Harvard University

NSF Awards · FY 2025 · 2025-05

This project will develop novel statistical theories and methods for handling large, heterogeneous datasets. Modern scientific applications often produce heterogeneous data of different types for the same problem. For instance, a single-cell biologist may observe multiple types of sequencing data from diverse instruments, all relevant for understanding the biological pathways of a single complex disease. The challenge lies in effectively combining these different data types to build statistical pipelines that outperform those developed using any one data type. Traditional statistical approaches struggle with this challenge. This project will establish a new statistical paradigm to address the complexities of such heterogeneous data while accounting for datasets with billions of variables. The project outcomes will facilitate principled prediction and inference in applications ranging from single-cell biology to precision health and neuroimaging. The project will involve graduate student participation and the development of new curricula at graduate and undergraduate levels that incorporate the project outcomes. Additionally, the research will engage medical professionals to facilitate the dissemination of the research products in current biomedical practice. This project will develop a modern statistical framework to address data heterogeneity in high dimensions, focusing on three key sub-themes: (i) creating principled and robust prediction strategies for multi-view learning, (ii) developing new inference pipelines and prediction analysis frameworks for meta-learning, and (iii) introducing novel inference methods for low-dimensional functionals under transfer learning. In multi-view learning, this project will quantify optimal strategies for cooperative learning, devise new adversarial learning techniques, and analyze the effects of interpolation learning. In meta-learning, this project will introduce new debiasing strategies to tackle inference questions that arise during fine-tuning following an initial phase of pre-training. In transfer learning, this project will develop general-purpose strategies for ranking source distributions and establish new inference schemes for low-dimensional functionals of scientific relevance. On the technical front, this project will introduce novel comparison inequalities, algorithmic proof methods, and leave-one-out techniques that effectively capture the interplay between high dimensionality and heterogeneity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Animal husbandry has been a cornerstone of human subsistence and economic development for more than 10,000 years, and the domestication of horses nearly 5,000 years ago was a major advancement in the reliance of human societies on domesticated livestock. Today, there are an estimated 60 million horses under human management globally, and in the United States, the horse industry provides jobs for more than 4 million people. Each year, an estimated 27 million Americans ride horses, and 2 million own horses. Although primarily valued for transportation, leisure, and sport today, the earliest domesticated horses played a critical role in the prehistoric development and spread of pastoralism. Horses increased food availability both by enabling the management of larger livestock herds and by directly contributing to human nutrition through milk production. The origins of horse milking are poorly understood, but horse dairy products like koumiss have been a core food tradition in grasslands for thousands of years. The goal of this collaborative and multidisciplinary project is to answer fundamental questions about the origins of horse husbandry and the early economic role of horse dairying in ancient pastoralist societies. Archaeological investigations of early horse management and milking clarify the development of Bronze and Iron Age horse husbandry, which laid the foundation for the rise of historic horse-focused empires and the growth of today’s global livestock economies. By bringing together an international team of archaeologists, chemists, cultural heritage managers, and commercial dairy producers, this project creates new collaborations and business opportunities between academic institutions and the food industry, builds stronger training networks for graduate students in food science and analytical chemistry, and contributes to greater public understanding of the history and science of dairying through cultural events, STEM-based outreach programs, and a museum exhibition. The investigators apply cutting-edge analysis of proteins in ancient human dental calculus (tooth tartar) using mass spectrometry to understand the emergence of horse milk consumption and its rise as a vibrant food tradition. This project tests and refines hypotheses regarding the origins and spread of horse dairying connected to horse domestication and early riding, clarifies the role that social and ecological factors played in the success of horse dairying in grassland environments, and examines the relationship between horse dairying and the dairying of other livestock species. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

AI and machine learning algorithms are transforming numerous scientific fields, with some of the most promising approaches relying on mathematical tools called "finite sample probability bounds." These bounds are crucial, for example, in reinforcement learning, which underpins the success of systems like AlphaGo. Additionally, they play a key role in uncertainty quantification and the theoretical analysis of black-box machine learning algorithms. However, classical finite sample bounds have a significant limitation: they are often overly conservative. This conservatism leads to underperforming algorithms and unnecessarily loose guarantees. This project is built around a novel yet straightforward idea: finite-sample bounds can be derived from infinite-sample results. By leveraging recent breakthroughs in optimal transport and probability theory, the project aims to develop a new method for deriving such inequalities. These methods will be applied to problems such as online data-driven decision-making, early stopping rules, and machine learning for multiscale physical models. The PI will interweave their research and teaching throughout the research period and beyond. In particular, the PI will provide research training opportunities to graduate students and develop undergraduate and graduate courses, with course materials made publicly available and with joint participation from industry. Classical concentration inequalities, such as the ones derived by Hoeffding or Bernstein, are over-conservative, are regularly inadequate for heavy-tail distributions, and often rely on the assumption of independence. In this project, the PI tackles those limitations from a new angle, starting with an infinite-sample result for a given problem, such as a central limit theorem, and translating it into a finite-sample result for the same problem by using the concept of distributional approximations. The advantage of this novel proof method is threefold: Firstly, the derived bounds improve as the sample size grows. This leads to inequalities that are considerably tighter than the classical ones. Secondly, limit theorems often hold for many forms of dependent data. This opens a more promising path to derive finite sample probability bounds than conventional Chernoff-based techniques. Lastly, by extending this approach to non-Gaussian limits, the PI develops finite sample concentration inequalities for heavy-tailed statistics. This project will (1) develop a completely novel method for obtaining concentration inequalities, as well as new results for transport distances and optimal transport, (2) provide machine learning theorists with a new set of powerful probability tools for obtaining high-probability guarantees for high-dimensional estimators and dependent and structured data, and (3) provide tighter tail bounds which will lead to algorithmic improvements and improved uncertainty quantification. 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 project seeks to forge new connections between the theory of distribution learning and the study of diffusion models, one of the primary arms of the recent revolution in generative artificial intelligence (AI). We seek to develop a rigorous mathematical framework that can shed light on the remarkable effectiveness of these models in capturing complex, high-dimensional distributions. Our work will advance the frontiers of machine learning theory by enriching the field with new definitions for what these models are accomplishing and new algorithmic goals more closely aligned with how they are used in practice. We aim to provide a theoretical foundation that can guide the development of more efficient and controllable generative models and reshape our understanding of what makes learning tractable in high dimensions. As this effort inherently straddles research divides across theory and practice, we will complement our mathematical insights with extensive experiments on real-world data and with the development of new educational materials and research opportunities at the undergraduate and graduate levels to expand participation in this rapidly growing field. This program is built around three thrusts. The first is to develop novel analytic tools to understand what makes score estimation, the key subroutine on which diffusion models are built, possible for real-world data distributions. We will explore how to leverage properties like low-dimensional manifold structure and stability under perturbation to prove end-to-end algorithmic guarantees for score estimation. Our second thrust introduces a new paradigm of learning: instead of trying to generate samples that are statistically close to the ground truth, we aim to develop learners that can generate samples which are computationally indistinguishable. We will explore whether diffusion models based on "computationally optimal score estimation" can achieve this goal. This new framework promises to better align theoretical guarantees with practice, potentially explaining the empirical success of diffusion models on distributions that were previously considered intractable to learn. Finally, our third thrust will develop a unified theory for solving downstream tasks via foundation models, like sampling acceleration, model distillation, and guided generation. The algorithms we develop here will suggest new design principles for practitioners that could lead to faster and more versatile generative modeling. 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

