University Of Chicago

NSF Awards · FY 2025 · 2025-05

For centuries, partial differential equations (PDE) have played a fundamental role in understanding physical and natural phenomena. Dispersive/wave equations model wave propagation phenomena which are ubiquitous in nature. They also describe the basic laws of quantum physics, which is one of the greatest achievements of the 20th century. This project studies fundamental questions about dispersive and wave equations by introducing ideas from probability theory. The results of the project will advance the mathematical theory of wave turbulence, which has important applications to plasma physics, nonlinear optics, and oceanography, and the analysis of Gibbs measures for Hamiltonian systems, which plays a key role in quantum field theory and statistical physics. Due to its scope and connections to physics and science, the project will also promote interdisciplinary interactions. As part of the project, the Principal Investigator (PI) is training junior researchers and contributes to maintaining the diversity in STEM disciplines at University of Southern California. This award supports work on five research projects (A-E). The first three projects are concerned with the mathematical theory of wave turbulence. In Project A, the PI extends the short kinetic time derivation of wave kinetic equation to longer kinetic times. This is a major step in the development of the theory, as it goes beyond the perturbative regime and will also shed light on the longstanding open problem of the long-time derivation of the Boltzmann equation. In Project B, the PI plans to generalize this derivation to cover the full range of conjectured scaling laws, which is physically well motivated and also leads to new mathematically interesting structures. New significant combinatorial structures and cancellations which are not present in the physics literature are expected to be discovered. Project C considers the wave turbulence problem for water waves, which has been studied since the 1960s by physicists. Mathematically, it is a quasilinear equation and substantial new ideas are required to obtain results similar to the ones available in the semilinear case. The last two projects concern Gibbs and other invariant measures in statistical physics and quantum field theory. Project D concerns the Gibbs measure for the 2D hyperbolic sine-Gordon equation, which is an important model that contains near-critical scenarios. Here, the goal is to further develop the random tensor theory introduced by the PI in earlier work. Project E investigates, through a combination of techniques from probability theory and integrable systems, the invariance of the white noise measure for the one-dimensional cubic nonlinear Schrödinger equation, which is critical but also completely integrable. 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

Quantum materials and devices have ushered in a new era of information processing based on superimposed states of zeroes and ones, bringing revolutionary changes to computing, communication, and sensing. However, the current manufacturing process for quantum materials and devices is inefficient and labor-intensive, lacking reproducibility and scalability. This Future Manufacturing Seed Grant (FMSG) project looks to address these challenges by developing self-learning robotic epitaxy, an artificial intelligence (AI) driven approach to manufacturing superconducting materials and quantum circuits with high precision and efficiency. By leveraging an innovative AI tool for decision making based on both the past and current actions of a system, robotic epitaxy looks to autonomously optimize fabrication parameters in real time, mimicking the operation of humans while eliminating human-induced errors. Additionally, the team will combine AI development with the equivalent of a 3D printer with the goal of manufacturing superconducting quantum circuits. This new technology looks to significantly reduce production costs and environmental impact. The knowledge gained from this research will help accelerate the development of quantum technologies, while also advancing workforce training at the intersection of quantum science, materials science, and AI-driven manufacturing. Manufacturing quantum materials and devices is key to the development of the quantum economy. However, the high-dimensional parameter space for quantum device fabrication and a strong device-to-device variation using the same recipe make it particularly challenging to deploy traditional AI methods. The goal of this research project is to drive two key innovations to enable self-learning robotic fabrication of quantum materials and devices. First, the team intends to develop wafer-scale robotic epitaxy. This technology will integrate structured reinforcement learning, an AI tool for decision making based on the trajectory of a dynamical system, with real-time electron diffraction data to guide the growth of high-temperature superconducting thin films. Second, the team looks to develop robotic mini-epitaxy, which enables direct printing of superconducting quantum circuits without nanofabrication. This setup will use nanoscale nozzles to confine molecular beams to sub-micrometer scales. Real-time optical imaging feedback intends to allow the reinforcement learning model to adaptively control deposition conditions, ensuring high-fidelity fabrication of superconducting devices. A primary target of this research is monolayer iron selenide superconductors, which serve as an ideal testbed due to their extreme sensitivity to growth conditions and their potential for high-performance quantum computing applications. Working to establish the first AI-driven “3D printer” for superconducting quantum circuitry will lay the foundation for scalable, automated quantum device manufacturing, seeding future growth of the research team and potentially having broad impacts on quantum computing, materials science, and AI-driven manufacturing. This project will also aim to establish an education program that uniquely integrates AI with quantum materials science, training a new generation of workforce with interdisciplinary expertise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-05

PROJECT SUMMARY Although the etiopathogenesis of Type 1 (T1D) and Type 2 diabetes (T2D) is different, decreased functional β-cell mass is a central feature in both diseases. Concerted research efforts aim at developing strategies to improve β-cell survival and restore β-cell function in patients with diabetes. Cellular senescence is a major hallmark of aging. Recent work has demonstrated that β-cell senescence is a common contributor to Type 1 diabetes (T1D) and Type 2 diabetes (T2D). RNA modifications are emerging as important modulators of many hallmarks of aging. We have discovered that m6A mRNA methylation is essential for β-cell function and survival, and recently characterized the m6A landscape of human T1D and T2D islets. Our preliminary data shows that the m6A eraser ALKBH5 is upregulated in aged human β-cells and differentially m6A methylated genes in T1D and T2D are enriched for DNA damage response and senescence pathways compared to controls. The goal of this application is to use state-of-the art methods to characterize the m6A methylome of the aging β- cell and identify m6A-regulated pathways that underlie the accelerated β-cell senescence in diabetes. I will test the overarching hypothesis that aging β-cells exhibit transcript-specific m6A hypomethylation and consequent upregulation of mRNAs involved in driving cell senescence, and that m6A is therefore a valuable therapeutic target for diabetes. In Specific Aim 1, I will characterize the temporal m6A landscape of the aging β-cell. Completion of Aim 1, which will take place during K99 phase, will provide me with training in aspects of aging biology and further experience with bioinformatics analysis of m6A data necessary to independently complete the R00 phase and future R01 submissions. In Specific Aim 2, which will span the K99 and R00 phases, I will identify the molecular targets of ALKBH5 in the human β-cell transition to senescence. Completion of the sub- aim of Aim 2 proposed during the K99 phase will provide me with the training in photoactivatable ribonucleoside- enhanced crosslinking and immunoprecipitation (PAR-CLIP) sequencing and data analysis necessary to complement Aim 1, and independently complete Aim 2 during the R00 phase. For Specific Aim 3, I will target m6A levels to reduce β-cell senescence and improve diabetes in vivo. Completion of this aim will allow me to validate candidate genes identified in Aim’s 1 and 2 and to test in vivo the role of Alkbh5 in driving the accelerated senescence seen in β-cells in diabetes. These experiments are novel as they combine omics-based approaches, mouse genetic models, physiology, and molecular biology to examine the mechanistic role of m6A, and particularly ALKBH5, in β-cell senescence transition in diabetes.

