Barnard College

NSF Awards · FY 2026 · 2026-09

The fruit fly uses its senses, its memory, and its internal states to make decisions about where to lay an egg, what to eat, and whether to pursue or accept a mate. This project seeks to discover the neural pathways and circuits in the fruit fly’s brain that enable it to flexibly make decisions. These parts of the brain are difficult to study with traditional methods. The recent availability of connectome data – full reconstructions of entire brain volumes – now provide researchers with access to the parts of the fly’s brain that ostensibly combine sensory information with memories and internal states to effect an action. This project will analyze the fruit fly connectome using methods that have traditionally been used for studying social networks. Network science tools can identify groups of neurons that interact with regions of known function, thus enabling the discovery of neural circuits that support cognitive-level function in the fruit fly. This project will advance our understanding of how flexible, context-dependent interactions happen in the brain. Current technology cannot autonomously emulate these flexible and context-dependent behaviors at such a small scale. The project’s education plan will broaden participation in scientific research through innovative workshops and courses for middle school, high school and college students that engage the next generation of researchers in learning basic coding and network analyses, and in exploring the connectome. The public availability of connectome data also makes it possible to engage young people in computational neuroscience research at scale. In addition to strengthening the U.S. domestic workforce through student training, this project will increase national competitiveness in science and engineering – specifically in pursuit of elucidating the parts of the fly’s brain that endow it with flexible, context-dependent behavior. The neural circuits that perform context-dependent computations are largely unknown and difficult to study with commonly used methods such as genetic manipulations which require a known target with an associated driver line. Researchers posit that the diffuse neuropils of the superior protocerebrum contain circuits for higher-order processing. The Drosophila connectome is a volumetric reconstruction of an entire brain, including annotated cell types with no currently known function in the diffuse regions of the brain. This project will leverage the connectome to discover the neural circuits that produce cognitive-level computations related to decision-making in oviposition, and other ethologically-relevant behaviors. Community detection methods, together with other statistical network analyses and computational modeling, will be employed to: (1) characterize the circuit structure of the understudied regions of the brain believed to support context-dependent computations; (2) investigate the inputs to the oviposition pre-motor circuit to connect populations of unknown cell types to behaviorally-relevant function; and, (3) determine the utility of community detection methods for detecting microscale circuits for dendritic processing. The project will lead to experimentally testable predictions for the neurons, cell types, and circuits that drive context-dependent behaviors in a model organism that has a wealth of genetic tools available for testing the resulting predictions. Furthermore, these studies will lead to computational principles of Drosophila brain organization and an enhanced understanding of how to use network science tools to reveal and investigate neuronal circuits. 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 · 2025-10

The project integrates formal verification techniques into creative technology education. It targets students working with microcontroller-based embedded systems and interactive media, including sound synthesis and art installations. As programmers in general, and especially creative technologists, increasingly use Artificial Intelligence (AI)-assisted tools to write code, they face growing challenges in managing and verifying the correctness of their systems. The project’s novelties are its integration of runtime verification, model checking, and reactive synthesis into creative embedded systems curricula, and its adaptation of these tools for use by non-verification students. The vision is that program verification is deeply integrated into every student’s experience, even if they are not explicitly studying verification. The project’s impacts are to make verification tools accessible to new user communities, address the urgent need for trustworthy AI-generated code in the maker and arts communities, and train students to be prepared for verification-focused engineering careers. The project advances educational research by exploring how formal reasoning can be taught through hands-on, project-based work and establishes a pipeline for future research into verification of AI-generated code for media arts software. Technically, the project develops new pedagogical strategies and tool integrations to introduce formal verification tools such as RTLola, TLA+, and Temporal Stream Logic (TSL) in creative contexts. The investigators adapt these tools to microcontroller-based platforms and design learning modules that align with the workflows of artists, designers, and creative coders. 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 · 2025-09