NONTECHNICAL SUMMARY Quantum materials research is central to technological advancements. From the band theory of solids and its role in the semiconductor revolution to contemporary innovations such as quantum computers, advances in the theoretical understanding of quantum materials have been pivotal in shaping technological progress. Such an understanding hinges on establishing a connection between microscopic properties of materials and their emergent macroscopic behavior. The PI aims to leverage recent advances in the realization of engineered moiré materials with tunable properties to explore such connection. The proposed research aims to establish links between the motion of a single electron on the microscopic scale, characterized by the topology and geometry of electron trajectories and their interaction with complex materials lattice patterns, and the macroscopic behavior of large swarms of interacting electrons in the same platform. Since the properties of single electrons are simpler to study and easier to control, this provides a pathway to engineering the macroscopic properties of materials, potentially leading to the realization of novel phases of matter or those with enhanced properties for various applications. The PI’s proposed education and outreach plan aims to address a significant knowledge gap in the public’s perception of quantum materials research. The goal is to inform and inspire high-school students to pursue careers in quantum materials research by engaging with Boston area high-schools. These efforts will target a broad audience including underrepresented minorities and students from economically disadvantaged backgrounds. Additionally, the PI’s outreach efforts will disseminate knowledge about quantum materials research to a broader audience through the development of online materials that simplify advanced concepts and relate them to everyday experiences. Furthermore, the PI will develop a graduate course to help prepare the next generation of graduate students for research in the field of quantum materials research. TECHNICAL SUMMARY The research project centers on exploring the profound implications of band topology on strong interactions in complex material systems, with a particular emphasis on two-dimensional moiré platforms. The primary scientific problem addressed is the relationship between single-particle characteristics, such as quantum geometry of the electron wavefunctions and the configurational layout of moiré superlattices, and the emergent properties of the system under strong electron interactions. The project is structured around three main objectives. First, the project will investigate the influence of quantum geometry on the formation and dynamics of exotic quasiparticles in interacting topological bands. These quasiparticles, such as skyrmions and anyons, arise from complex wavefunction structures unique to these systems, leading to new potential states of matter with applications in future technologies. Second, the project will develop a framework for understanding topological band theory within quasicrystals, leveraging recent advances in creating moiré quasicrystals. This involves extending traditional topological concepts to quasicrystals, aiming to discover novel topological responses and strong correlation effects. Lastly, the project will seek innovative ways to realize time-reversal symmetric topological phases by exploiting intervalley coupling in moiré systems. While most studies of fractional topological phases have focused on cases where time-reversal symmetry is broken, either explicitly or spontaneously, this research will explore conditions under which time-reversal symmetric fractional topological phases can be realized. The PI’s proposed education and outreach plan aims to address a significant knowledge gap in the public’s perception of quantum materials research. The goal is to inform and inspire high-school students to pursue careers in quantum materials research by engaging with Boston area high-schools. These efforts will target a broad audience including underrepresented minorities and students from economically disadvantaged backgrounds. Additionally, the PI’s outreach efforts will disseminate knowledge about quantum materials research to a broader audience through the development of online materials that simplify advanced concepts and relate them to everyday experiences. Furthermore, the PI will develop a graduate course to help prepare the next generation of graduate students for research in the field of quantum materials research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

