University Of Maryland Baltimore County

NSF Awards · FY 2026 · 2026-07

The Internet of Things (IoT) refers to the billions of sensors and connected devices embedded in buildings, factories, hospitals, and cities that continuously collect data about the world around us. Despite the enormous potential of this data to improve how we manage energy, monitor public health, and run our industries, most organizations struggle to make use of it. The reason is that turning raw sensor readings into useful answers requires deep technical expertise. This includes knowing which sensors to consult, which AI models to apply, and how to combine everything correctly. This project addresses this challenge by developing a new approach that allows anyone to simply ask what they want to know (e.g., "which rooms were underused last month and how much energy did they consume?") while the computer system intelligently figures out how to find the answer. By hiding the complexity of sensors, this work has the potential to make the benefits of IoT technology to a much wider range of Americans, including educators, building managers, public health officials, and factory operators, through data management and artificial intelligence. The project also trains the next generation of students through hands-on IoT courses at the university level, high school internships, and a summer camp for students in the Baltimore area. This project advances the foundational science of IoT data management by introducing semantic abstraction as a first-class concept in query processing. Rather than requiring users to specify the sensors, models, and data pipelines needed to answer a question, the system treats high-level concepts (such as occupancy, air quality, or energy consumption) as directly queryable entities. The research is organized around three technical thrusts. The first defines a formal query model and optimization framework that reasons about multiple ways to compute a desired concept, selecting among them based on accuracy, latency, and resource cost. The second develops self-driving abstraction planning techniques that learn, using methods from machine learning and adaptive scheduling, when to compute abstractions eagerly at data ingestion time versus lazily at query time, and how to execute them efficiently across heterogeneous storage systems including time-series, relational, graph, and document databases. The third thrust extends the framework to federated settings where data are spread across multiple organizations with different privacy policies, enabling incremental, confidence-annotated answers under partial data availability and access constraints. All techniques will be validated in real-world deployments including a smart campus environment at the University of Maryland, Baltimore County, a smart manufacturing testbed, and a smart home laboratory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

This research will focus on advancing the understanding of particle acidity in the atmosphere of cold environments and assessing the effects of fuel oil sulfur reductions on particulate sulfur and other species in Alaskan cities. A recent transition to lower-sulfur fuel oil as part of a government-mandated air quality mitigation strategy in Fairbanks and neighboring communities offers a rare opportunity to directly observe and quantify the atmospheric impacts of reduced sulfur emissions. This work advances public health and welfare by guiding national strategies to reduce exposure to fine particulate matter and addressing the unique air quality challenges faced by Arctic communities. The recently NSF-funded 2022 Alaskan Layered Pollution And Chemical Analysis (ALPACA) study in Fairbanks Alaska showed that a substantial portion of PM2.5 sulfur was found in sulfur compounds that are only formed via aqueous-phase chemistry within a narrow pH range. The unique Arctic winter conditions enabled a new understanding of aerosol processes that enhanced heterogeneous chemistry by partitioning gas-phase precursors (e.g., formaldehyde and sulfur dioxide) into the particle phase and influenced particle pH through the temperature-sensitive behavior of key semi-volatile species like ammonia. In this project, real-time monitoring instrumentation will be used to quantify PM2.5 sulfate, hydroxymethanesulfonate, and related sulfur species, while simultaneously characterizing aerosol thermodynamics focusing on liquid water content and acidity under Arctic winter conditions. The inclusion of high time resolution measurements of gas-phase ammonia, nitric acid, and other trace gases will greatly extend beyond ALPACA’s original scope to better understand the processes influencing aerosol composition and acidity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The rapid advancement of artificial intelligence (AI) technologies is leading to changes in the skills employers expect from college graduates. This raises questions about how well-positioned college administrators and instructors are to updated their curricula to help students meet these evolving needs. This project will study these questions in the context of software engineering jobs and computer science departments, which are large sectors of the economy and the university that are particularly affected because of the increasing ability of AI-based tools to perform common programming tasks. Through working with hiring managers, employees, faculty, and students, the research team will develop new curricula that align the needs of both industry and universities to develop high-quality educational experiences that enhance students’ competitiveness in the workforce. The goal of the award is to build industry and academic partnerships to establish an evidence-based set of recommendations, resources, and upskilling for undergraduate computing faculty and students around cultivating AI-related competencies. As a first step, the research team will conduct semi-structured interviews with current employees and hiring decision makers on the knowledge, skills, and dispositions required for new software engineers and developers. Then, the team will apply these findings to launch a career readiness training program for computing students from two institutions, as well as opportunities for these students to apply that training through internships. Based on the feedback of the interns and their industry mentors, the training approach and resources will be refined and then launched more widely with computing faculty and undergraduate students at institutions across the U.S. The team will work to disseminate the insights and materials developed through outreach activities including a virtual expert seminar series and a website centered on AI-related competency development and career preparation. The project advances technology workforce initiatives by strategically aligning industry employees, hiring decision-makers, computing faculty, and computing majors around shared expectations and the competencies essential for practical and professional success in an increasingly AI-driven society. 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 · 2026-04