The environment around us is constantly changing, in ways both big and small. Biological systems must utilize processes to maintain homeostasis (a physiological equilibrium) in order to continue functioning amidst a fluctuating environment. The electrical activity of neurons controls essential bodily functions and behaviors that are necessary for survival. Within the nervous system, another set of cells, called glia, regulate and support neurons. Recent work by the PI, and others, has shown that glial cells play important roles in helping neurons maintain homeostasis. This project will use genetic tools available in fruit flies to elucidate novel mechanisms for how glia and neurons interact to enable animals to survive and thrive in fluctuating environments. The project will be integrated with inclusive educational practices, including experiments completed by undergraduate students in the PI’s Neurobiology Lab Course, and participation in a research based mentoring program for first year undergraduates from historically underrepresented groups in STEM. This project will also develop a Science Translators Program, creating resources for non-scientists to learn about socially relevant science research, in languages other than English. Together, the objectives of this grant will incorporate a diverse group of students in studying biological mechanisms through which animals respond to stress, giving students opportunities to develop a sense of belonging in STEM, learn hands-on lab skills, and present their work at conferences and in peer-reviewed publications. While much work has focused on the cell intrinsic and neural circuit level mechanisms through which neurons regulate their excitability, mechanisms by which glia regulate the nervous system response to environmental stress are less well understood. Using Drosophila melanogaster, the PI’s lab has recently identified glial homeostatic roles for two genes, the voltage-gated potassium channel seizure and the ion transporter ncc69, in neuropile ensheathing glia (EGN). EGN have been shown by others to act as phagocytes in adult D. melanogaster, therefore this project tests the hypothesis that glial phagocytosis of neurons, in processes such as synaptic pruning, modulates the ability of the nervous system to maintain homeostasis in response to environmental stress. Furthermore, the project will tease apart the impact of developmental and adult glial function on nervous system homeostasis. Ultimately, the project takes advantage of the wealth of genetic tools, neurophysiological and behavioral assays available in D. melanogaster to uncover basic principles of glial function in their regulation of neural activity and animal behavior. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Abstract The class-III diiron proteins, a group of integral membrane proteins unified by a histidine-rich active site, catalyze a wide range of reactions including hydroxylations required for the production of sphingolipids (an essential component of the myelin sheath), desaturating fatty acids that regulate metabolism and cancer progress, and hydroxylating straight chain alkanes, enabling them to be biodegraded in oil-impacted environments. The diverse chemistry in this enzyme family is controlled, at least in part, by substrate channels well matched to the structure of the different substrates. The structure of the active site, only recently determined, is puzzling because no covalent bridge linking the two redox active iron ions is apparent. Electrons are required to activate these enzymes. Some class-III diiron proteins have their electron-transfer partners covalently bound while others do not. The functional significance of these different modalities is not known. This program will target structure/function relationships in alkane monooxygenases (AlkB), the most biochemically tractable member of the class-III diiron protein family, to understand reaction mechanisms and the factors that control reaction scope. We will combine mechanistic work on AlkB variants with spectroscopic characterization of the protein using a variety of techniques that can shed light on the three dimensional and electronic structure of the active site. We will mine our large library of functional AlkB enzymes (currently we can express more than 60 different AlkBs from different bacteria) to search for patterns in reactivity. We will utilize information obtained from the cryo-EM structure of AlkB we published in 2023, as well as deep mutational scanning, to develop a large library of variant to probe the structural factors that control reactivity. We will determine the mechanism of the recently discovered capability of AlkB to catalyze the defluorination of fluorinated alkanes. We will attempt to determine the structure of the new family of AlkBs whose existence we recently reported—a fusion protein containing two electron transfer partners not previously seen linked to AlkB—and characterize its reactivity. We will also explore whether archaea express AlkBs capable of participating in the global carbon cycle. We expect to generate new insights into mechanisms of selective C-H and C-F bond activation. By leveraging our unique expertise with this key member of the class-III diiron proteins, we expect to contribute to a fundamental understanding of how these metalloenzymes work, with implications for both human and environmental health.