A US-led international effort, Overturning in the Subpolar North Atlantic Program (OSNAP), has provided a continuous record of the full-water column, trans-basin fluxes of heat, mass, and freshwater in the subpolar North Atlantic since 2014, in partnership with the UK, Netherlands, Canada and Germany. Results from the first four years of OSNAP observations have challenged the current understanding of overturning circulation in the subpolar North Atlantic. This grant provides funding for an additional four years of observations in order to deliver to the community a ten-year time series of the meridional overturning, heat and freshwater fluxes in the subpolar basin. Variations in these quantities have been invoked to explain changes in a wide range of physical, chemical and biological parameters in the North Atlantic, Nordic Seas, and Arctic Ocean. Thus, by quantifying Atlantic meridional overturning variability and understanding its drivers, OSNAP is providing a critical first step towards addressing societally-relevant, interdisciplinary questions concerning the melting of Greenland ice and Arctic sea-ice, heat content in the Arctic Ocean, climate of the Nordic Seas, and anthropogenic carbon storage. The researchers will actively engage the broader international communities through four workshops, each targeting a societally relevant theme: 1) ocean biogeochemistry and carbon sequestration; 2) overturning in ocean models; 3) Arctic cryosphere; and 4) Nordic Seas variability. Two new postdocs and one graduate student will be supported in this phase of OSNAP and will benefit from the diversity of methodologies and exposure to the large number of OSNAP international scientists. In addition, approximately 20 graduate students from different US and international institutions will receive field training through participation in OSNAP cruises through 2024. The first four years of data from the OSNAP observing system have shown that the eastern subpolar region, from Greenland to Scotland, dominates the mean meridional mass and heat transport in the subpolar North Atlantic, while more than half of the total meridional freshwater transport occurs across the Labrador basin. Based on the success of the previous work, this project extends the time series and address the following critical questions: 1. What governs overturning variability in the North Atlantic subpolar gyre on intra-seasonal to interannual time scales? 2. What are the sources of freshwater across the OSNAP section and what governs their variability? 3. What are the impacts of the meridional heat and freshwater fluxes in the subpolar gyre? Additional motivation for this next phase of OSNAP is provided by the fundamental advancements in our understanding of subpolar North Atlantic dynamics and variability, that will result from the four main U.S. OSNAP mooring arrays, individually and in combination with other OSNAP observations. Further observations are expected to provide a strong observational basis for a new paradigm of overturning in this region, which will include an understanding of the linkage between dense water formation and overturning - a connection present in climate models, yet unobserved to date. Additionally, this this work will further quantify the structure and transport of the upper and deep ocean boundary currents off the east and west coasts of Greenland and within the Iceland Basin, as well as determine their variability and forcing mechanisms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-04

PROJECT SUMMARY/ABSTRACT The long-term objective of this K99/R00 application is to facilitate Dr. Asim Gazi’s development into a scientific leader in engineering and data science methods that enable personalized mobile health (mHealth) support during everyday life. The K99 phase of the project supports Dr. Gazi’s development in three key areas to facilitate his transition to scientific independence. First, Dr. Gazi will develop expertise in core topics relevant to the proposed K99/R00 research, including statistical machine learning methods for uncertainty quantification (UQ) and the behavioral science underlying smoking cessation and suicide prevention. Second, he will train in the research methods necessary to advance uncertainty quantification for machine learning in mHealth and reinforcement learning for just-in-time adaptive interventions (JITAIs), mHealth systems that intelligently intervene by adapting interventions to a patient’s state. Finally, his third training objective will be to develop professionally by growing his collaborative network and gaining experience in grant writing and academic leadership. These career development goals will be achieved while forming the foundation for a sustained line of research on uncertainty- informed decision making for JITAIs. Uncertainty-informed decision making for JITAIs requires (1) UQ algorithms to assess how confident a machine learning model is in its predictions of a patient states; and (2) uncertainty- informed reinforcement learning algorithms to incorporate these measures of confidence into a JITAI’s decision making (e.g., how should a JITAI intervene differently if suicidal risk is predicted with 51% confidence rather than 99% confidence?). The proposed research is divided into two aims accordingly. Aim 1 is to design, evaluate, and deploy UQ methods for prediction models that leverage passive biosensor data as input. Aim 2 is to design uncertainty-informed reinforcement learning algorithms that improve a JITAI’s efficacy by accounting for uncertainty in predictions of a patient’s state when intervening or interacting with the patient. The outcome of this research will be a set of algorithms that enable JITAIs to make uncertainty-informed decisions when adapting interventions and interactions with a patient to their predicted state. These algorithms will remove a significant obstacle in leveraging JITAIs to extend health care support outside the clinic in settings that are high risk or settings that would benefit from machine learning predictions of state. These settings include suicide and addiction, two behavioral health applications that will be investigated as part of this research. This project thus aligns with NIBIB’s mission to transform, through technology development, the ability to prevent and treat disease. The proposed research also fits within NIBIB’s Digital Health Program’s priorities and areas of interest in mHealth. Dr. Gazi will pursue these research and career development goals as a mentee of Dr. Susan Murphy and with the institutional commitment of the School of Engineering and Applied Sciences at Harvard University. This provides an ideal environment of resources and support to help him achieve his training and research goals.

NSF Awards · FY 2025 · 2025-03

Across global contexts, buried settlements are rich resources, preserving the daily lives and practices of their occupants. However, in the complexity and, frequently, large spatial scale of such sites, they present a host of challenges for the researcher. This is especially true for sites that leave little trace above ground, the study of which would typically require intensive and expansive excavation to recover meaningful datasets. This dissertation project has developed an alternative methodology for the study of settlements tested across a multi-sited landscape. The method combines geophysical techniques capable of non-destructively visualizing buried sites and targeted soil coring based upon geophysical datasets to recover subsurface samples for later laboratory analysis. This workflow addresses traditional limitations in the study of buried landscapes, allowing for spatial breadth but also the recovery of stratified subsurface data at significant depths, with special pertinence for fieldwork under time and/or budgetary constraints. The project provides educational and training opportunities for students. The research compares the morphology and chronology of dispersed settlements and nucleated villages. Relationships between the construction, modification, and abandonment of dispersed and nucleated settlements provides insight into cultural practices, with methodological contributions to settlement and landscape 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 2025 · 2025-03

