Illinois Institute Of Technology

NSF Awards · FY 2026 · 2026-09

The project aims to advance the field of statistical change-point detection by developing novel methods and their associated theory to handle complex data with irregular signals. Unlike the traditional setting in which signals before and after the change point are often assumed to differ by a constant shift, irregular signals refer to the situation when the post-change signal may vary in highly unpredictable ways without any pre-specifiable pattern or structure. This can pose a tremendous challenge on many existing change-point tests, often resulting in notable reductions in their statistical power and increasing their vulnerability to maliciously designed adversarial attacks. By allowing the post-change signals to be irregular and not necessarily follow the standard assumptions as in conventional change-point analyses, the research developed in this project is expected to lead to more robust and next-generation statistical and machine learning protocols and toolboxes with rigorous theoretical guarantees for change-point detection in a wide range of applications. For example, detecting abrupt changes in power grids, attacks in sensor networks, or emerging trends in social networks all require powerful methods for detecting irregular changes. As a result, the research will advance not only the field of statistics but also a range of other disciplines including machine learning and artificial intelligence where data with irregular signals may arise. The research will also be integrated into the undergraduate and graduate education at participated institutions to equip students with advanced yet accessible statistical and machine learning knowledge for analyzing data with irregular signals. The research involves the development of novel statistical methods and their associated theory for change-point detection and estimation in the presence of potentially irregular signals. To quantity the uncertainty in the estimated signals from dependent and noisy data, a causal representation framework is employed with a suitably constructed functional dependence measure to quantify the effect of dependence via the technique of perturbation and innovation coupling. This enables the use of deep probabilistic tools, such as the invariance principle and Gaussian approximation results, for a general class of dependent processes to guide the selection of a statistically appropriate alarm threshold for detecting change points in the presence of irregular signals. The project aims to address change-point detection under irregular signals both in the offline setting, where the analysis is performed after all the data are collected, and in the online setting, where sequential testing becomes desirable as data arrive. In addition, different asymptotic schemes are considered to address situations in which stable historical data are available and when such data are not available to practitioners. The research is also expected to promote scientific and technological advances in applications that require rapid anomaly detection with complex alternatives. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Researchers who study human diseases or test new drugs often use microfluidic devices that contain embedded cells that mimic the behavior of specific organs. The usual approach is to make a change in the cells’ environment and observe changes in the health of the cells. This project will expand that approach by finding ways to control the health of the cells as their environment changes. The project will create an “organ-on-a-controller” system that controls the health and function of human liver cells called hepatocytes. The system will integrate three components: 1) miniature sensors that monitor multiple vital signs of the hepatocytes in real-time, such as protein production and metabolite levels; 2) a computer model that learns how the cells respond to different drugs or nutrients; and 3) an intelligent control system that uses this knowledge to automatically adjust the input to the cells so that a particular cellular health state and function can be achieved. This approach will keep cells healthy and will guide unhealthy cells from a diseased state, such as fatty liver disease, back toward a healthy one. The technology will create a powerful tool that can accelerate the discovery of safer and more effective drugs, advance personalized medicine, reduce the need for animal testing, and provide a deeper understanding of complex chronic diseases. Results will help advance new concepts in biotechnology and advanced biomanufacturing. A fundamental gap exists in our ability to dynamically control complex biological systems. Current in vitro microphysiological systems (“organs-on-chips”) are largely open-loop, precluding the active regulation of cellular function based on real-time feedback. This project aims to address this knowledge gap by creating a first-of-its-kind “organ-on-a-controller” platform that integrates multiplexed biosensing, predictive modeling, and adaptive closed-loop control to actively steer cellular function. Using primary human hepatocytes as a biologically relevant model system, this project will design an integrated microfluidic platform for the simultaneous, real-time measurement of key secreted factors and intracellular reporters of transcription factor activity. Our approach will provide a continuous, multi-parameter view of the cellular state with high temporal accuracy. Further, a library of predictive mathematical models (transfer functions) will be developed that describe the dynamic input-output relationships of hepatocytes in response to metabolic and inflammatory stimuli. A sophisticated model predictive control will be implemented and validated to actively maintain hepatocyte homeostasis under inflammatory challenge and steer cells from a disease state toward a healthy phenotype. By closing the loop between sensing and actuation, the platform will be inherently adaptive, learning from cellular responses to account for biological variability and perturbation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a very poor overall prognosis and limited treatment options. The absence of elevated expression of the three common receptors, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) that are validated targets of therapeutic drugs, poses significant challenges in treating TNBC. Better therapeutic options for efficient TNBC control and improved survival are urgently needed. In this investigation, we propose to develop antibody chelator conjugates (ACCs) for targeted radionuclide therapy of TNBC. The novel ACCs contain a monoclonal antibody with high affinity for a validated therapeutic target in TNBC and a chelating agent labeled with â..emitting therapeutic radionuclide (177Lu or 90Y). The efficacy of targeted radionuclide therapy against cancer has been demonstrated in numerous clinical trials with radiopharmaceuticals including 177Lu-based Lutathera®, 177Lu-based Pluvicto®, and 90Y-based Zevalin®. Chelation chemistry is an essential component in creating clinically viable radiotherapeutics with high therapeutic efficacy and safety profiles. The PI has invented highly effective chelators for 177Lu and 90Y in targeted radionuclide therapy. In this investigation, we aim to utilize superior chelation chemistry to create 177Lu- or 90Y-based ACCs for antibody-targeted therapy of TNBC. Promising ACCs will be identified and evaluated for radiolabeling with 177Lu or 90Y, and the corresponding 177Lu-ACCs and 90Y-ACCs will be evaluated for in vitro stability and radioimmunoassay. The top 177Lu-ACCs and 90Y-ACCs will be further evaluated for in vivo biodistribution, metabolism, radiation dosimetry, therapy, and toxicity using TNBC tumorbearing mice. The proposed in vivo studies are essential because data relevant to clinical procedures cannot be obtained using computational, invertebrate, or in vitro methods. This investigation is proposed to generate potent and safe 177Lu-ACCs and 90Y-ACCs as novel radiopharmaceuticals for TNBC Therapy.

