revolution," but quantum ideas and applications remain largely absent from high-school physics classrooms. High-school physics teachers are a key leverage point for broadening early exposure to quantum information science and quantum sensing, yet most teachers have had limited preparation and often lack local colleagues and subject-specific professional development. This project will test an unproven but potentially transformative approach: repurposing an established, teacher-driven national network—the Physics of Living Systems Teacher Network—as scalable infrastructure for preparing high-school physics teachers to incorporate accessible, age-appropriate quantum topics into their teaching. The project will (1) develop and deliver a sequence of online workshops with associated classroom-ready resources; (2) pilot small peer-based "Quantum Curriculum Circles" (QCCs) that help teachers adapt quantum content to local curricular constraints and move from interest to classroom use; and (3) conduct an embedded, mixed-method study (brief surveys, participation data, and interviews) to identify which supports most effectively increase teacher readiness, instructional confidence, and self-reported classroom incorporation of quantum content. By enabling more high-school physics teachers to introduce quantum ideas and applications, the project strengthens the early educational pipeline into the future quantum workforce and broadens students' opportunity to see quantum science as part of modern physics and as a possible pathway for themselves. Because the model is online, lightweight, and peer-supported, it is designed to reach teachers who are geographically dispersed or professionally isolated, including those in rural or under-resourced settings. The project will produce openly shareable workshop materials, adaptable classroom resources, and a transferable QCC design framework that can be adopted by other teacher networks, professional societies, and school systems to support teacher learning in quantum and other emerging STEM domains. The project advances knowledge about how emerging scientific domains can enter secondary curricula through network-based professional learning. It will generate design-oriented evidence about what quantum entry points are feasible and valuable for high-school teachers, what barriers constrain adoption, and what combinations of supports (workshops, resources, and QCC participation) are most strongly associated with increased readiness and uptake. Methodologically, the project will contribute exploratory measures and analytic strategies for studying teacher learning and curricular incorporation in a naturalistic, teacher-driven system where participation is voluntary and heterogeneous—producing feasibility evidence and design principles that can motivate and shape a larger, more definitive future study. 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 · 2026-06

This award provides support the conference Quantum Field Theory and Topological Phases via Homotopy Theory and Operator Algebras, taking place 30 June through 11 July 2025 at the Center of Mathematical Sciences and Applications at Harvard University. The conference will bring together researchers from several communities within mathematics and physics who all explore how to model complicated physical systems and how key characterizing properties emerge (which identify the phase of the system, broadly speaking). These tools are crucial in understanding and developing new materials, particularly those with surprising "topological" phases, and in developing quantum computers, but the focus of the conference is to deepen mutual understanding and to share technical tools between communities who speak different technical languages and thus approach these topics in distinct, yet complementary, ways. To foster effective communication, the first week will be devoted to expository lectures by leading figures in this subject, while the second week will focus on talks on current research results. Graduate students and postdoctoral scholars will attend, fostering the next generation of work in this domain. We note that this conference will have a "twin" at the Max Planck Institute for Mathematics in Germany, so that researchers in Europe are part of this dialogue. In more technical terms, Quantum Field Theory (QFT) and Quantum Statistical Mechanics are central to high energy physics and condensed matter physics; they also raise deep questions in mathematics. The application of operator algebras to these areas of physics is well-known. Recent developments indicate that to understand some aspects of QFT properly a further ingredient is needed: homotopy theory and infinity-categories. One such development is the recognition that symmetry in a QFT is better described by a homotopy type rather than a group (so-called generalized symmetries). Another one is the work of Lurie and others on extended Topological Field Theory (TFT) and the Baez-Dolan cobordism hypothesis. Finally, there is a conjecture of Kitaev that invertible phases of matter are classified by homotopy groups of an Omega-spectrum. This workshop will bring together researchers and students approaching this physics using different mathematical techniques: operator algebras, homotopy theory, higher category theory, etc., and will catalyze new interactions between different communities. The workshop will also highlight recent developments and new progress on two outstanding problems: the Kitaev conjecture as well as the long-standing goal of finding a proper mathematical formulation for QFT. The website for the conference is https://cmsa.fas.harvard.edu/event/mpqft25/ 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