This award provides partial support for the conference, The Legacy of John Tate, and Beyond that will take place at the Harvard University Science Center, March 17-21, 2025. This is a research conference in several areas of Mathematics. Many leading researchers in Arithmetic Geometry, Algebraic Geometry, K-Theory, Topology and related topics will be speakers in this meeting and outline current research they and their collaborators are pursuing in these areas. The collective theme is that they were all worked on by John Tate and in some cases started by him. John Tate proved many fundamental theorems, but perhaps more importantly, he demonstrated new ways of thinking about mathematics that show no signs of slowing more than half a century after he introduced them. The majority of the funds will be used to assist with expenses for junior faculty and graduate students without other means of support, to attend and participate in the meeting. John Tate founded or strongly influenced many of the primary areas of study within algebraic geometry and number theory: his thesis, which gave a different way of looking at zeta and L-functions of number fields, the cohomological reformulation of local and global class field theory, arithmetic duality, nonarchimedean analysis, elliptic curves and abelian varieties, algebraic K-theory of local and global fields, p-adic Hodge theory, and conjectures on algebraic cycles. All of these are active areas of study. The conference will explain in detail how Tate's ideas and results have shaped the field in a fundamental way and are still leading to major advances. This conference will help inspire the current and next generation of mathematicians to extend and go beyond what is presently known in these areas. Conference website: https://www.math.harvard.edu/event/the-legacy-of-john-tate-and-beyond/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Current Artificial Intelligence (AI) implementations in semiconductor-based computing devices are energy-hungry, consuming resources at an enormous scale. Developing computing schemes with a significantly smaller energy consumption and ecological footprint than used in the current systems has immense societal benefits. In this work, first prototypes of miniaturized chemical computing devices will be demonstrated and a pathway towards neural networks that can be trained by large data sets with a fraction of today's energy consumption will be established. The group will also train a new generation of scientists seeking to unlock the power of biologically inspired computation. Developing computing schemes with a significantly smaller energy consumption and ecological footprint than used in the current systems has immense societal benefits. Chemical computing devices may provide low energy computing devices. Many examples of chemical computing devices are based on macroscopic reaction volumes of the order of 10^23 molecules/mL. An energy-efficient architecture will operate at an optimal number of molecules that produce signals with acceptable levels of noise. Today, it is not clear what minimal volumes of computing chemical reaction networks (CRNs) and their reactors can be achieved and whether fast and sensitive read-out and input methods for ultra-low amount of chemicals are available. Furthermore, effective coupling schemes need to be adapted to the microscale (inhibition and acceleration of reaction paths). Innovative ways of dynamically changing a miniaturized chemical computing architecture – an important prerequisite for complex computations – are not existent at this time and need to be discovered and developed. This award will allow investigators at Harvard, together with investigators at IBM Research in Zurich, to produce miniaturized reactor arrays and microfluidic supply structures for coupled CRN nodes of picoliter and femtoliter volumes (10^14 and 10^11 molecules). Their fabrication will rely on IBM's well-established microfabrication capabilities in silicon-based lithography, but also on unconventional methods such as thermal scanning probe lithography (t-SPL) (developed by the IBM team). The implementation of coupling schemes of CRNs in different reactors for the purpose of complex computation will rely on the principal investigator's renowned expertise in the chemistry of oscillating CRNs. The group will contribute physicochemical state variables and condensed matter physics description of the system that are required for the correlation between input and output of the computing CRNs. The team ideally combines strong experience and expertise in the relevant fields of this work (physics, chemistry, microfabrication and computing theory). The team at Harvard will be supported by this NSF award. The team at IBM Research will be supported by the Swiss National Science Foundation (SNSF). This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY Studies of model organisms have revealed many insights into how animals sense their environment and how this input is processed by their brains to produce behavioral output. However, our current understanding of how the brain controls behavior is derived from a handful of species that share certain features, e.g., their brains are highly regionalized and stereotyped. Yet, not all animal brains are structured in this manner, and to identify general principles for the control of animal behavior, a broader diversity of brains needs to be investigated. We will study the acoel worm, Hofstenia miamia, which is a new research organism with key features that position it to address major questions in behavioral neuroscience. First, Hofstenia, a voracious mangrove predator, is a marine invertebrate that performs complex behaviors in the lab, and our recent work has shown that it is amenable to rigorous, quantitative studies of its behaviors and of behavior-induced physical changes in its aquatic environment. Second, as an acoel, Hofstenia diverged from models such as flies and mice 550 million years ago and possesses a brain that does not show evidence of regionalization, providing an opportunity to study distributed computation. Third, the Hofstenia brain can regenerate from any starting condition, enabling the study of how robustness is encoded in the brain. Fourth, Hofstenia is highly tractable in the lab, offering many genomic resources and tools for functional genetics, which will make it possible to collect neural activity data. Given these features, the overall objective in this proposal is to leverage Hofstenia to study its behavior and environment at the whole-organism level. Specifically, we aim to: 1) Develop methods for rigorous measurement and quantification of worm behavior and its environment, using pose estimation to infer worm action sequences, water flow measurement to quantify physical changes around worms, and tracking of prey to quantify the worm’s biological environment. 2) Perturb both organism and environment to uncover mechanisms of behavioral control. We will amputate animals to determine how regenerating brain features correlate with elements of behavior, and deliver artificial mechanical flow stimuli to understand how animals read their physical environment. 3) We will assemble a team of researchers with expertise in behavioral neuroscience, pose estimation, connectomics, neural activity dynamics, fluid mechanics, computational modeling of behavior, and in the organism and its regenerative abilities to plan work that will enable the integration of neural activity data with behavioral and environmental data to reveal how distributed computations in the brain enable the animal to produce action sequences that successfully navigate the environment and capture prey. This proposal is innovative in its use of a new research organism, in its pursuit of behavior-environment quantification in the whole-organism context, and in its potential to reveal general principles of behavioral control, particularly via distributed computation.