NSF Awards · FY 2025 · 2025-05

Geophysical gravity waves are a ubiquitous phenomenon in Earth’s atmosphere and ocean, made possible by the interaction of gravity with a stratified, or layered fluid. They are excited in the atmosphere when winds flow over mountains, by thunderstorms and other strong convective systems, and when winter storms intensify. Gravity waves play an important role in the momentum and energy balance of the atmosphere, with direct impacts on surface weather and climate through their effect on the variability of key features of the climate system such as the jet streams and stratospheric polar vortices. These waves present a challenge to weather and climate prediction: waves on scales of 100 meters to 100 kilometers can neither be systematically measured with conventional observational systems, nor properly resolved in global atmospheric models. As a result, these waves must be represented, or approximated, based on the resolved flow that can be directly simulated. Current representations of gravity waves are severely limited by computational necessity and the scarcity of observations, leading to inaccuracies or uncertainties in short term weather and long term climate predictions. The objective of this project is to leverage unprecedented observations from Loon high altitude balloons and use specialized high resolution computer simulations and machine learning techniques to develop accurate, data-informed representation of gravity waves. The outcomes of this project are expected to result in better weather and climate models, thus improving short term forecasts of weather extremes and long term climate change projections, which have substantial societal benefits. Furthermore, the project will support the training of 3 Ph.D. students, 4 postdocs, and 10 undergraduate summer researchers to work at the intersection of atmospheric dynamics, climate modeling, and data science, thus preparing the next generation of scientists for interdisciplinary careers. The project will deliver two key advances. First, it will open up a new data source to constrain gravity wave momentum transport in the atmosphere. Loon LLC has been launching super pressure balloons since 2013 to provide global internet coverage. Very high resolution position, temperature, and pressure observations (taken every 60 seconds) are available from thousands of flights. This provides an unprecedented source of high resolution observations to constrain gravity wave sources and propagation. The project will process the balloon measurements and, in concert with novel high resolution simulations, establish a publicly available dataset to open up a potentially transformational resource for observationally constrained assessment of gravity wave sources, propagation, and breaking. The second transformation will be using machine learning techniques to develop computationally feasible representations of momentum deposition by gravity waves. Current physics-based representations only account for vertical propagation of the waves (i.e., they are one dimensional) and ignore their horizontal propagation. Using the data based on the Loon measurements and high resolution models, one and three dimensional data driven representations will be developed to more accurately and efficiently represent the effects of gravity waves in weather and climate models. These novel representations will be implemented in idealized atmospheric models to study the role of gravity waves in the variability of the extratropical jet streams, the Quasi Biennial Oscillation (a slow variation of the winds in the tropical stratosphere) and the polar vortex of the winter stratosphere, enabling better understanding their response to increased atmospheric greenhouse gas concentrations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-05

PROJECT SUMMARY In the United States, it is estimated there are ~730,000 new cases of prostate cancer, lung cancer, colorectal cancer (CRC), and ovarian cancer annually. Advances in therapies during the past decades have significantly improved clinical outcomes. For many common cancers, however, treatment options are limited when the cancer is diagnosed with distant metastasis at later stages. Although early detection may reduce cancer mortality, systematic screening programs are available only for a limited number of cancers (e.g., CRC), which also faces challenges such as patient compliance and potential complications. Therefore, minimally-invasive blood-based tools that could detect these common cancers at earlier stages are likely to be paradigm-shifting in cancer control and care. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial offers a unique opportunity and resource to investigate the possibility of applying our established 5hmC-Seal assay to: 1) explore cancer-specific early epigenetic marks, specifically 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA), when the cancer is not “overt”; and 2) develop a multi-cancer detection (MCD) tool that is minimally-invasive and possesses high sensitivity and specificity. Current MCD tests, e.g., Grail’s blood test, are not suitable to exploit the PLCO samples (e.g., that contain very limited amounts of DNA). In contrast, the 5hmC-Seal assay, only requiring nanograms amounts of DNA, together with the next-generation sequencing (NGS) holds the promise to address the challenge as a highly sensitive approach for PLCO biospecimens. Of note, our compelling preliminary results demonstrated: 1) the feasibility of applying the 5hmC-Seal assay to PLCO biospecimens (~200 µL plasma/sample); 2) analysis of genome-wide 5hmC from PLCO samples successfully identified a 5hmC-based model that detected CRC cases months or even years prior to overt disease; 3) altered 5hmC signatures associated with “overt” cases, such as CRC, gastric cancer, lung cancer, liver cancer, and brain cancer showed the biomarker potential of 5hmC in cfDNA for early cancer detection; 4) genome-wide 5hmC shows tissue-specificity and is associated with tissue-specific gene expression; and 5) the possibility of expanding the 5hmC-Seal protocol to PLCO samples stored under different conditions. The primary goal of this U01 is to investigate early epigenetic signatures for common cancers, specifically Prostate Cancer, Lung Cancer, CRC, and Ovarian Cancer, which were targeted by the PLCO Trial. In Aim 1, we will train and validate individual models for common cancers in PLCO samples. In Aim 2, we will develop an MCD algorithm for identifying “pre-clinical” cases in PLCO samples. In Aim 3, we will evaluate the PLCO-trained algorithm for early detection of multiple cancers in prospectively collected samples. This U01 leverages the PLCO biospecimens and our clinical resources as well as our previous studies of biomarker discovery using our highly sensitive 5hmC-Seal assay to fill a critical gap in knowledge required to improve our understanding of epigenetic signals associated with early cancer detection.