PROJECT SUMMARY/ABSTRACT The overall goal of my research program is to understand evolution in microbial populations, using a combination of mathematical models and laboratory evolution experiments in budding yeast. Here, we aim to pursue this overall goal by characterizing the statistical structure of complex and high-dimensional genotype- phenotype landscapes, and by analyzing how microbial and viral populations evolve across these landscapes. In the short term, evolution depends primarily on the most immediate aspect of the landscape: the distribution of fitness effects of individual mutations. However, on longer timescales interactions between the effects of multiple mutations (epistasis) can be crucial. Similarly, mutations often have different fitness effects in different environments (pleiotropy). This is essential to evolution in fluctuating environments. Recent work shows that epistasis and pleiotropy are strong and common among specific sets of mutations in many microbial systems. However, these studies of specific limited sets of mutations cannot fully explain how epistasis and pleiotropy constrain the rate, repeatability, or dynamics of adaptation. And even given a complete set of epistatic and pleiotropic interactions, we are still often unable to predict how evolution will act. This severely limits our ability to understand the evolution of complex phenotypes, such as compensated antibiotic resistance, multiple mutations required for immune escape, or multiple gene knockouts enabling cancer evolution. The first main research direction in this proposal will examine the role of epistasis and pleiotropy in the evolution of microbial populations. Rather than characterizing specific examples, we propose to survey the overall statistics of epistasis and pleiotropy that are relevant for constraining microbial adaptation. Given some structure of the genotype-phenotype landscape, we next aim to understand evolutionary dynamics and population genetics in microbial populations. Extensive previous work has addressed this question, but these methods are primarily constrained to the analysis of one or a few loci at a time and break down when selection acts simultaneously at many linked loci across the genome. The basic problem is that when natural selection is widespread, there is too much happening at once: mutations arise constantly in a variety of combinations linked together in physical chromosomes, and selection can only act on these combinations as a whole. Thus the dynamics of mutations at different loci are intertwined, creating complex correlations between sites. In recent years, it has become increasingly clear that these effects, known as linked selection, are pervasive in microbial and viral evolution. Yet despite their potentially broad importance, we have limited understanding of how we expect linked selection to affect evolutionary dynamics or observable patterns of genetic variation. The second main research direction in this proposal will use a combination of mathematical models and laboratory evolution experiments to analyze evolutionary dynamics and population genetics in these large and rapidly evolving populations, where linked selection is widespread.

NIH Research Projects · FY 2026 · 2026-05

Project Summary When animals are reared in different environments, the brain adapts their sensorimotor behaviors to local condi- tions. Understanding how the brain creates adaptive behavior requires dissecting how specific mechanisms for synaptic and cellular plasticity spread to system-wide modulation of sensorimotor circuits and behavior. We will develop the mating behavior of the C. elegans male as a comprehensively accessible paradigm for experience- dependent plasticity throughout a sensorimotor circuit. The C. elegans male uses a separate circuit in its tail – ~100 sensory neurons, interneurons, and motor neurons – to drive mating behavior. How this circuit executes information-processing and decision-making depends on the physical environment. When reared on flat surfaces, the male adapts his mating ritual to two-dimensions. During ‘parallel mating’, he limits his sensorimotor decision-making to sliding movements alongside his partner’s body from contact to copulation. When reared in liquids, the swimming male uses ‘spiral mating’, where sensorimotor decision-making includes three-dimensional movements like wrapping around his partner and using exploratory tail movements. Whether the sensorimotor circuit encodes parallel or spiral mating depends on life experience. Males reared in liquid are proficient at spiral mating. Males reared on plates are proficient at parallel mating. We seek synaptic, cellular, and wiring mechanisms that lead to different sensorimotor outcomes when an animal is reared in different environments. We will map the wiring diagram of the mating circuit as it adapt to 2D and 3D mating behaviors. We will record circuit-wide neural activity during parallel and spiral mating. We will use spatial transcriptomics and genetic manipulations to dissect how synaptic and cellular mechanisms lead to circuit and behavioral remodeling. We will build computational models that use neurophysiological, neuroanatomical, and neurogenetic datasets to interrelate synaptic, circuit, and behavioral mechanisms.