NSF Awards · FY 2025 · 2025-01

Computing’s carbon footprint is growing at an unsustainable rate. To overcome this global challenge, researchers and professionals are exploring techniques to reduce the total carbon footprint of computing systems across their entire lifetime, considering both embodied carbon (due to emissions during manufacturing) and operational carbon (from day-to-day use.) This represents a paradigm shift in humanity’s approach to designing computing systems, evolving from energy-efficient design (balancing performance and energy consumption) to carbon-efficient design (balancing performance and total carbon footprint). While today’s efforts in carbon-efficient computing are essential, many of them focus on today’s silicon-based technologies, and thus do not fully capture the wide range of future beyond-silicon technologies that are actively being investigated for future directions in energy-efficient computing. In particular, monolithic three-dimensional integrated circuits (3D ICs), which have multiple layers of computing and memory circuits densely integrated directly on top of each other in three dimensions, are unmatched in their projected energy efficiency benefits. However, their implications on computing’s carbon footprint are not as well understood. This project aims to answer the following question: should monolithic 3D ICs be pursued for carbon-efficient computing, considering trade-offs in power, performance, area, operational carbon, and embodied carbon? This project also emphases two key goals in education. The first is to leverage recent developments in Augmented Reality and Virtual Reality (AR/VR) to help students visualize complex manufacturing processes for monolithic 3D ICs. The second is to instill the idea that carbon footprint is a first-class figure of merit for computing systems, alongside conventional metrics including power, performance, and area. This project focuses on addressing three key challenges. Challenge 1: quantifying the embodied carbon footprint of future monolithic 3D ICs, as opposed to silicon-based ICs that are already commercially available, is particularly difficult for manufacturing processes that are not yet in production. Challenge 2: it is not clear which metrics (or figures of merit) designers should target for optimizing carbon efficiency, instead of energy efficiency. Challenge 3: there is high variability in quantifying carbon footprint, due to both (a) transparency: designers may not have full access to detailed carbon emission numbers from manufacturing, and (b) varying energy sources: the carbon footprint of executing a computing task varies depending on the energy source (for example, renewable versus non-renewable), which also changes over time. The technical approach is organized across four research thrusts designed to address these three key challenges. Thrust 1: Modeling embodied carbon of monolithic 3D ICs comprising emerging beyond-silicon nanotechnologies. Thrust 2: Analyzing metrics of carbon efficiency to drive carbon-aware optimization. Thrust 3: Developing case studies in carbon-efficient computing. Thrust 4: Leveraging mathematical robust optimization techniques to design carbon-efficient computing systems even when there is uncertainty in carbon accounting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The project aims to address critical gaps in our understanding of what noisy quantum devices can achieve, especially in the near and medium term. The investigators plans to explore three key areas: 1) developing new methods to demonstrate quantum advantage in noisy environments, particularly by making Shor's famous factoring algorithm feasible on current quantum devices; 2) proving the limitations of quantum devices that rely on geometrically local interactions, highlighting the need for long-range connectivity in experiments; and 3) creating benchmarks using machine learning theory to verify whether these devices are correctly implementing their intended tasks. By focusing on these areas, the research will develop new protocols and provide theoretical insights that are crucial for advancing quantum computing, even at current noise levels. The project will leverage ideas from quantum computing and machine learning, and closely collaborate with experimentalists to ensure their findings are both rigorous and practically applicable. The project will also develop new courses to bring together graduate and undergraduate students from different departments to grapple with present-day challenges in quantum computing. The proposal will focus on three critical areas to deepen our theoretical understanding of noisy quantum devices, i.e., Noisy Intermediate-Scale Quantum (NISQ). First, the researchers will develop a novel fault-tolerance theorem that maintains constant-depth overhead, enabling Shor's algorithm on NISQ devices. Second, they will aim to establish no-go theorems for geometrically local noisy quantum systems, providing rigorous proof that long-range interactions are essential for achieving quantum advantage in these systems. Finally, they will leverage learning theory to construct algorithms that can rigorously determine whether a noisy quantum device accurately executes the programmed quantum circuit or adiabatic process. This research will fill theoretical gaps in the understanding of NISQ devices, introducing new protocols that offer low-overhead fault tolerance, and provide formal proofs of the necessity of non-local interactions in quantum computation. Interdisciplinary techniques are expected to play a crucial role in the proposal. Collaboration with experimental groups will ensure the relevance and applicability of the theoretical models of noisy devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project examines how communities adapt to new lives in post-conflict conditions. The investigators specifically study the different impacts that international and local policy commissions and social and community-based efforts have on peace and reconciliation after conflict. In addition to providing scientific training for a graduate student in anthropology, the research findings will be made available to policy and legal agencies that address the needs of families and communities in post-conflict societies. The researchers will also produce visual materials and films that will enhance public knowledge of the most effective strategies for post-conflict rebuilding and healing. In order to test several social, economic, and legal drivers of post-conflict rebuilding and adaptation, the researchers will conduct qualitative, ethnographic research that includes semi-structured interviews, participant observation, and visual and online ethnography. The broader research findings will contribute to global studies of transitional justice, legal anthropology, post-conflict peacebuilding and reconciliation, and the anthropology of human rights. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY/ABSTRACT Environmental inputs are dynamically processed by a network of functionally distinct cell types organized into specific brain circuits to generate purposeful motor actions. While it is evident that physiological requirements significantly influence the selection of behavioral responses, the precise mechanisms through which internal states shape these innate behaviors remain poorly characterized. The lateral hypothalamus (LH) and the midbrain periaqueductal gray (PAG), which are interconnected, play a central role in various homeostatic and innate behaviors such as sleep, feeding, parenting, mating, and aggression. However, achieving a comprehensive understanding of how sensory information integrates with internal states to generate purposeful motor actions has remained a significant challenge due to their functional and transcriptomic diversity, where genetically defined neurons respond with great heterogeneity to a wide range of behaviors. Moreover, while tools have been developed to independently measure activity dynamics, connectivity, and transcriptional profiles of individual neurons, it remains challenging to integrate this diverse information into a coherent model of behavior. To address these challenges, I will develop a novel technological pipeline that enables monitoring of large-scale neural activity in freely moving animals and identifies transcriptomic identity of the same individual neurons by combining post-hoc spatial transcriptomics in the same brain tissue. My preliminary data examining neural population activity in the LH and PAG during social behavior suggest that LH neurons exhibit state-specific information whereas PAG neurons exhibit social distance information. I hypothesize that unique transcriptomic axes are involved in the neural dynamics that encode homeostatic, physiological states, and social behavior in these brain regions. This approach will enable functional probing of multiple cell types and their interaction during innate behavior and offer a mechanistic understanding of neural computation by revealing each recorded neuron’s molecular expression that contributes to their activity patterns. In the independent R00 phase, I will utilize these approaches to understand the neural circuit mechanism of aging-associated changes in cognitive, homeostatic, and social behavior in terms of behavior, gene expression, and neuronal activity. The successful completion of this project will provide a platform for future experiments toward understanding the aging-associated innate behavior circuits. The training phase of the award will be conducted in the lab of Dr. Catherine Dulac at Harvard University. I will be mentored by an outstanding team of scientists on my advisory committee, with specific training goals and career guidance. In my application, I outline a comprehensive plan for the acquisition of conceptual, technical, and professional skills that will enable my transition to an independent research position.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract My lab seeks to address mechanisms of cellular fatty acid metabolism and their implication in cellular functions. Subcellular organelles called lipid droplets (LDs) play an integral role in lipid metabolism by storing lipids and buffering cellular fatty acid fluctuation to prevent lipotoxicity and to adequately provide lipid sources as needed. Much effort has focused on defining and elucidating the pathways governing LD formation, but less is known about how fatty acids are released from LDs in (patho)physiological circumstances. A major long-term goal of our research program is to understand how cells regulate fatty acid recycling pathways to control metabolism and signaling in health and disease. In the next five years, we will focus on two areas outlined in this proposal: 1) What are the regulatory mechanisms underlying the autophagic lipid degradation pathway? Lipophagy is a critical cellular pathway that degrades LDs to supply lipids for cellular functions. We recently discovered a specific lipophagy receptor, a crucial first step toward revealing the mechanisms of this pathway and its contribution to cellular lipid metabolism. Building on this foundational discovery, my lab will investigate the cell states and metabolic conditions in which the lipophagy pathway participates and its mechanistic foundations. 2) How are lipids trafficked within cells and utilized at inter-organelle membrane contact sites? Lipids recycled from LDs are essential for various cellular functions, including membrane biogenesis, energy production, and signal transduction. However, the process of how fatty acids traffic and find their destinations in cells remains elusive. My lab will elucidate how LDs crosstalk with various subcellular organelles to efficiently transfer lipids and support cellular metabolism. Our proposed research will reveal fundamental principles of lipid recycling mechanisms and contribute to the development of potential therapeutic strategies focused on defective lipid metabolism in various metabolic and neurodegenerative disorders.