NSF Awards · FY 2025 · 2025-05

This award funds a research project examining the factors influencing exchange rates, with a focus on international asset prices and capital flows. Understanding the drivers of exchange rates is a central objective of international macroeconomics and finance. Exchange rates are key prices affecting the relative cost of imports and exports, as well as the returns to investing in assets abroad. Yet exchange rates have long puzzled economists: they are inconsistent with benchmark models in economics and are very difficult to predict. This project makes progress on these puzzles by exploring the relationship between exchange rates and other international asset prices, such as bonds and stocks in different countries, as well as macroeconomic quantities, such as capital flows across borders. This research helps market participants use exchange rates to better understand macroeconomic conditions in the global economy. It also helps decision-makers better understand how monetary and fiscal policies affect exchange rates and thus the broader economy. The research findings could inspire innovative international policy frameworks, improve the well-being of households, businesses, and investors, and reinforce global market activity. This award project explores exchange rates through the lens of general equilibrium models with international capital market frictions. These capital market frictions are reflected in a central role for global arbitrageurs to intermediate capital flows across borders. Such models thus allow the researchers to accommodate classic forces that have been thought to drive exchange rates, such as the supply or demand for goods, as well as drivers that have been proposed in more recent research, such as changes in the intermediation capacity of global arbitrageurs. The project includes research to decompose the drivers of the dollar exchange rate versus other advanced economies through the lens of such a model. The co-movements of exchange rates with bond yields, spreads in financial markets, and capital flows suggest a nuanced variance decomposition of exchange rates: at high frequencies, shocks to intermediation capacity play an important role in driving exchange rates, but at low frequencies and overall, demand shocks play the most important role. The project includes further research to understand the sources of demand shocks driving variation in these prices and capital flows. The researchers study the co-movements of exchange rates, bonds, and stocks to differentiate between potential sources of demand shocks. The project also includes research to understand how heterogeneous exposures to demand or intermediation shocks can explain differences in exchange rate behavior across countries. The researchers study how patterns in trade linkages and in net foreign assets give rise to heterogeneous effects of these shocks. The project finally includes research on the propagation of macroeconomic policies through exchange rates. The researchers study whether state-dependence in the effects of foreign exchange rate intervention can account for mixed evidence on its effectiveness in the data. This research could help researchers and decision-makers gain deeper insights into exchange rates and their relationship with international asset prices, capital flows, and other macroeconomic factors, ultimately improving the well-being of the U.S. population and strengthening the country's leadership in international markets. 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

Self-assembled thin films formed at the air-liquid interface are critical to many biological and technological systems from lipid monolayers in eyes and ears to nanoparticle monolayers in electronic devices. Understanding how these monolayers respond to mechanical forces is important to regulate surface tension, a critical enabler of successful applications. As compressive loading increases, an experimentally tunable range of surface phenomena has been reported in monolayers including out-of-plane buckling and in-plane relaxation with distinctive surface morphologies. While the former was successfully studied using theories of thin elastic sheets, a unified mechanism that can explain and connect out-of-plane and in-plane instabilities remains elusive. This award supports fundamental research that will look to develop a general elastic framework with new theoretical and computational models validated against experimental data to unify various mechanical instabilities in highly compressed monolayers. The generated knowledge intends to inform new treatments for multiple syndromes, coatings for biomedical implants and drug deliveries, and materials for electronic devices. Furthermore, the project looks to provide STEM educational and research opportunities for trainees from multiple disciplines (physical and biological sciences) and levels (high school to postdoctoral) through research activities, interactive seminars on solid mechanics and computational modeling, and mentorship and outreach activities. Self-assembled monolayers exhibit rich phenomena of surface instabilities and solid-like phase evolutions. The overarching objective of this research is to understand out-of-plane folding and in-plane shear banding in monolayers, and their transition and correlation with monolayer structural components. Utilizing lipid monolayers as a model material system, the project seeks to develop a continuum-scale constitutive model to investigate compression-induced microstructural changes in which evolving transition between solid-like quasi-phases composed of folding and shear banding occurs. Finite element models of highly compressed monolayers will be developed, and the predicted surface morphologies will be analyzed and validated against data from Langmuir trough compression experiments. The research will also combine atomic force microscopy data (characterization of monolayer stiffness and topography) at varying monolayer compression levels with modeling. The outcomes look to elucidate the active response within the monolayer by connecting the various observed instability modes, thereby bridging the knowledge gap in building a descriptive theoretical elastic framework to explain the monolayer mechanical 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 2026 · 2025-05

Innate immune cells have classically been considered to have no immunological memory. However, recent studies have challenged this dogma by demonstrating that innate immune cells, particularly monocytes and macrophages, can mount long-term memory and resistance to reinfection. These observations led to the concept of "trained immunity" - a phenomenon in which innate immune cells such as monocytes/macrophages and NK cells develop a faster and more robust immune response upon secondary stimulations by either the same or an unrelated pathogen. Epigenetic reprogramming is thought to be central to the induction of trained immunity. Yet, there is still a lack of comprehensive mechanistic knowledge of what are the specific epigenetic determinants required to induce long-lasting trained immunity in humans. Using combined expertise in functional genomics, computational biology, human immunology, and infectious diseases, this project will address three outstanding questions in the field: What is that nature of the epigenetic changes induced by "trained immunity adjuvants" in human myeloid and lymphoid progenitor cells? What trained immunity-induced epigenetic changes can be transmitted from the bone marrow hematopoietic stem cells to their differentiated counterpart cells in humans? What are the genetic and molecular determinants of inter-individual variation in trained immunity? In Aim 1, we will use single-cell gene expression and chromatin accessibility profiling (as an overall mark of epigenetic remodeling) to measure cell type-specific epigenetic and transcriptional signatures at the level of progenitor cells in the bone marrow of individuals that have been vaccinated with Bacille Calmette-Guerin (BCG) alone, beta-glucan (trained immunity adjuvant), BCG + beta-glucan, or placebo (control group). In Aim 2, we will ask how the transcriptional, and epigenetic changes induced by vaccination/adjuvants impact the function of cells from both the myeloid and lymphoid lineages. We will do so both in vitro (re-stimulation of peripheral blood mononuclear cells) and in vivo (secondary immune challenge with BCG). In Aim 3, we will use a response quantitative trait loci (QTL) mapping approach to identify genetic and epigenetic factors associated with inter-individual variation in trained immunity. This work will yield unique insight into the molecular mechanism necessary for the induction of long-lasting trained immunity in humans. In addition, it will provide a roadmap of the genetic and epigenetic factors underlying inter-individual variation in the induction of trained immunity, a critical step towards understanding the basis of population variation in response to vaccines and other immune stimuli.