NSF Awards · FY 2025 · 2025-09

Building artificial intelligence (AI) systems that approach human cognitive flexibility requires a better understanding of how the brain uses visual and linguistic information to achieve specific goals. While previous research in cognitive neuroscience and AI has focused on visual classification tasks, such as identifying objects or labeling scenes, real-world behavior is more nuanced and often depends on selecting task-relevant information, guided by the observer’s goals. Critically, this process draws not only on the visual features of the scene, but on conceptual and linguistic knowledge as well. This project examines how people flexibly extract and use visual information in context and how this information is represented in computational models, supporting the goal of advancing theories of cognition and the development of more adaptive, human-aligned AI systems. The project integrates methods from visual AI (convolutional neural networks), language-based AI (large language models), neuroscience, and cognitive science. First, deep networks are trained to predict language embeddings of human scene descriptions elicited under different task goals, capturing how semantic meaning maps onto visual features. Next, these networks are reverse-engineered to generate activation maps that identify the regions of each image most relevant for a given task. These maps are validated using both behavioral experiments and electroencephalography (EEG). A novel multivariate analysis technique (dynamic electrode-to-image mapping) is used to track when and how these task-relevant features are processed in the brain. Finally, the project assesses whether features identified by the brain contribute to successful behavior. This approach reveals how visual, conceptual, and neural systems interact to support goal-directed perception, offering a new framework for understanding scene processing and for building AI systems that better reflect human needs and capacities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

Project Summary/Abstract The Kaposi sarcoma-associated herpesvirus (KSHV) is an etiological agent of cancer. Among these malignancies, Kaposi sarcoma (KS) is particularly prevalent in people living with HIV. Central to identifying potential points of therapeutic intervention is a fundamental understanding of how the virus drives cancer progression at the molecular level. Since its discovery decades ago, the field has excelled at laboratory studies of KSHV infection. Particular challenges remain, however, in studying actual tissue. This proposal attempts to bridge this gap with a focused project that connects key collaborators in the greatest areas of need. By building on established relationships with a biorepository and leveraging resources at an advanced core facility, this project aims to study hard-to-find specimens with frontline genomics methods. All experiments will be performed by undergraduate students. Specific Aim 1 will identify virus-associated host enhancers and regulated genes in KS tissue. The project will obtain clinical specimens of KS from the AIDS and Cancer Specimen Resource. Skin lesions will be brought to and processed at the Mount Sinai School of Medicine Center for Advanced Genomics Technology for multiome single-cell ATAC-seq and single-cell RNA-seq experiments. The sequencing data of open chromatin and gene expression will be analyzed by a team of undergraduates from Barnard College. The goal is to identify enhancers and regulated genes modulated by the presence of viral infection. This will provide a map of chromatin interactions from actual cancer tissue and represents a transformative step beyond cell culture studies. Specific Aim 2 will identify host super-enhancers in KS tissue. To complement the open chromatin experiments of Aim 1, Aim 2 will use ChIP-seq to identify super-enhancers for KS. The same frozen tumor tissue from the AIDS and Cancer Specimen Resource in Aim 1 will be processed. ChIP-seq will be performed using antibodies against BRD4, a host regulator of super- enhancer activity. The goal is to identify active super-enhancers. This will provide a map of key oncogene regulatory elements from actual cancer tissue and represents a transformative step beyond cell culture studies. The AREA Impact Strategy emphasizes student peer mentorship and leadership. This proposal will designate three students to serve as laboratory mentors and leaders. Along with the principal investigator, the peer leaders will coordinate experiments and data analysis with other students on the team. This intertwines mentorship training with the research experience and provides opportunities at multiple levels of engagement. The overall impact will be identification of potential drivers of KS. Elucidating regulatory architecture and circuitry will identify enhancers and transcription factors that may be essential for growth and potential targets in future treatments.