NSF Awards · FY 2026 · 2026-01

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Illinois Institute of Technology. A total of 22 scholars pursuing bachelor's and master's degrees in artificial intelligence, applied mathematics, computer science, cybersecurity, data science, operations research, and statistics will receive scholarships averaging $15,000 for up to five years. Scholars will receive faculty and peer mentoring, and the project will build strong scholar cohorts through experiential and interdisciplinary learning experiences. Additional activities for scholars include community building and career advancement activities. The overall goal of this S-STEM Track 2 project is to increase STEM degree completion of academically talented, low-income undergraduate and graduate students with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in computing and other key areas of need. The project will be assessed by an experienced evaluator that will provide timely and adaptive feedback, and the data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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

Modern transportation systems generate massive amounts of data, including where and how vehicles and people move, traffic conditions, road conditions, and videos captured during actual trips. This includes detailed information about everyday driving behavior collected by cameras and sensors in cars and on roads. These datasets are essential for improving traffic safety, reducing congestion, and supporting the development of advanced technologies such as self-driving cars. However, they often contain sensitive personal details about individuals, making it difficult to share among traffic authorities, companies, and research institutions. This project addresses this challenge by developing secure methods for sharing transportation data while protecting individual privacy, serving the national interest by advancing transportation safety, supporting economic competitiveness in autonomous vehicle technologies, and strengthening infrastructure resilience through improved data-driven decision making. This project develops a comprehensive privacy-preserving platform for sharing diverse intelligent transportation systems data across different entities. The research targets multiple data types, including vehicle and road user information such as speed, travel times, and trajectories, as well as infrastructure data including traffic flow, control states, and videos. The project focuses particularly on naturalistic driving data collected by in-vehicle sensors and mobile devices. The research team will adapt and scale privacy-preserving techniques to support both centralized and distributed data-sharing models, ensuring secure data exchange without compromising individual privacy. The project will develop a web-based recommendation system to assist stakeholders in selecting appropriate privacy-preserving techniques for their specific datasets. Additionally, the team will create audit and compliance tools based on formal privacy guarantees and conduct user studies to ensure practical relevance. Secure cyberinfrastructure will be designed and deployed through collaboration with public and private partners. The platform will be evaluated using real-world transportation datasets to demonstrate effectiveness in enabling privacy-preserving data sharing that supports transportation research, improves traffic management, and accelerates development of data-driven mobility technologies. 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 June 23, 2025 memo from the Office of Science and Technology Policy highlighted the importance of collaborative science that integrates a wide range of expertise, methodologies, and perspectives across disciplines and sectors. Key parts of collaborations are the fundamental mentoring relationships between senior researchers and trainees that impact the conduct of research. This project will gather data to describe and create evidence-based approaches to support these relationships. Strengthening these relationships is important to help avoid research misconduct, support cutting edge science, improve science standards, and foster the competitiveness of the national STEM talent pool. This project employs mixed methods to investigate questions concerning the collaborations between advisors and trainees in laboratory settings to strengthen scientific training and research. In addition to gathering data on the basic practice of scientific research and its use of the apprenticeship model to educate new researchers, the project will use the gathered evidence to develop evidence-based professional development materials to improve this foundational relationship. The final products will include workshop lesson plans, case studies, and a guide for researchers and organizations to develop effective research environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Each year, foodborne pathogens cause hundreds of millions of illnesses and hundreds of thousands of deaths globally. These numbers in the U. S. alone amounts to 48,000,000 and 3,000 per year, respec�vely. The U. S. Food and Drug Administra�on (FDA) through its successful ini�a�ve, GenomeTrakr, has taken considerable steps towards modernizing our food safety system by implemen�ng and using whole-genome sequencing (WGS) in its inves�ga�ve and regulatory workflow. Establishment of an interna�onal laboratory network and the crea�on of the GenomeTrakr database are but two impressive accomplishments of this ini�a�ve. The GenomeTrakr laboratory network and its database are the products of a scien�fic vision that incorporates a global and data driven approach to the problem of foodborne illnesses. However, despite significant accomplishments of this ini�a�ve, a cri�cal gap remains in our knowledge about the environmental reservoirs where pathogens like E. coli, Salmonella, and Listeria reside and persist before contamina�ng the food supply. This in turn hampers our preven�ve efforts. Our proposed project directly addresses this gap and supports the FDA's Laboratory Flexible Funding Model (LFFM) Program by establishing and employing a dedicated capability for high-throughput sequencing (HTS) of the microbial communi�es and WGS of foodborne pathogens isolated from the posi�ve environmental samples (Track A5). The core scien�fic work, detailed in the A5 Microbiology Component, involves systema�c environmental surveillance across diverse agricultural and ecological landscapes in Illinois. Our proposal is designed to meet this condi�on and will generate three main types of data: a) the sequence data from microbial communi�es in the environmental samples; b) geographic, topographic, meteorological and anthropogenic metadata; and c) WGS data from the pathogens isolated from the posi�ve samples. To ensure consistency and replicability, we will adhere to current protocols issued by the FDA's GenomeTrakr Program Coordinators in all our laboratory work and in data dissemina�on and submission to the Na�onal Center for Biotechnology Informa�on (NCBI). By characterizing pathogen reservoirs and contribu�ng genomic data from underrepresented environmental sources, this project will improve our understanding of pathogen ecology, strengthen na�onal surveillance infrastructure, and ul�mately support the development of more effec�ve strategies to prevent foodborne disease and protect public health. The project leverages the unique resources and exper�se at the Ins�tute for Food Safety and Health (IFSH) at Illinois Ins�tute of Technology.