NIH Research Projects · FY 2026 · 2025-01

Project Summary: Although many highly regenerative animals harbor adult pluripotent stem cells, the molecular and cellular mechanisms by which these cells form during development remains unknown in any species. Major gaps in our understanding of adult pluripotent stem cell formation include the identity of the molecules that govern the specification and, or maintenance of these cellular populations. Studying regenerative species can reveal mechanisms for how adult pluripotent stem cells are maintained through development, nature’s solution for making a faithful and easily programmable population of stem cells. Many research organisms have been developed into model systems to interrogate cellular contributions and molecular players in regeneration, yet it has been challenging to access embryogenesis in most of these species. This has created a gap in knowledge in understanding the developmental origins of adult pluripotent stem cells and in turn, has left many outstanding questions, specifically: how are adult pluripotent stem cells (aPSCs) formed in highly regenerative species. The long-term goal of this project is to determine how the identity of adult pluripotent stem cell population is established during development and how it is retained in adult animals. The overall objective of this proposal is to identify mechanisms for aPSC formation during embryonic development in Hofstenia and to determine the accompaning stem cell-specific chromatin state. Preliminary data indicate one pair of cells in the embryo gives rise to cells that resemble aPSCs in distribution, behavior, and gene expression. The rationale for this proposed work is through leveraging the developmental lineage of aPSCs in Hofstenia we identify the essential genetic components of sustained pluripotency. Our central hypothesis is that specific gene regulatory networks form aPSCs either by the specification or maintenance of open chromatin. This hypothesis will be tested by pursuing three specific aims: I will (1) identify the complete molecular trajectory of adult pluripotent stem cell (aPSC) formation to understand the progression of stem cell properties and reveal putative regulators of stem cell formation, (2) define the chromatin landscape associated with of aPSCs during development to uncover genomic states that enable pluripotency, and (3) functionally assess transcription factors involved in specifying and, or, maintaining stem cells. Our approach is innovative because it is one of the first to mechanistically interrogate the developmental origin of an adult pluripotent stem cell population, and further because it uses an integrative strategy that combines genomic and cell biological approaches. The proposed research is significant because it will advance our understanding of the regulation of stem cell identity in development and may open new avenues of research for understanding stem cell biology. During the fellowship award period I will learn a multitude of approaches in Hofstenia, expanding my experimental toolkit to address mechanistic questions about stem cells all within training environment of Harvard University which has the experts in all the disciplines which my work will span.

NSF Awards · FY 2025 · 2025-01

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

NSF Awards · FY 2025 · 2025-01

This grant provides funding for a workshop entitled "Reinforcement Learning: from Theorem to Real-World" to be held in Boston, Massachusetts, 23-24 January 2025. Despite Reinforcement Learning (RL) growing in influence across fields like healthcare, robotics, energy systems, and transportation, a substantial gap remains between theoretical progress and practical deployment. This workshop on RL aims to close this gap by uniting experts from computer science, control theory, machine learning, and specific application areas to focus on core issues, including aligning RL theory with practical requirements, incorporating domain-specific structures into RL algorithms, applying RL in diverse real-world settings, and refining benchmarks and performance metrics. The ultimate objective is to create a forward-looking RL research agenda that promotes both theoretical rigor and practical feasibility. This workshop will have broad impacts by fostering interdisciplinary collaboration and encouraging innovations that can improve applications across fields. Additionally, it will focus on education and training strategies to broaden participation in RL, preparing students from varied disciplines and backgrounds to work in this growing area. The intellectual contributions of the workshop are three-fold: (1) it will convene a complementary group of RL experts to collaboratively address common technical challenges, establishing a focused research agenda for RL; (2) it will identify critical gaps in theory, algorithm design, dataset availability, and benchmarking practices, enabling advancements that enhance RL’s reliability and utility across applications; and (3) it will enrich RL research through interdisciplinary insights from engineering, societal systems, science, and computational sciences to foster a more holistic approach to RL development. By gathering researchers across disciplines, the workshop aims to establish a research, education, and application framework for RL that is adaptable, rigorous, and inclusive. 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 · 2024-12