NIH Research Projects · FY 2025 · 2025-04

PROJECT SUMMARY/ABSTRACT While immunotherapy have benefited some cancer patients, a substantial proportion fails to respond. Lifestyle choices have been increasingly recognized as a crucial aspect influencing the effectiveness of cancer therapy. However, precise mechanisms through which lifestyle choices, especially diet, could impact patient outcomes remain unclear. This is mainly due to the diversity of the dietary components involved. To address this challenge, our lab compiled a proprietary blood nutrient library that provides a unique collection of circulating nutrients to perform high-throughput, comprehensive cell-based screenings with physiological relevance. Using our novel library, I conducted a co-culture screen to identify nutrient candidates that enhance T cell-mediated tumor cytotoxicity. My preliminary data, for the first time, indicate that dietary carotenoid zeaxanthin enhances T cell-mediated tumor cell killing in vitro and attenuates tumor growth potential in C57BL/6 mice bearing B16F10 melanoma cells in a CD8+ T cell-dependent manner. This proposal will test the central hypothesis that dietary nutrient zeaxanthin enhances anti-tumor immunity by reprogramming CD8+ T cell function. In Aim 1, I will demonstrate zeaxanthin’s translational potential with various preclinical models. I will utilize tumor-bearing mice to further assess the anti-tumor effects of zeaxanthin in immunogenic and poorly immunogenic models. I will evaluate zeaxanthin’s synergistic effect with immune checkpoint inhibitor therapy such as anti-PD-1 antibody treatment. I will also characterize the changes in immune cell populations and functional markers in tumors, spleens, and draining lymph nodes upon zeaxanthin administration. In Aim 2, I will elucidate the precise molecular mechanisms underlying zeaxanthin’s effect on CD8+ T cells using temporal, integrated mechanistic studies including kethoxal-assisted single-stranded DNA sequencing (KAS-seq), phospho-kinase antibody array, and RNA sequencing. I will determine zeaxanthin’s target in CD8+ T cells via in vitro knockdown and in vivo knockout models. The Chen lab has a proven track record of expertise in demonstrating signaling significance of blood chemicals with in-depth mechanistic studies and diverse mouse models. We foster close collaborations with Drs. Justin Kline (UChicago) and Chuan He (HHMI & UChicago), who are experts in immuno-oncology and advanced sequencing methods, respectively. Together, the support and resources available through this network provide an ideal training and research platform to conduct the proposed project. Overall, my proposal will employ cutting-edge approaches to demonstrate zeaxanthin’s impact on anti- tumor immunity in vivo and to elucidate molecular mechanisms underlying zeaxanthin-dependent changes in CD8+ T cell effector function. This proposal aims to gain insights on how lifestyle choices, particularly diet, impact T-cell based cancer immunotherapy and to propose novel strategies to improve patient outcomes.

NSF Awards · FY 2025 · 2025-04

Massive hot stars are the greatest sources of energy and new material in the Galaxy. A collaboration of astronomers at the Monterey Institute for Research in Astronomy (MIRA), Florida Gulf Coast University, The SETI Institute, the University of Maryland Baltimore County, and the University of Wisconsin Madison, along with their international partners aim to determine the interior structures of these stars by the application of a new technique: polarimetric asteroseismology. Seismic waves bounce around the interiors of stars disturbing their surfaces as if in a perpetual star-quake. The collaboration will make observations of this phenomenon in key stars in tandem with ground- and space-based telescopes (including the NASA TESS mission). A new network of the World's most sensitive polarimeters, spanning a third of the Earth will be used to detect the surface oscillations caused by these seismic waves. The team will also build on established code to develop sophisticated new computer models to interpret the multi-faceted data. College undergraduate and high school students, including some from traditionally under-represented groups, will assist with the project and gain their first hands-on experience of observational astronomy and modeling. Citizen scientists will also be involved, and the project will form part of MIRA's public education programs. A very extensive data set will allow the team to determine the interior structures of about 10 beta Cephei and Slowly Pulsating B-type stars in various stages of evolution. This will be enabled by a large-scale coordinated high-precision polarimetric observing campaign. To achieve the needed phase coverage, it will involve multiple observatories, all equipped with state-of-the-art PICSARR polarimeters. To obtain the necessary S/N and frequency resolution (which depends on temporal baseline) will require many thousands of new polarimetric observations spanning more than 2 years, matched to corresponding photometry and spectroscopy – including new and archival data. The observations will be followed by an intensive multi-part analysis involving sophisticated radiative transfer modeling. Integral to the work is the creation of a new software program that combines pulsating star and polarized radiative transfer codes to properly account for the significant effects of rotation. This program will make mode identification feasible using polarimetry in rapidly rotating stars. The results will enable stellar evolution models to be properly calibrated and extrapolated to the supernova stage. 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 award supports the Fourth International Symposium on the Infectious Diseases of Bats (‘BatID 2025’), an international conference which brings together researchers from the disparate fields of virology, immunology, biochemistry, ecology, physiology, and genetics to investigate the role of bats as unique pathogen hosts. Bats are natural reservoir hosts for several high profile emerging human pathogens—including SARS-related coronaviruses, the likely precursors to the COVID-19 pandemic—yet they demonstrate limited pathology upon infection with viruses that cause extreme disease in non-bat (including human) hosts. Studying the mechanisms by which bats avoid disease from infection offers opportunities to translate bat-inspired immunological approaches into human disease therapeutics. This meeting offers opportunities for early career trainees, particularly graduate students and postdoctoral scholars, to share ideas and research findings with experts across this wide-ranging and interdisciplinary field. The meeting also aims to facilitate conversation between bat ecologists and conservationists with those engaged in more molecular approaches to understanding bat infectious disease to reconcile bats’ roles as major pathogen reservoirs. BatID 2025 is organized around three major conference objectives: (1) to disseminate research on the unique role of bats as pathogen hosts, (2) to foster collaborations and expand the field of bat infectious disease research, and (3) to identify a priority future research agenda in the study of bat infectious diseases. The first objective highlights this meeting’s utility as a research-sharing forum that welcomes representatives from disparate disciplines, who may not closely follow research outputs from other fields. The second objective seeks to turn this idea exchange into action by fostering collaborations among attendees, this year with a particular emphasis on recruiting participants and speakers who have not previously attended this meeting. Finally, the third objective seeks to organize the community around future research goals through an open discussion at the end of the two-day program. As an output, the Conference Organizing Committee will produce a peer-reviewed ‘Conference Proceedings’ article (to be led by junior researcher attendees) that shares these goals with the broader scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This research studies how artificial intelligence surveillance shapes social work practices. The goal of the study is to contribute to an understanding of how artificial intelligence is re-shaping social service decisions and practices. The study focuses on the application of algorithms for the evaluation of fidelity to evidence-based practices through interactions between human workers and non-human actors. The findings of this research inform reforms in the use of surveillance technologies in social services to improve the interaction between child welfare workers and AI systems. Combining theories of governance, social work, and science and technology studies, through partnership with local child welfare organizations, the research is conducted by interviewing workers, observing their work, and collecting and analyzing documents to understand the conditions structuring, practices of, and stakes of child welfare organizations using artificial intelligence to meet new accountability standards. The findings contribute to understanding how to legally stipulate what counts as evidence of effectiveness and under what conditions to incentivize, regulate, and monitor artificial intelligence. For practitioners and administrators, findings elucidate how artificial intelligence shapes workers’ relationships with families and other workers, as well as the daily work of child welfare. 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