NSF Awards · FY 2025 · 2025-09

This CSSI project is a multi-university collaboration between Tennessee Technological University, the University of Tennessee, Knoxville, Stony Brook University, and the Illinois Institute of Technology. This project improves how massively parallel computers run large-scale artificial intelligence (AI) applications by enhancing the Message Passing Interface (MPI), a widely used standard for coordinating work across many computers in parallel programs. Currently, the enabling data-transfer software used in AI, for communication between computers enhanced by Graphical Processing Units (GPUs), are often proprietary and/or limited in scope; they cannot be expanded or enhanced by an open community. That situation restricts innovation, making it harder for scientists to collaborate and enhance their science output on limited computer resources, while also creating dependency on a few vendors. By contrast, this project builds on and advances Open MPI, a major open-source implementation of MPI with a long history of broad impact, to make it more efficient, flexible, and better suited for modern AI tasks. In addition to improving the Open MPI implementation, MPI4AI aims at standardizing extensions to MPI so all implementations and users of MPI will benefit from this project's outcome. MPI4AI introduces key improvements to Open MPI, including native support for GPU communication, enhanced collective (group) communication operations including those that are AI-algorithm specific, compute stream integration, and optimized data movement. Specifically, these advances target performance bottlenecks in three AI patterns: neural architecture search with transfer learning, key-value prefix caching in large language model inference, and large-scale data-parallel training. The project improves resilience and malleability through fault-tolerant mechanisms, enabling AI applications to adapt dynamically to system changes and to use resources more efficiently. By forwarding these enhancements toward adoption in the upcoming MPI-5 and MPI-6 standards, the project ensures long-term impact across both academic research and industrial AI workflows. These contributions will lower the cost of running large AI workloads and broaden access to scalable AI infrastructure. MPI4AI's capabilities will enable researchers exploring new modalities of AI computation to express their algorithms and code efficiently and more effectively as compared to existing solutions that work within the confines of current MPI features and vendor-specific message-passing libraries. Underlying improvements devised for Open MPI will also be broadly beneficial to other use cases and users of this parallel programming system. Overall, key strengths of this effort are a strong commitment to standardization and emphasis on performance-portability across various hardware platforms with particular focus on AI-enablement. 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

This award provides travel support for students and early career researchers to attend the 2025 SIAM Great Lakes Section Conference (GLSIAM 2025), to be held September 27-28, 2025, at the Illinois Institute of Technology in Chicago. The conference will bring together applied mathematicians and engineers from across the Midwest, fostering interdisciplinary collaboration and professional development. Emphasizing the value of a wide range of expertise in tackling complex scientific challenges, GLSIAM 2025 aims to strengthen regional research networks and promote partnerships with societal impact. The scientific program will explore the intersection of differential equations, computational mathematics, data science, and the physical and biological sciences. Highlights include plenary lectures by leading experts, a poster session, and parallel contributed talks. A key focus of the conference is to increase the visibility of early-career participants, connecting them with peers, mentors, and future collaborators. NSF support will help ensure broad and equitable access to this opportunity. The event will be open to anyone interested in the conference themes. More information can be found through the conference website, https://sites.google.com/iit.edu/glsiam2025/, with updates beginning June 2025. 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