This award is to assist in funding a number of US-based graduate students for attending the 44-th IEEE VLSI Test Symposium with the intention of exposing them to the discipline and to encourage them to pursue careers in microelectronics. Participation in this symposium is considered an important part of the graduate school experience of students in the field of VLSI design, providing the opportunity to interact with senior researchers and to be exposed to leading edge work in the field. The support will enable dissemination of scientific knowledge to students who would otherwise not have the experience. This student travel support will also facilitate the development of promising graduate students and will thus indirectly also help train the future workforce in the important area of microelectronics. The IEEE VLSI Test Symposium (VTS) for the past 43 years has been the premier academic forum where emerging trends and novel concepts in testing, debug and repair of microelectronic circuits and systems are explored. The 44-th edition of this symposium will addresses key trends and challenges in the semiconductor design and manufacturing industries through a program that includes Keynote and Plenary Talks, Technical Paper Sessions, Embedded Tutorials, Panels, Hot Topic Sessions, Half-day Tutorials, and the Innovative Practices Track. The conference committee has set up an adequate evaluation criterion to select the qualified students from a pool of applicants formed via open advertisement. The committee also plans to consider a small number of undergraduate applicants to better reach a broad population of potential researchers in this field. 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

Dynamic mapping of complex brain circuits by monitoring and modulating brain activity can enhance our understanding of brain functions and provide the promise of better treatment and prevention of different neurological disorders. Interfacing with the brain also has the potential to enhance our perceptual, motor, and cognitive capabilities, as well as to restore sensory and motor functions lost through injuries or diseases. The development of closed-loop neural interfaces with high-resolution recording and stimulation capabilities from the distributed neural circuits within the entire brain is still a grand challenge of neuroscience research. Current noninvasive neuromodulation techniques still suffer from poor spatial resolution (> 100-1000’s of mm3), while implantable methods with finer resolution only provide a limited coverage of 100-1000’s of neurons through highly invasive parenchymal implantation. This integrated research and education program enables minimally invasive ultrasound neuromodulation (and neural recording) of the brain with high spatial resolution (< 200 µm) at large scale (over the whole brain). This project will yield a unique building block for a comprehensive set of neural interfaces. It will open new opportunities in neuroscience with significant improvements in spatial resolution and coverage of the brain stimulation in animals. It will also have translational potential for clinical applications in humans, such as the treatment of neurological and psychiatric disorders and brain-machine interfaces. This project also includes an integrated outreach and educational component to impact K-12 teachers/students and undergraduate and graduate students, and to develop an interdisciplinary workforce. This project will educate a broad audience in the science and applications of the research components and enhance their research skills through systematic troubleshooting activities. Graduate curriculums across different disciplines will also be transformed with related multidisciplinary projects and guest lectures. This project includes scientific research to investigate implantable ultrasound stimulation on a flexible platform (placed on the brain surface with no parenchymal penetration) to simultaneously provide high spatial resolution (< 200 µm) and broad coverage (over the whole brain) while dramatically reducing invasiveness. This multidisciplinary project, which brings together expertise in electrical and biomedical engineering as well as material, computer, and neuro science, is transformative in that it is potentially the only method that promises large-scale stimulation across distributed brain regions at different depths with high resolutions of < 200 µm without parenchymal implantation, opening a new venue for understanding neural and cognitive systems at large temporal and spatial scales. The development of this technology builds upon investigators’ strength in circuits, wireless power, flexible technologies, thin-film ultrasound arrays, machine learning, and neural interfaces. The project pushes the limits of ultrasound neuromodulation by investigating a flexible, image-guided (with machine learning models), hybrid electrical-acoustic implantable system with the form factor of a thin flexible sheet (on the brain surface) for ultrasound stimulation (and electrophysiology recording). Three fundamental research gaps will be addressed. 1) For large-scale and high-resolution ultrasound beam focusing and steering, the optimal approach in scaling up the number of ultrasound elements and application-specific integrated circuit (ASIC) channels at high frequencies (e.g., 5 MHz) will be explored. To reduce the complexity, thin-film transistors on a flexible substrate will be leveraged to form a large two-dimensional ultrasound array with selectable one-dimensional arrays (e.g., 256-element) driven by only one ASIC. 2) Selectable thin-film ultrasound arrays with thin-film transistor switches on flexible substrate will be optimized to achieve high efficiency and high pressure output. 3) Imaging and machine learning models based on image sequence analysis will be developed to guide the ultrasound focused beam, considering the device flexibility (ultrasound elements’ orientation) and post-implantation effects. A system-level demonstration in benchtop and in vivo settings will establish the feasibility of this flexible implantable system. 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