NSF Awards · FY 2026 · 2026-05

This grant provides funding for a workshop entitled The Future of Pharma Supply Chain: Addressing Real-World Challenges, being held in Boston, Massachusetts, Boston, 27-29 May 2026. The goal of the workshop is the progress of science, advance national health and prosperity, and strengthen societal well-being by convening a three-day interdisciplinary workshop addressing the growing scientific and operational complexities of modern pharmaceutical supply chains. These supply chains are critical to national health and economic prosperity, but are increasingly strained by the need to support a diverse portfolio of products, from traditional small-molecule drugs to biologics and personalized therapies. Ensuring that these systems remain efficient, resilient, and responsive is essential for timely access to life-saving medications and sustaining the nation’s capacity for scientific and economic growth. The workshop will convene leading researchers in Operations Research (OR), Artificial Intelligence (AI), Chemical and Biological Engineering (CME), and Biomedical Engineering (BME), together with industry and regulatory stakeholders, to define the fundamental scientific challenges shaping pharmaceutical supply chains. Through structured dialogue and collaborative problem-solving, the workshop will generate an open-access research roadmap that connects scientific inquiry with real-world implementation. In doing so, it will contribute to the national interest by advancing scientific understanding, improving patient access to therapies, and supporting economic growth through stronger manufacturing and logistics capabilities. The technical goal of the workshop is to develop a forward-looking research roadmap that systematically links real-world pharmaceutical challenges with emerging analytical, computational, and engineering methods. The workshop is structured around five stages of the pharmaceutical value chain: discovery and preclinical development, clinical trials, manufacturing, supply chain logistics and distribution, and market access. Each session follows a challenge-driven format in which speakers present concrete operational problems, followed by complementary perspectives from OR/AI/CBE/BME experts and practitioners. Structured breakout discussions will identify methodological gaps, data needs, and opportunities for interdisciplinary collaboration. Key discussion themes include adaptive trial design, data-driven manufacturing, personalized medicine, rare diseases, cold-chain logistics, and innovative pricing and incentive strategies informed by real-world evidence. The primary outcome of the workshop will be a research roadmap, which will be disseminated in an open-access format to support future interdisciplinary research. Post-workshop activities will be organized to ensure continued collaboration and community building, fostering an open dialogue between research and practice. 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

The activity of T cells is key to anti-tumor immunity, but T cells frequently exhibit poor penetration into solid tumors and a dysfunctional phenotype. Preclinical studies typically utilize mouse models to study these processes, but even humanized mouse models do not capture key aspects of human biology relevant to immunotherapies. Here we will develop a 3D model of human cancer biology to enable studies of T cell trafficking within tumors, their engagement with dendritic cells (DCs) in activation niches, and their killing of cancerous cells. We will use µPOROUS embedded printing to fabricate these models. We will first create 3D models of mouse cancer, due to the ready availability of isogenic cancer, DCs and T cells, and reagents to analyze murine DCs and T cells. We will then create the human models, including tumor, vascular and immune cells derived from the same patient. Together, the murine and human models will be used to explore two key questions that are difficult to study in vivo: (1) the impact of changing matrix viscoelasticity on DC-T cell interaction, and (2) the role of the activation niche in T effector function. The following specific aims will be pursued: (Aim 1) Utilize µPOROUS embedded printing to develop in vitro murine and human cancer models that include vascular conduits for T cell entry, a stromal compartment that enables T cell migration similar to that found in vivo, and a central tumor compartment consisting of either murine isogenic (B16F10) or human HLA-matched melanoma. (Aim 2) Study how the viscoelasticity of the matrix impacts the ability of type 1 conventional dendritic cells (cDC1s) to activate, in an antigen-specific manner, T cells migrating through the model stroma. (Aim 3) Determine the impact of activation niches on T cell phenotype and cytotoxic function, and validate the ability of the system to model the role of checkpoint blockade therapy. At the completion of this project we will have developed novel, 3D models of both mouse and human tumors that will allow replication of key aspects of cancer immunotherapy. Beyond addressing two key questions in T cell biology and performing initial validation, these models will enable more realistic investigation of human tumor - DC - T cell interactions in a 3D setting, including the preclinical evaluation of novel immunotherapy strategies.