Project Summary Abstract: Our goal is to develop methods for combined molecular and ultrastructural imaging of neural cells and tissues. This goal is motivated by the critical need in neuroscience and neuropathology to understand the interplay between morphological and molecular phenotypes of neural networks in health and disease. Ultrastructure refers to cellular features that can be seen in electron microscopy (e.g., various membranes), while molecular specificity is typically achieved by fluorescence microscopy. A combination of these two techniques would provide orthogonal information on neural physiology. However, state-of-the-art approaches to combine fluorescence and electron imaging of cells and tissues have limitations. The major limitation of correlative light and electron microscopy (CLEM) methods is the requirement to use antibodies that are difficult to deliver into cells without permeabilization. The major limitation of heavy-metal-based tags is their reliance on the same contrast mechanism as ultrastructural imaging in EM, making the two difficult to distinguish. We will address the limitation of CLEM probes by developing a set of smaller immunofluorescent labels based on recombinant fragments of antibodies called single chain variable antibody fragments (scFv). We will also address the limitations of heavy-metal-based tags by developing multicolor nanoparticle and small-molecule probes (cathodophores) whose optical emission is induced directly by the electron beam in a process termed cathodoluminescence (CL). Thus, we will establish two generally applicable methods for multicolor EM: one via CLEM using scFv probes and the other via CL using cathodophores. The following three aims are proposed: Aim 1: To develop detergent-free multicolor immunoprobes that reveal molecular components via CLEM. Aim 2: To develop sub-10-nm lanthanide nanoparticle cathodophores that can be used as multicolor probes for localization of single proteins in electron microscopy. Aim 3: To establish small-molecule dyes as cathodoluminescent contrast agents in multicolor EM. Our approach is innovative because scFvs that we will engineer in Aim 1 are a new type of molecular probe that holds promise for multiplexed CLEM of neuronal tissue. In Aims 2 and 3, we are exploring a new light-electron-matter interaction in neurobiology research: optical emission induced by the electron beam – cathodoluminescence. Our extensive preliminary results confirm feasibility and lay the necessary foundation for the proposed work. Our technology-driven approach has the potential to be impactful in neurobiology and medicine, redefining the way we visualize and understand disease at the level of brain ultrastructure.

NIH Research Projects · FY 2026 · 2024-12

Project Summary Sleep and pregnancy appear to have a bidirectional relationship: pregnancy alters sleep by imposing strong sleep drive (somnolence) during the first trimester, and in turn inadequate sleep is associated with health complications in the mother and her offspring. Despite these correlations there is little mechanistic understanding of the relationship between sleep and pregnancy. Based on a simple but striking observation I made in Drosophila melanogaster, I have developed a model that allows for study of the causes and consequences of reproductive sleep need. I found that sleep is strongly upregulated during the one to two weeks females experience gravidity (the state of carrying young). I call this phenomenon mating-induced somnolence, and this proposal takes steps to uncover its function and underlying mechanisms with the goal of addressing the severely understudied yet critical interplay between sleep, pregnancy, and women’s health. My first Aim will assess the function of increased sleep in female Drosophila after mating. I hypothesize that sleep is promoted to compensate for the physiological burden of reproduction. I will test this idea by characterizing various measures of health of the mother and her offspring – including reproductive health, developmental viability of progeny, and gut health – while preventing the mating-dependent increase in sleep. My second Aim will determine the mating-status signals that promote sleep. There are several pathways that orchestrate the post-copulatory behavioral and physiological state, and I will perform manipulations in each of these pathways to determine their necessity or sufficiency in generating mating-induced somnolence. My third Aim is an exploratory aim that characterizes the ways known sleep circuitry in Drosophila contributes to, and is altered by, mating-induced somnolence. I will achieve this through a perturbation-based screen to identify which sleep neurons are required for the post-mating somnolent state, and via characterization of physiological changes to a key sleep circuit during mating-induced somnolence. This proposal will help elucidate the mechanisms and consequences of gravidity-dependent sleep regulation, something of great public health significance given the disproportionate prevalence of insomnia in women and the susceptibility of women and their children to negative health consequences during pregnancy. Further, Drosophila mating-induced somnolence provides an opportunity to study the way sleep need is measured, regulated, and fulfilled in general, which is my long term goal as an independent academic researcher. The hands-on mentorship from my sponsor Dr. Dragana Rogulja and co-sponsor Dr. Michael Rosbash will ensure my successful completion of this research proposal. As I push to expand my technical skills, knowledge base, scientific communication, and mentoring proficiency throughout my training, they will provide critical professional guidance towards achieving my ultimate goal of establishing my own academic research group.

NIH Research Projects · FY 2026 · 2024-11

Project Summary/Abstract: Our research is focused on the synthesis and biological study of antibiotics to address the growing problem of modern multi-drug resistant (MDR) pathogens of critical threat. By introducing three key structural variations within the scaffold of an established inhibitor of the localization of lipoproteins (lol) pathway, we have synthesized novel antibiotics with superior potencies against MDR Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae). Crucially, these more potent antibiotics induce a much lower frequency of spontaneous resistance in E. coli (FoR ~10-9) than the previously reported inhibitor (FoR ~10-7). We propose to diversify and improve the modular platform we have developed to synthesize large numbers of candidate antibiotics with improved potencies and optimized pharmacokinetic and pharmacodynamic profiles, which will be evaluated with in vitro and, where appropriate, in vivo studies. Furthermore, we aim to deepen the understanding of the mechanism of action of our molecules by cryo-EM imaging of the molecules bound to their target, the lolCD2E lipoprotein transport machinery. This will also inform structure-based design of future antibiotics candidates.