Measurements at high energy colliders are critical to understand the fundamental particle content of the universe. The Standard Model of particle physics provides the best known description of this content, but we know it is incomplete. Natural phenomena such as dark matter, as well as theoretical questions related to naturalness, strongly suggest the existence of undiscovered particles at or around scales probed by the Large Hadron Collider (LHC). This research program seeks to enable their discovery with novel search strategies and cutting-edge pixel detector development. The educational component of this program seeks to lay the foundation for the long-term future of high energy physics with outreach and workforce development. PI DiPetrillo’s group will work primarily on the ATLAS experiment at the LHC. Her group will target scenarios with long-lived particles, which decay a measurable distance from the collision point. Her group will use innovative trigger, reconstruction, and analysis techniques to access this challenging and uncharted territory in the Run 3 ATLAS dataset. To fully explore the TeV scale at the upcoming High Luminosity LHC, her group will also build and commission a new ATLAS pixel detector. In parallel, she will lay the groundwork for energy breakthroughs at future colliders by leading physics and pixel detector studies for a future Muon Collider. Finally, this program will pursue a coherent education program designed to inspire, educate, and train the next generation of STEM leaders, seed public interest and support for future colliders, and pave the way for future discoveries. PI DiPetrillo will incorporate a new science communication component into UChicago’s undergraduate physics curriculum and expand outreach efforts in the local community. 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

The goal of this Future Manufacturing Seed Grant (FMSG) project is to enable domestic lithium production by developing methods which can extract lithium ions from unconventional and dilute sources, such as oilfield-produced water, geothermal brines, or desalination brines. Using these dilute sources benefits society as they are not obtained from mining, which is resource intensive and environmentally unfriendly. The process developed in this project will use electricity to trap the lithium ions in a solid crystalline host material, called intercalation. This intercalation material will be tuned to selectively trap only lithium, excluding other ions present in the solution to create a high-quality lithium ion source. This project aims to encourage participation in STEM by outreach to students in grades 5-9 and recruiting undergraduate veterans for research internships. The education and outreach materials will be disseminated to the public via open-access websites and channels to make additional impact. This research will (i) unveil the Li selectivity limit based on the FePO4 host and electrochemical intercalation method, which can be translated to other critical element separation; (ii) generate new fundamental knowledge to optimize Li selectivity to major ions at high reaction rates through morphology, diffusion length, and defect control; (iii) develop 4D-STEM into a powerful tool to reveal atomic and nanoscopic level phase and defect features in inorganic materials, which the materials science community can adopt immediately; (iv) design integrated processes and workflow to realize electrochemical Li feedstock production from mining from dilute unconventional Li sources; (v) gain the capability to predict material degradation to guide further manufacturing investigation direction. This Future Manufacturing award is co-funded by the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences (MPS) and the Divisions of Chemical, Bioengineering, Environmental and Transport Systems (CBET) and Civil, Mechanical and Manufacturing Innovation (CMMI) in the Engineering Directorate (ENG). 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