NIH Research Projects · FY 2026 · 2026-05

Project Summary Retrieving memories of emotional events can result in (re)experiencing subjective feelings. Importantly, the feelings elicited by memories can influence current emotional states. For example, recalling positive events can both reduce current negative affect and diminish acute physiological stress responses. Similarly, recalling negative events can lead to current negative feelings and moods. The feelings of memories have also been linked to subjective clinical symptoms. The frequency of reported negative intrusive memories is related to both the severity of clinical symptoms in a treatment-seeking population and the likelihood of reporting clinical symptoms in a typical undergraduate population. Because of the impact of the feelings elicited by memories on current emotional health and well-being, understanding how the feelings of memories might change over time has to potential to suggest novel treatment innovations. The proposed research investigates techniques intended to alter the feelings associated with memories. Specifically, we take advantage of reactivation induced memory change (RIMC) to determine factors that may reduce or enhance the negative feelings elicited by memories. To explore how RIMC can modify the feelings of memories, the proposed studies examine three kinds of memories and two techniques to alter subjective feelings. There are three specific aims: Aim 1 examines whether RIMC interventions can be used to diminish the negative feelings elicited by memories for negative events. Aim 2 examines how RIMC mechanisms may enhance the negative feelings elicited by memories for negative events. Aim 3 examines if RIMC techniques can infuse negative feelings into previously neutral memories. In addition to examining the feelings elicited by memories, we will also examine the impact of these interventions on changes in physiological stress reactions and the pattern of blood oxygen level dependent (BOLD) signals when retrieving these memories. It is hypothesized interventions following the reactivation of a previous acquired memory will change the later expression of subjective feelings elicited by that memory more than interventions without reactivation. It is further hypothesized that the reactivation of a previously acquired negative memory without any intervention will result in strengthening the memory and enhancing negative feelings. We expect these changes will also be reflected in physiological stress reactions and BOLD response patterns during retrieval.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Polypeptides, due to their high affinity and selectivity for diverse biological targets, hold significant promise as therapeutic agents for treating complex diseases such as cancer, metabolic disorders, and infectious diseases. However, poor pharmacokinetics (PK), including rapid renal clearance and protease degradation, often hinders their clinical applications. This leads to short half-lives and limited bioavailability, necessitating frequent dosing and increasing the burden on the patient and the risk of side effects. Existing strategies to improve PK, such as recombinant fusions to the N- or C-terminus of human serum albumin (HSA) or chemical modifications to constrain conformation like cyclization, frequently compromise the biological activity and manufacturability of these therapeutic agents. This project aims to avoid activity losses due to the steric hindrance from the albumin fusion partner while constraining conformation naturally by developing a novel platform that nonterminally fuses polypeptides to albumin. Nonterminal fusions are relatively unexplored due to the challenges associated with maintaining the native folding and function of the fusion partner. However, recent advancements in structural prediction tools now enable us to accurately predict and mitigate these disruptions. We hypothesize that nonterminally fusing polypeptide cargoes to albumin at externally presented disulfide-constrained loop regions will significantly improve their pharmacokinetic properties without compromising their activity. We will evaluate this strategy for the ability to enhance the stability, half-life, and target localization of therapeutic polypeptides through three specific aims: Aim 1 will establish an expression system and leverage a model peptide to screen potential insertion sites for structural perturbations and availability. Aim 2 will use AlphaFold to develop a computational pipeline to predict structural changes due to nonterminal fusions, establishing rules for generalizing the strategy to any payload. Aim 3 will evaluate the PK properties and therapeutic benefits of nonterminal fusions of Neo-2/15, an engineered IL-2/15 receptor agonist, in cancer models. Successful completion of this project will yield a robust platform for nonterminally fusing polypeptides to albumin, offering a modular and generalizable strategy to improve drug activity, half-life, and target tropism. This approach has the potential to significantly advance the fields of protein engineering and drug delivery. By establishing a precedent and pipeline for nonterminal fusions across any carrier proteins, more effective and durable treatments can be developed in many therapeutic areas and indications, ultimately improving patient outcomes and enhancing public health.