NIH Research Projects · FY 2026 · 2024-11

Abstract The application's broad objective is to recapitulate critical features of the developing human forebrain, including its patterning, morphology, and regional cellular organization. The application also seeks to develop a robust platform to perform high throughput penetrant and cell type-specific genetic perturbations to elucidate the mechanisms underlying human forebrain development. In doing so, it develops and integrates novel technologies from stem cell and developmental biology, molecular biology, bioengineering, single-cell genomics, and imaging. Given that model organisms inadequately mirror human brain development and disease, and ethical constraints limit direct human brain studies, it's imperative to develop in vitro models that accurately represent its microenvironment, organization, and function. Accurately reproducing the developing brain's attributes and the ability to perturb the genetic network at scale is crucial to decipher mechanisms underpinning human brain development and disease. In preliminary work, we established bioengineering methods to print and differentiate single-lumen cysts into forebrain-like tissue. We further demonstrated that coupling these cysts with signal coated generates spatial gradients of signals. With the ability to expose hundreds of organoids per experiment to distinct signaling gradients and using statistical analysis, we could rapidly learn how to generate correctly patterned complex tissues. Using these approaches, we will generate organoids with correctly integrated choroid plexus producing cerebrospinal fluid, an adjacent cortical hem, a more distal correctly layered cortex, and ventral inhibitory interneuron progenitors. The cortical hem and choroid plexus, adjacent to the developing cortical tissue, are essential for proper cortical development. We expect to be successful, given our preliminary results, and believe that the resulting platform to study human brain development will be broadly useful to the community. Further in preliminary work, we established bioengineering methods for targeted penetrant perturbation of individual organoids on a coverslip at a scale that was impossible previously because of the very high costs. This platform will allow us to perturb hundreds of genes per experiment, and we will test and demonstrate its capabilities by identifying human-specific mechanisms of forebrain neural tube patterning and closure. Uncovering mechanisms underlying the patterning and morphogenetic events leading to neural tube closure is essential for understanding human brain development. In sum, the proposal will establish a platform to mimic human brain development more accurately and further establish the ability to perform penetrant genetic perturbations, both at a scale not previously possible. Together these abilities will allow the community to better understand the development, morphogenesis, and mechanisms governing human brain development and disease.

NSF Awards · FY 2024 · 2024-11

There are numerous examples of biologically inspired robots that mimic features of animals and plants, and many can operate in natural environments. However, the overwhelming majority of these devices are rigid, inorganic, and created from toxic and/or non-biodegradable materials; they are in many ways the antithesis of the organisms that they mimic. Roboticists often cite tasks in natural environments – forests, oceans, farms – as quintessential examples of the broad utility and future of robotics. However, operation in any of these environments is impossible or highly impractical if the robots performing these tasks cannot autonomously return “home”. Furthermore, failure of any individual robot would require human intervention or result in environmental contamination by a “dead” robot left behind. This project will develop “living materials” that can be composed to create biodegradable robots that operate autonomously in natural environments. At the end of a task, the devices remain in place and degrade into harmless or useful substances. The development of biodegradable living materials represents the next phase in robotics, ushering in new capabilities for environmental monitoring and remediation; infrastructure inspection, long-term monitoring, and repair; environmental exploration, including locations otherwise difficult to access such as deep ocean and remote ecosystems; and biomedical robots for internal medicine and wearable assistive devices. The materials and experimental focus of this project will also facilitate structured hands-on learning experiences for K-12, undergraduate, and graduate students. The current reliance on a classical pallet of engineering materials is more of a hindrance than an enabler for the future of robotics, in particular for fulfilling the promise of autonomy in natural environments. More traditional robot architectures are fragile and expensive, leading to conservative design, control, and deployment strategies, highly limiting the scope of use and impeding the achievement of science-fiction-like tasks in natural terrestrial or aquatic environments. Furthermore, all such devices must be collected at the end of their operational lifetime, raising additional challenges for full autonomy. This program focuses on robots created from “living” materials that embody function in soft biodegradable composites. These materials will perform a desired task and, at the end of the mission, simply remain in place and degrade into benign or potentially beneficial substances. This concept represents a paradigm shift for how we can think about autonomous devices operating in unstructured environments, relaxing more typical control goals and guarantees since device failure would be inconsequential or potentially even useful. This vision motivates research into materials chemistry of new artificial muscles, new architectures for electrical and chemical energy storage and conversion, materials-based methods for controlling the distribution of energy and sequencing of actuation in a programmed manner, and multi-scale multi-material fabrication strategies for creating biodegradable composites that embody these functions. This project will conclude with demonstrations of heterogeneous collectives of biodegradable synthetic “living” robots autonomously performing tasks including assisted agriculture and environmental remediation, and subsequently degrading in place. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Often people need to make moral decisions in cases where no clear rules apply. For instance, during the COVID pandemic people often faced difficult tradeoffs and choices in situations where no clear rules existed. Philosophers have suggested that one promising approach is for a person to act in the way that they believe everybody would agree to, if there were time for everybody to talk things over. This research project asks whether ordinary people imagine a kind of bargain between interested parties and regard the imagined outcome of the bargain as the most morally appropriate solution. The project develops precise computational models of how people do this, and then tests the models by conducting experiments. These experiments ask people to reason about everyday situations, and also puts people in structured economic exchanges with each other to explore whether their choices reflect basic principles of bargaining. Finally, the project embeds bargaining principles within current artificial intelligence (AI) 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.