Stellar evolution theory was one of the major accomplishments of 20th century astrophysics. However, the resulting models generally apply only to single stars. Stars in binary or higher-order stellar systems can evolve completely differently, because the two stars in a binary system can interact as they evolve, often merging or transferring mass between the two components. A researcher at the Illinois Institute of Technology (IIT) will search for such systems in 47 open star clusters using the Gaia space telescope and the Vera C. Rubin Observatory. Open clusters are ideal for this project, because their stellar constituents share the same age, initial chemical composition, and distance, making post-interaction stars easier to identify. The researcher will determine critical stellar and orbital parameters for these binary systems. The project will provide STEM training for a graduate and summer undergraduate students and also a public outreach program that conducts public daytime observing events on IIT’s campus and at local K-12 schools using a dedicated solar telescope. The graduate and undergraduate students will participate in the outreach activities, providing opportunities for them to develop science communication skills, apply knowledge from their coursework to setting up telescopes and discussing astronomical phenomena, and grow their confidence as developing scientists. The project aims to dramatically expand the sample of star clusters that have well-characterized populations of blue straggler, blue lurker, and yellow straggler stars, all of which are expected to have formed via mass transfer or mergers in binary systems. Blue and yellow stragglers are more massive than typical for the cluster age and can be identified by their anomalous positions in the cluster color-magnitude diagram (CMD), whereas blue lurkers are found on the cluster main sequence and identified by their unusually rapid rotation. The PI will use Gaia photometry to determine membership and construct CMDs of the 47 target open clusters and identify blue and yellow stragglers in each. Blue lurkers will be identified by constructing light curves of FGK stars and Fourier transform them to detect periodic signals indicative of rotation periods. The rotation periods will then be compared to predicted periods from gyrochronology models. The PI and students will use a custom implementation of the Bayesian isochrone fitting software called BASE-9, which allows them to flag blue stragglers, yellow stragglers, or other post-interaction stars in a cluster. These methods will then be applied to Rubin photometry, which will require updating of the BASE-9 code. The project will result in a catalog that is the largest census of post-interaction binaries in clusters to include well-characterized stellar and orbital parameters. 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

This project is using artificial intelligence (AI) to better understand how metals are used by cyanobacteria, the most abundant group of organisms to have ever existed in Earth history. Metals are essential for life and are held tightly by proteins inside of cyanobacterial cells. AlphaFold is a powerful AI tool that can predict the shapes of these proteins and how they hold onto metals. So far, these predictions have not yet been tested with laboratory experiments. In this study, college students are growing cyanobacteria in the laboratory and using advanced X-ray techniques to see how metals are bound inside the cells. By comparing the AI predictions with real data, the team hopes to better understand how metals move through environments when these cells die and break apart. This knowledge will provide a better picture of how dissolved metals are recycled in aquatic ecosystems. The project also includes outreach to schools and communities, including science activities for children, story-writing contests, and support for college students to get involved in science. The laboratory studies focus on resolving the chemical speciation of Zn and Fe in cyanobacteria. Experiments are performed by the researchers to assess whether AI-predicted metal-ligand binding environments reflect the actual speciation of Zn and Fe in living cells. Marine and freshwater cyanobacteria are being cultured under metal-controlled conditions, and proteins expressed under different growth phases are being identified using LC-MS/MS proteomics. The three-dimensional structures of the proteins will then be modeled using AlphaFold, and the protein structures will be annotated to identify putative metal-binding sites, coordination numbers, and ligand identities. These predictions will be experimentally tested using High Energy Resolution Fluorescence Detection (HERFD) X-ray absorption spectroscopy at the Zn and Fe K-edges, conducted at the Advanced Photon Source. Spectral data will be analyzed using linear combination fitting and principal component analysis to quantify the distribution of metals among cysteine, histidine, and carboxyl ligands. It is anticipated that AI predictions will correlate with experimental data, particularly in conserved protein families. These findings will provide mechanistic insights into metal-ligand complexation in cyanobacteria and establish a framework for AI-enabled investigations of metal cycling and biogeochemistry in natural aquatic systems. 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 · 2025-08