NSF Awards · FY 2025 · 2025-01

Millions of people across the developing world live and work in marginalized urban spaces with limited access to basic public goods. Often these same spaces are sites for organized criminal groups engaged in violent and illicit activities, including the sale and distribution of drugs. But relatively little is known about how the presence and operation of organized crime impact the ability of vulnerable urban populations to access critical public goods often in short supply in marginalized urban settings, including potable water, sanitation, and green space. Existing research shows that access to public goods bears a relation to levels of economic development and the stability of democratic political institutions. Moreover, these conditions have broader implications for phenomenon of global concern, including population movement and the flows of illicit goods. This study aims to advance knowledge of the relationship between organized crime and access to public goods. The study uses multiple methodologies and emphasizes the training of undergraduate students to gain experience collecting and analyzing different forms of data as part of carrying out social science research. This study asks: when and how do criminal organizations impact community-level political mobilization for public goods in socioeconomically marginalized settings? The investigator evaluates whether variation in the extent of competition among criminal groups can have distinct consequences for the incentives and capacity of communities to mobilize to demand public goods from states. The departure from the conventional scholarly focus on highly violent developing world cities promises both research and policy insights relevant for a broader range of urban settings. The study evaluates the generalizability of preliminary findings from a pilot study by deploying a multi-method research design: (a) collection and analysis of primary and secondary quantitative data at the neighborhood-level, including from government archives and media; (b) an original survey representative of a random sample of neighborhoods that vary in levels of criminal competition and collective political mobilization; and (c) fieldwork to collect qualitative data in select neighborhoods. The resulting study promises several broader impacts, including training undergraduate students to carry out quantitative and qualitative data collection and analysis, generating scholarly publications, and developing briefs with recommendations to be disseminated in convenings with civil society organizations and decision makers. 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 · 2024-09

Nonlinear optimal control solvers are used in a diverse range of applications from robotics to manufacturing and utilities. Unfortunately, current software infrastructure fundamentally limits the performance of many of these systems due to its inability to effectively scale to large-scale problems. At the same time, advances in parallel computational hardware have shown promise for addressing these limitations. This project overcomes such computational challenges through acceleration on Graphics Processing Units (GPUs), and develops more general, accessible, and documented open-source GPU optimal control libraries that can support a broader range of scientific research, with a focus in robotics. These libraries are paired with open-source benchmark problems and datasets and integrated into machine learning (ML) frameworks to enable fair evaluations of new algorithms and to enable broader participation in this interdisciplinary field. Finally, this project develops an integrated educational curriculum that provides background knowledge, enabling researchers and practitioners worldwide to learn about these topics, leverage this cyberinfrastructure to improve their systems, and contribute to the current project. This project addresses fundamental shortcomings in current software libraries for optimal control, which do not consider the use of GPU-acceleration, and are often not compatible and comparable with each other. As such, this work addresses critical scientific needs for low-latency optimal control at the edge as well as unified APIs and benchmark problems and datasets. This project expands upon and generalizes existing proof-of-concept, open-source GPU-accelerated optimal control solvers for robotics enabling them to be broadly used across both the robotics domain, as well as for other optimization tasks. This not only includes support for general purpose constraints and supporting kernels commonly found in robotics and beyond, but also wrappers in high level languages and integration with popular machine learning (ML) frameworks. Open-source benchmark problems and datasets as well as unified APIs are provided to: enable the optimal control community to fully and fairly evaluate novel algorithms and implementations; enable low-barriers to entry for contributions from researchers and practitioners worldwide; and avoid the current unnecessary development of bespoke libraries by individual research groups and organizations. The team evaluates this approach by tracking the number of projects, researchers, educators, unique applications, and subfields using, citing, and contributing to this software, courseware, and benchmarks. 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 · 2024-08

Gamma ray telescopes explore the Universe at the highest energies, offering a stunning view of cosmic phenomena that occur only under extreme physical conditions. Ground-based observatories such as VERITAS in southern Arizona and the upcoming Cherenkov Telescope Array Observatory are uniquely able to make detailed and sensitive observations of high-energy gamma-ray sources. This project focuses on the physics of active galaxies, cosmology, the study of blazar jets, counterparts of neutrinos detected by the IceCube Neutrino Observatory, and exploring very high energy Galactic accelerators. This award introduces undergraduate students to frontier research, trains graduate students and early-career postdoctoral scientists, and thus helps to create next-generation scientific leaders. The Barnard group will operate, and observe with, VERITAS, an imaging atmospheric Cherenkov telescope facility sensitive to gamma rays at energies above a hundred billion electron-volts. Research topics include the nature of PeVatron candidates and pulsar wind nebulae in the Galaxy. The group uses data from LHAASO, HAWC, VERITAS, Fermi-LAT, and X-ray satellites. Students and other junior researchers will carry out frontier research in particle astrophysics with a state-of-the art high-energy astrophysical facility. Group members are actively engaged in mentoring activities focused on underrepresented students This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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 · 2024-07