Artificial Intelligence (AI) and Machine Learning (ML) have become crucial foundations for all phases of the scientific method, from hypothesis generation to publication. It is increasingly critical that all students, irrespective of science domain, be trained from an early stage in their career on AI and ML foundations and methods. Leveraging AI in science without strong foundations may lead to negative impacts such as amplifying errors, magnifying biases, and misleading conclusions. This Research Experiences for Undergraduates (REU) site will contribute to the advancement of knowledge with research into new AI techniques and application of these techniques to complex scientific problems. The four science focus areas—AI+Materials, AI+Earth, AI+Biology, and AI+Astrophysics—each have the potential for significant societal impacts: more efficient methods for developing batteries that are critical for storage and transportation; physics-based models for weather and climate to predict extreme weather events and improve climate predictions; modeling biological phenomena to understand complex interactions, predict health outcomes, and enable personalized medicine; and improving the use of cosmic probes to better understand the evolution of the universe. This project will host the AI+Science Summer Lab REU site at the University of Chicago. The site will expose 10 students per year to cutting-edge research at the intersection of AI and Science over an 8-week program. The AI+Science Summer Lab REU site will train the next generation of AI researchers and scientists, pairing students with mentors in both AI and science domains. It will provide the critical foundational skills in AI as well as the interdisciplinary grounding to apply these advancements to science. The site will include a rigorous and engaging program with weekly activities that are designed to educate students on the research process, teach skills to use throughout the program and beyond, and expose students to other areas of research. It will also build a strong cohort environment to help students overcome the many challenges faced during research, create excitement around research, spur collaborations, and build long-lasting relationships well beyond the end of the program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This project examines the importance of visualization and embodiment in understanding numbers. Specifically, it investigates a diagrammatic and gestural tradition central to medieval mathematics, culture, and daily life: the ancient tradition of finger-counting. For centuries, people depicted and performed numbers using the same powerful finger‑counting system which allowed counting to one million only using the hands. This in-depth study of the medieval finger-counting tradition contributes to the history of mathematics and computation. The project also engages broader audiences by creating an open-source database, developing an undergraduate course, and sharing findings at international conferences. The grant will support data collection and archival research. Over one hundred medieval manuscripts and early printed books will be examined and visual, textual, and material data will be collected. This work provide insights into premodern numeracy, embodied cognition, and knowledge transmission, contributing to science and technology studies, art history, and medieval studies. The project will illuminate how historical practices of visualization and embodiment shaped numerical cognition in the medieval period and will enhance our understanding of premodern numeracy and its implications for cognitive science and the history of technology. 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 In unprimed specific pathogen free and germ-free mice, there exists a population of αβ CD8+ T-cells that exhibit hallmarks of agonist ligand encounter, termed CD8 memory phenotype (CD8-MP) cells. These cells account for ~10% of the CD8+ T cell repertoire in adult mice, have a CD44hiCD122+ phenotype, display other memory markers shared by ‘true memory’ CD8+ T cells (CD8-TM) generated in response to pathogen challenge, and have incompletely defined functions. Previously, CD8-MP cells were thought to develop in the periphery due to subthreshold T-cell receptor (TCR) signaling to non-cognate self-antigen and in response to cytokine signaling. However, recent work from our lab demonstrated that CD8-MP differentiation is a robust and conserved TCR- instructed process driven by the recognition of agonist self-ligands in the thymus and consolidated in the periphery. Importantly, we also demonstrated that select CD8-MP-biased clones are recurrently enriched in oncogene-driven prostate tumors and express high densities of PD-1, suggesting a previously unappreciated role for CD8-MP cells in the immune response to cancer. The objective of this proposal is to define the immunological mechanisms driving the differentiation of CD8-MP cells and the functional potential imparted by this process by analyzing CD8-MP functional activity in settings of infection and cancer. We will test the hypothesis that the differentiation of CD8-MP cells is a two-step process, triggered by the recognition of self- pMHC-I antigen displayed by medullary thymic epithelial cells (mTECs), followed by sensing of MHC-I and IL- 15 in the periphery, which confer CD8-MP cells with a unique “non-cytolytic” functional profile. In Aim 1, we will define the antigen presenting cells and accessory signals directing the differentiation of CD8-MP cells at both a polyclonal and monoclonal level, utilizing our TCR “retrogenic” mouse pipeline and our previously identified naturally occurring CD8-MP biased clones. In Aim 2, we will identify CD8-naïve and CD8-MP-biased clones reactive to the same model antigen and utilize our TCRrg pipeline to interrogate the function of these cells following agonist ligand encounter in a tumor model and in other inflammatory contexts. Elucidating these mechanisms will provide needed insight into the biology of this prevalent T cell subset and will have key implications for our understanding of self-tolerance and cancer.

NIH Research Projects · FY 2026 · 2025-04

Project Abstract With the aging of the American population, the number of older adults at risk for developing cognitive impairment is staggering. Recent research points to age-related change in cognitive performance beginning as early as age 30, highlighting the potential for early interventions. Cognitive function has long been assessed using standardized cognitive tasks administered via neuropsychological evaluation. However, the traditional way to assess cognitive ability is time consuming, requires trained personnel, requires an office visit, and identifying decline among younger adults is particularly challenging because it can be masked by item redundancy effects. Here we propose developing a new computerized adaptive test (CAT) to assess cognitive function, either in clinic or remotely, that is based on recent advances in multidimensional item response theory (MIRT). We are calling it the CAT-COG. The CAT-COG will assess global cognitive ability as a primary domain as well as 5 cognitive subdomains: episodic memory, language/semantic memory, processing speed, attentional control/working memory, flexible cognition/reasoning. Our approach will revolutionize computerbased cognitive testing (ultimately in a platform independent way), providing precise estimation of an individual's ability on these domains with minimal respondent burden, using a sufficienUy large bank of items so that the same individual's cognitive ability can be assessed repeatedly without reusing items or stimuli. This project (resubmission) brings together an accomplished interdisciplinary team of researchers and also builds on the unique resources of the Rush Alzheimer's Disease Center (RADC). A portion of the original Aim 1 of the grant that involved development of a new 500 item bank of cognitive tasks, data collection, bifactor model calibration and simulated adaptive testing has now been independently funded by an NIA R56 award (2 years of what we proposed to complete in 3 years). These are the key remaining project steps: (1) We will expand our Ml RT-based model calibration to include a wide variety alternative IRT/MIRT models (bifactor, unrestricted MIRT, domain-specific IRT, TesUet and trifactor models) and select the best fitting model(s) for the final CATCOG. Note that different models may be used for the global and domain specific tests. (2) We will validate the CAT-COG among returning RADC participants who will also receive traditional neuropsychological testing. (3) We will study short-term variability of the CAT-COG to determine learning effects, develop a testing protocol that is immune to such effects, and assess test-retest reliability. (4) We will harmonize the CAT-COG with the RADC standard test battery so that existing data can be linked to newly collected CAT-COG assessments. (5) Assess differential item functioning to detect possible bias as a function of age, race, sex, and education.