Modern computers run applications in "containers", software units that contain the application code and all the additional software that the code requires to run. To manage these containerized applications, sophisticated platforms such as Kubernetes have become crucial for ensuring scalability, reliability, and cost efficiency. However, tuning these platforms for optimal performance is a highly complex task, given their large configuration spaces and subtle parameter interactions. This project addresses these challenges by developing Cosimo, a co-simulation-based optimization framework that integrates real-world orchestration platforms with simulated containerized applications. By enabling low-overhead, high-fidelity experiments without the need for costly physical testbeds, Cosimo empowers researchers and practitioners to optimize orchestration strategies in a scalable, efficient, and cost-effective way. The broader impacts of the project include advancing cloud computing practices, improving resource efficiency in both scientific and industrial cloud environments, and contributing to workforce development through integration into graduate education, open-source dissemination, and public outreach. The project introduces Cosimo as a transformative tool for understanding and improving container orchestration at scale. It involves three main research thrusts: (1) developing a co-simulation framework that models critical orchestration features such as autoscaling, load balancing, and failure recovery by coupling existing simulators with live platforms; (2) designing a multi-objective optimization engine that supports hybrid, adaptive algorithms, including meta-learning and performance aggregation techniques to explore complex configuration spaces efficiently; and (3) validating the framework through empirical benchmarking and comparison against real-world systems to ensure accuracy and practical relevance. Together, these contributions provide a robust foundation for systematic evaluation and tuning of orchestration strategies, facilitating future innovations in cloud infrastructure management and performance engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This research will advance the state-of-the-art of industrial material removal processes for high-temperature refractory metals through a recently uncovered chemical effect (local embrittlement) in surface plasticity, referred to as Organic Monolayer Embrittlement (OME), arising from nanoscale organic films. It is well known that high-strength metal alloys, e.g., hard steels, are difficult to machine. What is much less well recognized is that relatively soft, refractory metals like tantalum and niobium are equally challenging to cut, grind and comminute, with high forces and surface quality problems, earning them the moniker "gummy." The gummy behavior is due to the high malleability of these metals, with non-homogeneous deformation and intense energy dissipation. This award supports research that seeks to solve the gumminess challenge via scientific understanding of the nanoscale OME phenomenon and its implementation in manufacturing processes. The research project will test the hypothesis that if the gumminess can be eliminated by local embrittlement, using benign organic media that induce a surface stress in the metal, then material removal will occur by fracture, with low forces/energy, improved surface quality and increased productivity. A suite of high-performance chemomechanical manufacturing processes should emerge, advancing refractory metal applications in areas including aerospace, hypersonics, nuclear energy and electronics. Complementing the research is an education program involving undergraduate researchers in creating a video gallery of plastic flow and fracture phenomena for manufacturing, and scientific collaborations with companies and universities. The research combining high-speed in situ observations of deformation, chemistry/material interactions and surface science will explain how nanoscale organic films influence (a) large-strain deformation and material removal in refractory metals via surface stress and (b) forces, deformation, and fracture, which are all manifest at the macroscale. A fully instrumented plane-strain cutting system will impose controlled large-strain deformation typical of material removal processes. The basis of the chemical effect in plasticity will be established by (a) integrating surface molecular probes and high-resolution in situ imaging of deformation, with ex situ materials characterization, (b) multiscale modeling of materials behavior and chemical effects in plasticity/fracture, and (c) characterizing media effects on process attributes such as forces, energy, and workpiece surface quality (finish, metallurgy). The study will investigate model material systems, including tantalum and nickel alloys, selected for their deformation response and technological interest. The findings will impact areas such as manufacturing, wear, and environmentally assisted cracking, wherein interactive effects of chemistry, plasticity and fracture often play a key role. 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 · 2025-07

In this project we aim to develop deep-learning anthropomorphic model observers (AMO) as a substitute for human observers (HO) in studies aimed to assess image quality, defined in a task- based fashion based on clinical diagnostic performance. An accurate AMO would allow fast and clinically relevant procedure for optimization of imaging system and algorithm designs. The proposed AMO methods aim to achieve good generalization, i.e., an AMO developed for images reconstructed by one algorithm (or scanner setup) should predict HO performance accurately for a different reconstruction algorithm (or different scanner setting). In other words, a desired AMO should be tolerant to domain shifts. It is further proposed to design AMO which is aware of its own limitations and type of images (or organs) it can be used on, so that it does not attempt to evaluate images it is unsuited to judge. To this end, we propose to augment the AMO with the capability of domain-awareness to recognize image domain shift, and to provide a mechanism that can be used to economize the use of HO studies by more efficiently adapting the AMO to newly available datasets This project will also aim to overcome common concerns about deep learning being a “black- box” by leveraging methods from visual psychophysics, such as reverse-correlation. The advantage of this approach is that the identical procedure had been applied to HO, thus allowing a direct comparison and interpretation of the resulting receptive fields. To accurately train, test and clinically validate AMO we will develop a rich, image data sets, annotated by experts and non-experts, mimicking CT imaging of the liver. Therefore, a key component of the project will be development of extensive annotated datasets ranging from numerical simulation, acquisition from 3D printed phantoms to clinical patient data. For image acquisition we will use simulated, virtual CT and clinical (physical) CT scanning followed by several reconstruction methods. We choose to use liver CT imaging as the development platform for AMOs in low-contrast lesion localization and discrimination tasks. The chosen application is significant by itself because, despite years of research, there is still no systematic framework for optimization of the many factors in imaging system design for clinical diagnostic tasks. We believe that the proposed domain-aware AMO approach and AMO interpretability methodology will have a substantial and lasting effect not only on the methodology of AMO development and use, but also on general deep-learning applications in medical imaging.