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Membrane proteins represent over half of drug targets due to their accessibility and diverse roles in mediating interactions with and responses to the environment. My research program focuses on uncovering the molecular mechanisms and selectivity determinants of membrane proteins that mediate critical cellular processes such as transport, signaling, and specialized metabolic pathway regulation. To achieve this, we combine experimental techniques like X-ray crystallography and single-particle cryogenic electron microscopy with functional assays, both in vitro and in cells. We also use computational approaches, such as all-atom molecular dynamics simulations to investigate protein dynamics and bioinformatic analyses to investigate the evolution of protein families. Recently, we have expanded from structure-guided protein engineering to large-scale mutational screens, providing comprehensive datasets that enhance our understanding of structure-function relationships. Over the next five years our research will focus on the following three bacterial membrane protein systems—all of which contain common transporter folds that have evolved to perform diverse specialized functions: Nramp Transporters: Nramps, part of the LeuT-fold transporter superfamily, are metal ion transporters found across all kingdoms of life. In the past 10 years, we developed a detailed understanding of Nramp function using a bacterial homolog. Leveraging this rigorous foundation, we will use large-scale mutational screens to identify determinants of metal ion selectivity. These findings will clarify the evolution of substrate selectivity among Nramps and provide insight into strategies organisms use to avoid toxic metal accumulation. Type II Prodrug-Activating Peptidases: These bacterial peptidases, which include a type IV ABC transporter fold, selectively export and activate specialized metabolite toxins. Our recent elucidation of the structure and selectivity determinants of the type I colibactin-activating peptidase ClbP and expertise on ABC transporters prime our research on how the export and enzymatic activities of ZmaM are coordinated to activate zwittermicin. These insights could inform synthetic biology applications, including engineered microbial systems. TM-LuxR Regulators: Combining a Major Facilitator Superfamily (MFS) transmembrane domain with a LuxR- like DNA-binding domain, these transcriptional regulators are pivotal for extracellular sensing and transcriptional responses in gut microbiota. We aim to elucidate the structural basis for ligand specificity and regulatory mechanisms, shedding light on their roles in bacterial adaptation and host-microbe interactions. Biomedical Relevance: This research will provide foundational knowledge on important membrane protein families and how their members gain specializations. The insights gained on Nramp transporters could aid in mitigating metal toxicity and improving micronutrient homeostasis. Understanding prodrug-activating peptidases and TM-LuxR regulators will offer pathways for manipulating bacterial metabolism with potential implications for microbiome health and disease management.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract Parasites that hijack the behavior of their animal hosts are incredible neuroscientists. Our proposed research seeks to understand the mechanisms by which the fungus Entomophthora muscae manipulates behavior in its host, the fruit fly Drosophila melanogaster. Broadly, we aim to decode the fungal triggers and fly neural targets involved in behavior manipulation, enhancing our understanding of neural circuitry and its vulnerabilities. Specifically, we seek to identify and characterize the fungal genes responsible for inducing E. muscae summiting behavior (wherein infected flies are driven to climb), understand the molecular cues that trigger flies to summit, determine the necessity of central nervous system invasion by the fungus for driving summiting, and pinpoint which neurons in the fly are directly influenced during this manipulation and how they are impacted. Our innovative approach integrates techniques across neurobiology, genetics, and microbiology, allowing us to uncover "natural" solutions to neuronal circuit manipulation. Utilizing the extensive Drosophila genetic toolkit and modern molecular approaches, we will employ real-time behavioral classification, single-cell mRNA sequencing, and metabolomics to uncover the molecular, genetic, and neural circuit underpinnings of fungal-induced behaviors. Additionally, our work seeks to establish transgenic access to E. muscae and push the field of neuroparasitology beyond correlative studies and into the realm of causal mechanisms between parasite genes and host behavior. Our work offers a fresh perspective on host-parasite interactions, revealing how E. muscae achieves its behavioral influence and providing a unique lens through which to view neural circuit modulation. The outcomes of this research could uncover novel insights into neural circuit functionality and identify new fungal natural products with potential therapeutic applications. Our long-term vision for this work is to set the foundation for broader comparative studies that will enrich our understanding of the evolution of “zombie” parasites and inform how many ways a brain can make a behavior.