NSF Awards · FY 2025 · 2025-03

While all humans have unique experiences and memories, surprisingly a lot of what is remembered is similar across people. For example, when seeing a group of people for the first time, some names and faces may easily stick in memory, while others fade away even after several meetings. Indeed, recent work has found that images, voices, and words have an inherent memorability—where some are more easily remembered than others, across people. This universality in memory means that one can make honed predictions of what people will remember, based on the events they are experiencing. Further, this universality suggests shared mechanisms across people that determines what information is saved into memory. However, it is still unknown what causes an item to be memorable—prior work suggests that it is not one singular factor (e.g., attractiveness) or a combination of factors, but instead something deeper about how the brain processes information. This project tests three major theories about what determines an item’s memorability, with the goal of answering how brains prioritize what should be saved in memory versus what should be discarded. Testing and understanding these theories makes memory more predictable—allowing for the creation of textbooks with easy-to-remember images, better diagnostic tests of Alzheimer’s disease, or art exhibits that leave a lasting impression. These three main theories will be tested using converging methods in behavioral experiments, brain imaging (functional magnetic resonance imaging or fMRI), and artificial intelligence (AI). The first theory poses that brains organize memories in maps, and memorability reflects where an item is in that map. In other words, memorable items may be in more central, accessible locations, while forgettable items are on the less accessible outskirts. This hypothesis is tested using computational models testing the structure of these maps and how they relate to actual memory. The second theory poses that memorable items are those that are easiest for the brain to process—in other words, if it is easy to process, it is easier to save in memory. This theory is tested by looking at measures of cognitive effort (using behavior and eye-tracking methods) and testing if AI models also show similar effects. Finally, the third theory poses that this “memorability effect” reflects an important computation of the brain that happens between perceiving information and saving it into memory. This hypothesis is tested using fMRI scans to see how the brain represents memorable items and how it changes with task, stimulus type, and familiarity. In addition to experiments in the laboratory, this project also tests memory theories in more real-world settings. This project is supported by the Perception, Action and Cognition Program and the Cognitive Neuroscience Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Non-technical abstract: Physics on a curved surface is different than in flat space. The physics of curved quantum materials is a particularly interesting case. For instance, current flowing along a curved surface is theorized to generate a force whose nature can reveal information about the host material. In this project, the research team aims to develop a new method for detecting the role of curvature induced in two-dimensional materials by placing them on top of nanoscale resonators. The curvature alters the stiffness, which can be measured experimentally. The success of this project will be to provide a general approach to controlling properties of noel materials based on their curvature. This project features a strong, combined scientific and educational component. Educational activities include the development of new modules in the undergraduate curriculum, building laboratories demonstrations that make some our proposed concepts tangible – for instance corrections to elasticity due to centrifugal forces. These demonstrations will be presented both at the undergraduate level within the University of Chicago and externally at popular science events. Technical abstract: Rapid technological advancements in quantum science are quickly finding use in other fields. This project adapts high-quality “trampoline” resonators pioneered for quantum experiments in optomechanics as a probe of forces in two-dimensional (2D) quantum materials by forming 2D-trampoline hybrid systems. In addition to being able to detect minute forces, 2D-trampoline hybrids promise access to previously invisible observables related to the interplay between in-plane electronic order and spatial curvature. Such exotic forces are not only interesting in their own right but perhaps provide a key general approach for identifying topological materials, which are important for metrology, classical electronics, and quantum computing. The technical approach is to place a target material on top of a trampoline resonator, and to precisely measure the mechanical motion. Curvature response of the target material will cause corrections to the mechanical eigenfrequencies. The project pursues a set of experimental goals with escalating technical demands and physical rewards. Initial experiments will measure the centrifugal force on supercurrent in a curved surface. These experiments naturally lead to exploration of the role of spatial curvature on superconducting vortices and will allow us to test predictions that vortices feel a repulsive force from regions of spatial curvature. Finally, we will integrate two-dimensional materials with our membranes to search for mechanical signatures of the Wen-Zee curvature coupling in quantum Hall systems. The end point is not only the demonstration of fascinating physical effects, but a new, general approach for exploring the interplay between geometry and topology in two-dimensional materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-03

PROJECT SUMMARY/ABSTRACT In 2022, more than 11 million Americans, half of whom were 50 or older, provided more than 18 billion hours of unpaid care for people with dementia. Many caregivers have no formal training and limited support. The White House, along with the Alzheimer's Association, the National Institute on Aging and others, is calling for urgent attention to the health and well-being of the fast-growing population of dementia caregivers, with heightened concern for caregivers living in historically marginalized communities. Scalable, evidence-based, solutions leveraging existing assets are urgently needed to meaningfully reach all caregivers. Our approach to addressing these unmet needs, CommunityRxDpeer, is an information technology-based, low-intensity, health system-initiated community resource navigation intervention delivered to caregivers by peer caregiver navigators remotely over time. Essential components include education about common social, including caregiving needs, activation of personalized community resource information and ongoing navigation-focused support. The CRxDpeer intervention components are informed by evidence-based “processes” identified in the Grey et al. Self-and Family Management Framework that are known to promote desirable health outcomes among people living or caring for others with chronic health conditions, including dementia. Prior CommunityRx trials have successfully deployed community members, clinicians and researchers to deliver the intervention in real-world and research settings with positive outcomes in a wide range of populations. In this pragmatic trial, experienced and willing dementia caregivers from the CRxDementia cohort (2020-24) will be recruited and trained as peer navigators to deliver CRxDpeer. Using a hybrid effectiveness implementation design with a double-blind RCT, we will evaluate the effectiveness of CRxDpeer versus usual care on caregiver health and well-being, healthcare utilization and social care outcomes. In parallel, we will evaluate the adoption, fidelity and cost of CRxDpeer and, using mixed methods, characterize perceived mechanisms of impact on caregivers delivering and receiving CRxDpeer. The specific objectives are to demonstrate that CRxDpeer can be delivered in the real world with fidelity and to assess the effectiveness of this approach on important outcomes. Resource referral and peer support IT platforms deployed for intervention delivery are already in commercial use, paving a viable path to replication and scale as a stand-alone or adjunct to other caregiver interventions, like the Center for Medicare & Medicaid Guiding an Improved Dementia Experience (GUIDE) Model test, which aims to enable people with dementia to age at home by supporting family caregivers with education and resources. CRxDpeer has the potential to improve the health and well-being of millions of dementia caregivers and their care recipients by meaningfully connecting them to vital social and caregiving resources and creating opportunity for willing and experienced caregivers to support with others