NSF Awards · FY 2025 · 2025-07

Cloud outsourcing has become critical for computing vast amounts of data for emerging applications, such as machine learning, bio-medical analysis, private information retrieval, etc. However, sharing data to the cloud may leak sensitive information, leading to various societal issues, including identity theft, financial loss, reputational damage, and legal consequences. Fully homomorphic encryption (FHE) is a post-quantum cryptography framework that supports computations directly on encrypted data, providing strong protection for sensitive data that remains encrypted during data transmission and cloud services. Despite the benefits, adopting FHE in real-world applications is challenging in (1) transforming application data and operations into encrypted ciphertexts and operations with restricted formats using various complex algorithmic schemes and (2) porting and optimizing on different computing hardware platforms that are critical for ensuring a practical runtime. The project's broader significance and importance are: (1) boosting the development and development of FHE to advance the national welfare by protecting data privacy in broader domains, and (2) promoting the progress of computer science and engineering to improve the availability and performance of privacy-preserving data sharing and analytics. This project develops a novel programming and compilation framework for exploiting FHE in general privacy-preserving applications on real systems. This project's novelties are (1) a new domain-specific language that improves the programmability of general FHE applications with different algorithmic schemes and (2) an optimized compiler infrastructure to generate high-performance FHE programs on various emerging hardware platforms. This project combines concepts and techniques at different levels in the full system stack of privacy-preserving computing, including privacy-preserving applications, programming languages, and computer systems. The proposed research aims to provide an innovative end-to-end solution that drives fundamental advancements in various aspects of privacy-preserving computing, aiming to proliferate the adoption of practical privacy-preserving computing in broader areas. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award provides funding to support about 10 students enrolled in PhD or masters programs in U.S. educational institutions to present their accepted papers and posters and attend the Doctoral Consortium associated with the 2025 International Conference on User Modeling, Adaptation and Personalization (UMAP 2025). User modeling is an increasingly valuable method for enhancing the effectiveness and usability of software. Applications of user modeling include recommender systems, adaptive educational systems, human-robot interaction, and many more domains. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. Active participation of young researchers in this conference is very important for the health of the field and for the researchers themselves. Thus, by bringing young and creative researchers to the 2025 UMAP conference, the requested funds will help advance an important and socially valuable research field. Supporting student travel to the UMAP doctoral consortium will help train advanced professionals in Science, Technology, Engineering, and Mathematics (STEM). This funding is critical because attending conferences is expensive and students do not have other means of support. The application and selection process is designed to create a cohort of students from a wide range of disciplinary, institutional, and topical backgrounds that have promising research trajectories related to the key themes of the conference. This cohort-building, combined with the connections students will make with doctoral consortium mentors and other conference attendees, will provide valuable academic and personal support toward advancing their careers, and the UMAP community as a whole. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Hierarchical classification is the task of categorizing items belonging to a structured hierarchy. Examples of this task include tracking of animals for monitoring variability in species and pests in precision agriculture. Reliable automation of hierarchical classification has the potential to accelerate sustainability efforts and boost agricultural productivity. Machine learning is increasingly being used as an automation tool in ecology and agriculture. However, to fully realize the potential benefits of machine learning in hierarchical classification, principled methodologies are needed to ensure the trustworthiness and robustness of the models used in these applications. This research will develop methodologies for machine learning models to accurately report their uncertainty about predictions and to defer to human experts in a principled, resource-aware manner. Data scarcity and low-quality labels often hinder the effectiveness of machine learning systems. To address these challenges, this research will also develop theory and methodologies to overcome the constraints of scarce and low-quality data, both of which are common in hierarchical classification tasks in practice. The project will develop isotonic regression methods tailored for uncertainty quantification in hierarchical classification. Moreover, it will design efficient algorithms for learning-to-defer in hierarchical classification. It will also develop hierarchical few-shot learning techniques to address data scarcity and robust algorithms for handling hierarchical label noise. Collectively, these efforts aim to establish principled methodologies that ensure the trustworthiness and robustness of hierarchical classification systems, enabling their effective use in mission-critical applications constrained by scarce, low-quality data. 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 will support student travel to the ACM Hypertext Conference (ACM HT) pre-conference summer school, to be held in September 2025. ACM HT is a leading venue for high quality peer-reviewed research on hypertext theory, systems, applications, publishing, artwork, and related practices. The summer school program is designed to connect senior researchers with up and coming students through interactive group sessions and one-on-one research and career mentoring activities. Bringing young researchers from a variety of disciplines to the summer school program will both help them develop their own research and careers and benefit the hypertext research field as a whole. This grant will provide travel support to about 10 U.S.-based students who otherwise have limited travel funding and so might not be able to attend. The committee will widely advertise the availability of travel funding to solicit applications from students of a range of institutional, disciplinary, topical, and personal backgrounds. Criteria for selection include fit to the conference topics, ability to both benefit from and provide value to the summer school, and financial need. Students' institutions will also be encouraged to provide matching funds to increase the impact of the award and support more students. 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 · 2024-11