NIH Research Projects · FY 2026 · 2026-03

ABSTRACT Cell division is an essential process for all organisms; thus, understanding the proteins and mechanisms underlying the division machinery is essential for understanding how to design antibiotics to target this process. Exerting the force needed to constrict pressurized bacteria in half is a primary function of the division machinery. Still, despite decades of study, the underlying physical mechanisms exerting this force remain unknown. Dividing cells is an energetically costly process, especially for bacteria, given their high internal turgor. The first - most energetically costly - step of cell division is the initial invagination of the membrane, deforming it from a flat surface into an inward bend, working against the membrane tension. How this initial deformation occurs in the cell remains unclear. In vitro studies have shown that membrane-attached FtsZ filaments can deform liposomes from the inside. This demonstrates that FtsZ can exert membrane-deforming force, but these deformations occur even when FtsZ cannot hydrolyze GTP. Rather, the liposome membranes only deform inward when the FtsZ filaments become laterally associated and condense together. These deformations share a commonality with every membrane-deforming system studied in eukaryotes: the local crowding of proteins. Protein crowding is known to deform membranes due to different effects, including: 1) the high concentration of amphipathic helices bends adjacent lipid headgroups apart, and 2) the crowding of proteins (even GFP) on membranes results in entropic repulsion between proteins, causing the membrane to bend towards the proteins. While it has been demonstrated that FtsZ filament crowding deforms membranes in vitro, we wish to 1) examine if FtsA/FtsZ filament condensation in vivo works to overcome the membrane tension to initiate division and 2) if and how certain properties within these filaments modulate the ability of filaments to deform membranes. To examine FtsZ condensation in vivo, we utilize our recent finding that when B subtilis lacks proteins that bundle FtsZ filaments, the filaments never condense into a ring, and cell division never initiates. To test if filament crowding works against membrane tension to bend membranes, we will first develop ways to both measure and modulate membrane tension in B. subtilis. Next, we will examine if the amount of FtsZ bundling protein needed to initiate division scales with the cell’s membrane tension, as expected if condensation works to deform membranes. We will then examine this relationship in vitro, seeing if FtsA/FtsZ filaments can deform liposomes of defined tensions with different amounts of FtsZ binding proteins. Next, we will examine how 3 features of FtsA/FtsZ filaments affect their ability to deform the A) cell membrane and B) liposomes under different tensions: 1) FtsA’s amphipathic helix, 2) the disordered linker within FtsZ, and 3) the total amount of FtsA/FtsZ polymer in the cell. Overall, this proposal will A) test a novel mechanism for how the bacterial division machinery could bend the membrane to initiate cell division and B) examine what features of FtsA/FtsZ filaments contribute to deforming the membranes.