NIH Research Projects · FY 2026 · 2025-03

SUMMARY Small cell lung cancer (SCLC) is among the most lethal malignancies, noted for its pronounced metastatic potential across solid tumors, yet the underlying mechanisms of its metastasis remain largely unknown. In this project, we aim to deploy innovative methods and tools to delve into the longstanding enigmas of SCLC metastasis, leveraging advances in technology to gain clearer insights into this complex issue. We recently developed a method to generate functionally viable pulmonary neuroendocrine cells (PNECs) from human pluripotent stem cells (hPSCs). These cells, as the putative cell of origin of SCLC, can be transformed oncogenically by inducing mutations in key SCLC genes—RB1, TP53, and MYC—resulting in tumor formation in mice that closely resembles clinical SCLC. These modified PNECs effectively replicate the critical stages of SCLC, from tumor initiation to metastatic spread, providing a robust model to investigate the cellular and molecular drivers behind the disease’s aggressive metastasis. Our research strategy integrates hPSC-derived SCLC models, clinical samples, patient-derived xenografts (PDX), and state-of-the-art technologies such as single-cell sequencing and ATAC-seq. By examining cell behavior in both primary tumors and metastatic sites, we propose to uncover essential insights into the metastatic cellular and molecular drivers of SCLC. The outcomes of this research are anticipated to shed light on why SCLC aggressively metastasizes and could lead to novel diagnostic and therapeutic approaches for this formidable cancer.

NSF Awards · FY 2025 · 2025-03

This research program identifies the basic building blocks that give rise to effective decision-making including cultural variance in emerging decision-making strategies. A deeper understanding of children’s negotiation abilities is useful in thinking about how children solve problems and resolve conflicts. Moreover, it is arguably more productive to intervene in maladaptive decision-making strategies as they unfold in real-time rather than waiting until adulthood when such errors are harder to undo. Developmental and cross-cultural research in this space is useful for educators and policymakers as they engage in training the next generation of critical thinkers and strategic decision-makers. Indeed, this body of work can assist educators and policymakers in creating teaching methods and curricula that foster critical thinking and problem-solving skills from an early age. This research program explores the development of strategic decision-making. In everyday life, people often must decide how to handle situations in which they either share the same interests with others or have competing interests. In negotiations, the best outcomes are achieved when each person makes concessions on lower-priority interests to make gains on higher-priority interests. Little is known about how the conceptual skills that support successful negotiations develop in the first place and across societies. Across five empirical studies, the study has five overarching goals: (a) probe early markers of effective negotiations; (b) explore young humans’ capacity to engage in effective negotiations involving competing and overlapping interests; (c) observe how efficient agreements vary across age groups and regions; (d) examine dyadic real-time negotiation strategies, including personality factors that lead to efficient agreements; and (e) determine the synchrony (or delay) in one’s basic ability to make efficient agreements and its application in real-time negotiations. 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

Linguists often take for granted that language is organized into discrete syntactic units called sentences or clauses- either as main clauses or subordinate clauses, as in "Nathaniel suggests (that) Theodore eat the cookies," where ‘Nathaniel suggests (it)’ is the main clause, and ‘(that) Theodore eat the cookies’ is the subordinate clause. All languages seem to have these two categories, yet what distinguishes them remains nebulous. Further complicating the picture are instances of so-called "insubordination" in which a normally subordinate clause gains main-clause status; for example, when Theodore's mother exclaims: "(that) Theodore eat the cookies?! Absolutely not!" The existence of insubordination complicates the generalization that clauses can be categorized in a binary of main or subordinate. This doctoral dissertation project seeks to understand clause types and the main vs. subordinate dichotomy, using data from a language that exhibits a previously unstudied form of insubordination. The data gathered advances linguists' understanding of clausal syntax and semantics and is invaluable for language documentation and pedagogical purposes. In linguistic science, the characteristics of a main clause are often referred to as evidencing "finiteness," while the characteristics of a subordinate one evidence "nonfiniteness." This dissertation project investigates what syntactic or semantic realities underlie these characteristics. Previous work on these questions has focused almost entirely on well-known languages and has seldom taken into account the phenomenon of insubordination. This project remedies these gaps by creating and analyzing new data from an understudied language through elicitation sessions with native speakers. 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

Summary and Relevance of Proposed Research Humans have a remarkable capacity to learn to recognize and make decisions about incoming visual stimuli. This ability, which is disrupted by brain-based diseases and conditions such as Alzheimer’s disease, schizophrenia, stroke, and attention deficit disorder, is critical because it allows us to learn about the meaning of the stimuli that we encounter, and it enables us to make appropriate decisions. The neuronal computations which underlie rapid and flexible visually-based decisions involve interactions among neuronal populations both within and between brain regions spanning visual, cognitive, and motor areas. To understand how coordinated neuronal activity mediated decision related neuronal computations, this project employs a close interaction between experimental and theoretical modeling approaches. The experimental work employs large- scale neuronal population recording techniques to monitor the activity in three interconnected brain regions— posterior parietal cortex (PPC), frontal eye field (FEF), and superior colliculus (SC)—during visual decision making tasks. Experiments also employ reversible inactivation of each brain region to causally test hypotheses. The theoretical work develops novel theories of neuronal population function directly inspired by the experimental data and inactivation results, in order to determine the patterns of neuronal activity and interactions within and between regions which support computations underlying task performance. While much is known about how the brain processes visual features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meaning, or category, of stimuli. A greater understanding of visual categorization is critical for addressing many brain diseases and conditions (e.g. stroke, Alzheimer’s disease, attention deficit disorder, schizophrenia, and stroke) that leave patients impaired in everyday tasks that require visual learning, recognition and/or evaluating and responding appropriately to sensory information. The long-term goal of this project is to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction of school age children and young adults. Thus, a detailed understanding of the basic brain mechanisms of categorical decisions and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive and perceptual abilities.