This award will support student attendance at the 2025 version of the Intelligent User Interfaces (IUI) conference to be held in Cagliary, Italy. IUI is the main international conference where researchers in human computer interaction (HCI) and artificial intelligence (AI) present work on systems that use AI to help people be more effective in interacting with computers. IUI research spans a wide range of socially important topics and domains, including recommender systems, adaptive educational systems, accessibility, autonomous systems, creativity, virtual agents, privacy and security of intelligent interfaces, and many others. Providing travel funding that allows junior researchers to present their own work and be enriched by others' work will strengthen the IUI community and provide valuable benefits for the students involved. The funding will support around 14 students to attend the conference. Most of these students will also attend a Doctoral Consortium at which later-stage PhD students will present and get feedback on their dissertation research from both senior researchers and other PhD students in the IUI community. The availability of travel funding will be widely announced in the relevant communities, with students being invited to submit applications describing their doctoral work. These applications will be reviewed by a program committee that gives substantive feedback for every application, whether accepted or not. Acceptance decisions will be made with an eye toward topical, institutional, and demographic diversity of participants, while also considering financial need. Chosen students will attend a day-long doctoral consortium, have additional opportunities to meet with individual mentors, and be invited to present in a poster session for the conference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The transformation of the news ecosystem from traditional print media to online platforms has fundamentally changed how people engage with current events. This decentralization of media has arguably resulted in greater access to diverse and timely information, but it has also led to growing concerns about unreliable information, polarization, and the role that computer-mediated communication plays in fostering these phenomena. This project advances the understanding of how people interact with news online and how their behaviors evolve over time. By analyzing how people share, support, or criticize news on social media, this research identifies different stages of news engagement, in terms of the types and tone of news people interact with. Understanding these progression stages and the factors that influence them will provide insights into designing online platforms with healthy news ecosystems, reducing the spread of unreliable information while maintaining information diversity. The project will integrate findings into university courses and community workshops, ultimately fostering a more informed public. To meet these goals, this project advances computational models of online news engagement through three main research thrusts. First, it develops models to identify various types of news engagement behaviors and their progression stages, innovating advanced language and user modeling techniques to predict future behavior patterns. Second, it establishes a technical framework for estimating causal relationships between different news engagement behaviors, combining natural language processing with causal inference methods to estimate treatment effects from observational data. Third, the project tests socio-technical hypotheses regarding strong positions on issues, trust, and information reliability using this framework. The research employs multi-year, publicly available data from online social media platforms, enriched with databases of political news sources. Evaluation methods include machine learning metrics, semi-synthetic experiments, and validation and verification through surveys and focus groups. This comprehensive approach will produce more accurate predictive models and robust causal estimation methods applicable across various domains to study human behavior from online data. 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

Complex fluids are ubiquitous in daily life. Examples include shampoo, biological fluids like blood, ionic solutions in batteries, and liquid crystals used for display devices. These are fluids with microscopic structures, such as the orientational order of rod-like molecules, the elasticity of deformable particles, and interactions between charged ions. Due to the coupling and competition among various thermo-chemo-mechanical mechanisms on different spatial-temporal scales, complex fluids exhibit a variety of interesting phenomena and properties not encountered in simple fluids or gases. Mathematical models and computer simulations are two indispensable tools in studying complex fluids. Variational principles, such as the energetic variational approaches (EnVarA), provide unified and thermodynamically consistent frameworks to model various complex fluids through their energy and dissipation. In this project we will address the computational challenges for variational models for various complex fluids by developing new computational tools. The project will provide education and training to graduate and undergraduate students, along with postdoctoral associates, including those from underrepresented groups, in the fields of physical and biological modeling, scientific computing, and numerical analysis. Students will participate in the proposed numerical and experimental activities, and acquire a wide range of knowledge and skills from close interaction within the interdisciplinary team involved in the project. The goal of this proposal is to develop structure-preserving, high-order, efficient numerical methods for various complex fluid models, particularly those involving thermo-chemo-mechanical coupling. Rather than relying on partial differential equations, this approach builds numerical discretizations directly from the continuous energetic variational formulations, which describe all physics and assumptions in the system. The "discretize-then-variation" approach ensures that the variational structure, as well as the kinematics of thermodynamic variables, are preserved at the semi-discrete level. Different spatial discretizations, such as Eulerian and Lagrangian approaches, will be utilized based on the continuous variational formulation. The investigators will focus on three major research tasks, targeting different prototype models with increased complexity: (1) Developing high-order variational Lagrangian schemes for generalized diffusions; (2) Developing variational operator splitting schemes for reaction-diffusion models by incorporating Eulerian schemes for chemical reactions with Lagrangian schemes for diffusions; (3) Developing entropy-stable schemes for non-isothermal reactive flows, which will address the challenges in preserving thermodynamic consistency and entropy stability in non-isothermal models. Comprehensive numerical analysis and extensive computational studies of the numerical schemes will be conducted for each research task. 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

In today's data-driven world, many complex systems can be represented as interconnected networks or graphs. This project aims to develop new methods for analyzing, generating, and optimizing these graph structures, with potential applications in areas such as social network analysis and molecular design. By improving the ability to learn from and work with graph-structured data, the project is expected to provide new tools for researchers across various scientific fields. The proposed research contributes to advancements in areas such as drug discovery, network analysis, and modeling of physical systems, offering new ways to approach complex problems in these domains. This project also offers research training opportunities for undergraduate and graduate students. The project focuses on four main research areas: (1) developing more expressive and efficient graph neural networks, (2) creating improved generative models for graphs, (3) applying graph learning techniques to optimization problems, and (4) exploring the use of graph neural networks for discovering physical relations. The interconnected research thrusts aim to improve the capabilities of machine learning models based on graphs, laying the groundwork for solving complex graph-related challenges. The project will produce new mathematical and statistical tools, theoretical frameworks, and assessment methods for learning from graphs. The work is expected to advance graph learning techniques and their applications in scientific fields, providing researchers with new ways to handle data structured as graphs. 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