NIH Research Projects · FY 2026 · 2026-03

Project Summary Azoles are ubiquitous amongst bioactive molecules, present in approximately one third of commercial pharmaceuticals. However, selective preparation of N-alkylated azoles is an outstanding synthetic challenge. As a result, stereochemistry and N-regioselectivity are often achieved through the separation of isomeric mixtures or introduced during de novo ring synthesis. This approach creates a significant synthetic barrier when evaluating analogs of bioactive molecules during drug discovery campaigns. Therefore, a selective method to install alkyl fragments on existing azoles is poised to advance the fields of chemical biology and medicinal chemistry. This proposal leverages anion-binding catalysis to induce enantio- and N-regioselectivity in azole alkylation. We will study how hydrogen-bond-donor catalysts engage azole nucleophiles through a network of noncovalent interactions to control selective nucleophile delivery. Aim 1. We are advancing an enantioconvergent SN1 reaction to form a-tertiary azoles. Aim 2. We are developing a novel method for N-regiocontrol to form the contra-thermodynamic azole product. The methods developed through this work will address long-standing challenges in azole chemistry, enabling the synthesis of medicinally relevant frameworks that are currently inaccessible. Additionally, the research plan will develop a deep understanding of the structural features required to engage azoles as competent nucleophiles in anion-abstraction catalysis, introducing a new class of nucleophiles to this approach. Beyond tackling a significant challenge in modern synthesis, I also designed my research plan to provide me with training in multiple important areas that I had limited exposure to in during my doctoral career. Through performing the proposed experiments, I will become proficient in asymmetric catalysis, supramolecular chemistry, carbohydrate chemistry, computational analysis, and mechanistic investigation. Furthermore, I will both enhance my existing synthetic repertoire through the synthesis of new catalyst architectures and become familiar with new experimental techniques such as measurement of enantioenrichment by HPLC. Therefore, the proposed research plan will prepare me with the skills I need to pursue an independent academic position.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Coronaviruses (CoV) are associated with severe diseases as demonstrated by the 2003 severe acute respiratory syndrome (SARS)-CoV1 epidemic and the SARS-CoV2 pandemic. One of the critical steps of infection involves viral mRNA mediated recoding of gene expression; a -1 frameshifting event that occurs during translation. It is this elegant mechanism that allows the ribosome to bypass a stop codon and synthesize viral enzymatic proteins. Furthermore, the frequency by which this event occurs is important for efficient viral infectivity and is regulated by domains in the translating mRNA (in the case of the SARS-CoV, this domain is a pseudoknot). Although structural studies of frameshifting have received considerable aOention and various structures have been proposed and solved, information on exactly which structure causes the frameshifting is lacking. Our preliminary studies indicate that CoV gene expression is regulated by a dynamic, proton-driven equilibrium between an active, and two inactive pseudoknot conformations that allows for strict control over the protein ratios. This proposal aims to gain a complete structural and mechanistic understanding of the frameshifting frequency in CoV by combining structural studies with biochemical and in vivo experiments. Our aims will be: (#1) to understand the basis for how the frameshifting frequency is maintained by engineering structure-guided mutants to test our equilibrium model, (#2) to determine the structures of the pseudoknot signal in both configurations: permissive and nonpermissive for frameshifting, and (#3) to determine the structure of ribosomes as they encounter the permissive conformation of the pseudoknot.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY / ABSTRACT This proposal, “Elucidating biomolecular dynamics using magnetic resonance augmented by machine learning and quantum technologies” addresses the need for experimental methods to probe the dynamics of large biomolecules in near-physiological conditions in a straightforward and rapid manner. Indeed, many post- translational modifications employed by cells to regulate fundamental biological processes remain poorly understood. Magnetic resonance techniques are well-suited to elucidate these mechanisms, but their application to large biomolecules is limited by sensitivity and resolution constraints. Dr. Seetharam proposes a two-pronged approach to overcome these limitations: (i) develop a novel analysis workflow for magnetic resonance experiments that can extract information from data uninterpretable by humans, and (ii) develop protocols for a novel nanoscale magnetic resonance platform that can operate at high molecular weights and low concentrations. This approach will be conducted through three specific aims that leverage machine learning, quantum computing, and quantum sensing methods. Successful completion of these aims will greatly expand the application of magnetic resonance techniques by enabling an interpretability to information density trade-off in protocol design, bypassing the molecular weight bottleneck, and circumventing the sensitivity limitation, thereby opening a high-resolution window into biomolecular dynamics in near-physiological conditions. Dr. Seetharam is uniquely positioned to perform the proposed work given his interdisciplinary background bridging research and policy, physics and engineering, theory and experiment. He is currently a postdoctoral researcher under the mentorship of Prof. Mikhail Lukin, a pioneer in quantum computing and nanoscale quantum sensing, at Harvard University and Prof. Haribabu Arthanari, a leading expert in biomolecular magnetic resonance, at the Dana-Farber Cancer Institute (DFCI). The proposed research will be conducted at Harvard University and DFCI with support from an exceptional scientific advisory committee experienced in machine learning for magnetic resonance, nanoscale magnetic resonance, single-molecule biophysics, biochemistry, cell biology, and systems biology. This K25 award will provide Dr. Seetharam with the requisite training in machine learning and biological techniques needed to successfully transition to an independent career in magnetic resonance while simultaneously addressing an unmet need in the study of biological processes.