Much privacy research has taken an app-centric approach, narrowly focused on understanding user concerns, if any, with privacy risks of an app and alleviating the symptomatic evidence of the ailment (data leaked by a specific app). This project tackles the privacy ecosystem, an interlocking web of dataveillance that can encompass everything from credit card purchases to location history to communications metadata. The larger privacy ecosystems goes beyond the risks of particular apps and aims to mitigate the multiple privacy risks that threaten people, particularly vulnerable individuals who are both more at risk of privacy breaches and more harmed by their consequences. Health is a key context and domain in which a broad view of privacy is necessary. This project goes beyond app-centric views of health privacy and aims to examine vulnerable individuals’ privacy behaviors in the healthcare context. It develops and evaluates ways that these individuals can better protect themselves, as well as tools to help healthcare providers support their clients’ privacy. Through qualitative research that includes in-depth interviews and systematic analysis, this project is characterizing the understanding of risk experienced by vulnerable individuals in the context of their healthcare, taking into account the broad privacy ecosystem beyond individual apps. Qualitative research also is exploring the role of service providers, including librarians, social workers, teachers, and healthcare professionals. providing privacy management strategies. Through qualitative research that includes in-depth interviews and systematic analysis, this project is characterizing the understanding of risk experienced by vulnerable individuals in the context of their healthcare, taking into account the broad privacy ecosystem beyond individual apps. Qualitative research also is exploring the role of service providers, including librarians, social workers, teachers, and healthcare professionals in providing privacy management strategies. The second part of this research involves vulnerable populations, service providers, and privacy experts. Participatory design is employed to develop and evaluate a toolkit to support privacy ecosystem management. This research project is developing a freely available privacy toolkit designed for those who provide support and guidance to vulnerable individuals to help mitigate privacy harms. The project is contributing to understanding privacy risk and management for vulnerable individuals and provides a new frame for privacy and security researchers in the study of privacy protection for vulnerable communities. 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 2024 · 2018-08

PROJECT SUMMARY/ABSTRACT The retina is an adaptable circuit that is capable of rapidly changing its processing mode with changing stimulus conditions. This is an enormous undertaking because the retina has to process an unpredictable and wide-ranging assortment of inputs. An understanding of how local neuron dynamics are coordinated to produce and alter global network computations is needed for understanding how neural circuits of the brain are able to flexibly integrate information from many different areas. Advances on this front will have a broad impact on the ability to design targeted therapies to ameliorate brain disorders. The long-term goal of the proposed work is to understand how local adaptation mechanisms impact the computational function of neural circuits like the retina. Signals from the photoreceptors are processed along parallel pathways in the early part of the retina circuit and later combined. The properties of the visual inputs can modulate the how these inputs are processed via local adaptation mechanisms. This kind of adaptation has consequences for the manner in which visual inputs are encoded and processed in the early part of the visual system. The proposed research during the mentored phase aims to characterize the rod pathway adaptation that leads to a switch in computational processing of inputs and to determine the consequences for the spatiotemporal encoding of visual inputs. I will characterize adaptation through the rod-AII pathway for a range of luminance conditions to build a predictive model and to then determine how this adaptation affects the feature-sensitivity of ON α- ganglion cells under different luminance conditions. The independent phase research will build on the progress made during the mentored phase. There are several distinct ganglion cell types that convey information to the brain about particular visual features - similar to the higher order processing that takes place in the visual cortex. I will conduct studies to determine how ON and OFF α-ganglion cell types use the same circuitry to produce computations that are fundamentally distinct beyond their opposite polarities. The objective of this work is to obtain a better understanding of how mechanisms of local adaptation can reconfigure the computational functions of diverse circuit components. The University of Washington offers the ideal environment for my career development and for pursuing this highly interdisciplinary project. I have two outstanding mentors, an experimentalist and a theorist. I will receive training to become proficient in retina electrophysiology experiments and take courses to hone my quantitative skills. This training will prepare me to launch my own lab with an interdisciplinary focus. The proposed research will establish my expertise in the effects of biophysical mechanisms on sensory circuit computation.