This project concerns the fundamental mechanisms underpinning collective behavior of large groups of agents, such as flocks of birds, schools of fish, or swarms of bacteria. Mathematical models for these phenomena offer insights into how large-scale structures emerge from small-scale interactions in physical systems, with potential applications in technology, including in computer graphics. In order to efficiently study systems with an otherwise intractable number of agents, this project will focus on the "effective" large-scale dynamics rather than on individual trajectories. Taking this perspective brings the problems of interest into the realm of partial differential equations. The models that arise in these problems bear substantial resemblance to equations found in fluid dynamics and continuum mechanics, a connection that will be leveraged extensively in the research to be carried out. The mentorship, training, and professional development of students and junior researchers will also be a key goal of the project. The proposed analysis will center on the effects of a nonlocal velocity alignment mechanism in isolation, as manifested in the class of hydrodynamic equations known as Euler Alignment systems. The PI will investigate the consequences of imposing different communication rules, especially as they relate to the large-time structure and regularity of the density profile. Emphasis will be placed on the as-of-yet poorly understood transition between qualitatively different regimes of interactions. In particular, the PI will leverage the additional structure available in settings with simple geometries to draw connections between models that incorporate strongly localized alignment and those that feature sticky particles. The PDEs governing alignment dynamics serve as a paradigm for more general nonlocal equations, and the proposed research has the potential to advance the understanding of classes of nonlocal models far beyond those explicitly studied in the project. 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

Scientific discovery is increasingly driven by massive, complex datasets that hold the keys to unlocking new knowledge and solving global challenges. However, the current landscape of data storage and management is struggling to keep pace with this data deluge. IOWarp is designed to create a next-generation data management platform tailored to the unique needs of modern, data-intensive science. This platform will address the fundamental challenges that researchers face when dealing with diverse data types, the exponential growth of data, and the need for rapid access to critical information. By streamlining data workflows and enhancing data accessibility, IOWarp will empower scientists and researchers to focus their valuable time and resources on their core mission: making groundbreaking discoveries. Beyond its technical advancements, IOWarp will also foster a collaborative and inclusive research environment, democratizing access to data and equipping the next generation of scientists with essential data skills. By accelerating research in critical fields like genomics, climate modeling, and AI-driven discovery, IOWarp has the potential to unlock transformative solutions to global challenges and drive innovation. The IOWarp project will engineer a modular, adaptable, and scalable data management platform that addresses the specific challenges encountered in modern scientific workflows, particularly those enhanced by artificial intelligence (AI) technologies. IOWarp will significantly reduce data access times, accelerating the pace of scientific discovery by harnessing state-of-the-art technologies like NVMe SSDs, CXL devices, and CPU-GPU codesigns. It will foster a collaborative ecosystem, inviting contributions from diverse scientific and engineering communities. Building upon the team's extensive expertise in multi-tiered storage research and leveraging prior NSF investments in this field, IOWarp will integrate AI-driven solutions to redefine data management for high-performance computing environments. The anticipated outcomes of IOWarp will establish a thriving community-driven platform that continually evolves to meet the changing needs of scientific research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The safety, security, and reliability of software applications have far-reaching impacts on everyone’s lives; therefore, formal reasoning about these guarantees has become increasingly important. While formal verification provides the highest assurance, very few companies can afford the cost of it. On the other hand, lower-effort approaches such as testing, applying linting tools, and peer code review have been widely adopted in the industry. In addition, a complex system may include differently analyzed components: some are tested, some go through a static analysis, while others undergo code review. This project aims to answer the question of how to formally reason about the safety, reliability, and security assurances of such complex systems when heterogeneous analysis methods are applied. Further, this project investigates, in the event of an incident, how counterfactual reasoning can be applied to identifying the cause of the issue and refine or harden the analyses or software to prevent future incidents. This project provides training opportunities for undergraduate and graduate students in topics including program testing, verification, vulnerability detection, and software security via research projects and dedicated course modules. The project first develops the logical and semantic foundations for compositional assurance reasoning, where modal operators such as possibility and necessity are used to express truth derived from under-approximate (incomplete) analysis and truth derived from over-approximate (complete) analysis, respectively. Reasoning principles to compose results from different types of analysis are built around these modal operators. To be concretely applicable to the analysis of programs for assurance, a Kripke semantics based on the program execution semantics is defined to give meaning to the logical formulas. Next, this project develops counterfactual reasoning principles to aid the refinement of the analysis and repairing programs when an incident occurs. The expressiveness, effectiveness, and efficiency of the reasoning systems are evaluated via case studies drawn from security incidents reported in recent years. 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.