Neuromodulation has the potential to map neural functions; enhance our perceptual, motor, and cognitive capabilities; and restore sensory and motor functions lost through injury or disease. Despite several decades of research and development, state-of-the-art noninvasive neuromodulation techniques still suffer from poor spatial resolution (more than several millimeters), while implantable electrical and optical methods with finer spatial resolution only provide a limited coverage of hundreds to thousands of neurons through extremely invasive parenchymal implantation. These limitations are fundamental, and further optimization of these technologies cannot simultaneously meet the critical requirements of minimal invasiveness, microscopic spatial resolution (hundreds of micrometers and below), and whole brain coverage. This program includes scientific research in a radical approach that explores ultrasound, which has already been effective in transcranial neuromodulation with sub-centimeter resolution, as a minimally invasive implantable means for unprecedented microscopic-resolution neuromodulation at large scale. The proposed research will yield a unique building block for a comprehensive set of minimally invasive neural interfaces. It will open new opportunities in neuroscience with significant improvements in spatial resolution and coverage of neuromodulation of the brain, initially in animals. Ultimately, it will also have huge translational potential for many clinical applications in humans, such as the treatment of neurological and psychiatric disorders and brain-machine interfaces. Leveraging the multidisciplinary nature of the research, this program also includes an integrated outreach and educational component created around a "Troubleshooting and Inquiry-based Learning (TIL) Framework" to enhance students' learning of principles and research skills at different education levels. Transforming an undergraduate circuit course with the TIL framework will enhance the research skills, problem solving, and creative thinking of many undergraduate students. An annual week-long summer workshop for teachers with educational TIL-based hands-on and in-class computer-game-based modules will educate K-12 teachers and their students from districts underrepresented in the science, technology, engineering, and mathematics (STEM) fields in this research. A TIL-based "Ultrasonically Transferred Song" hands-on module for pre-college female students will attract them to the engineering profession and educate them in this research. A new medical-device course will educate graduate students in this field. This program will explore implantable microscopic ultrasound stimulation (IuUS) with minimally invasive modulation of the whole brain with the spatial resolution of hundreds of micrometers and below. This program will establish the fundamental basis for IuUS, in which an ultrasound transducer array is implanted on the brain surface (partially removed skull) with no parenchymal penetration to electronically steer highly focused ultrasound beams towards different neural targets. Such a system can be utilized in basic neuroscience experiments to address the most fundamental scientific questions in ultrasound neuromodulation: underlying mechanism, efficacy, and safety. This work will explore and establish vibro-acoustography for high energy efficiency in IuUS. It will investigate fundamental limits of spatial resolution and coverage as well as energy efficiency in IuUS by developing numerical and computational models based on wave equations to explore effects of different transducer geometries, frequencies, and configurations as well as their interactions with tissue and electronics. To manage post-implantation uncertainties (e.g. micromotions), this work will explore and create a learning-based all-acoustic image-guided system for accurate anatomical targeting. An on-chip machine-learning model with offline training will be developed to dynamically map changes in the profile of acoustic beams to micromotions and tissue changes in a fast and accurate fashion. An inductively interrogated closed-loop (recording and stimulation) system-on-chip with novel circuitry will also be developed for IuUS. Finally, a system-level demonstration will establish the fundamental basis for IuUS. 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

This project aims to serve the national interest by enhancing engineering curriculum to improve learning and graduation rates for all engineering students, especially students with fragmented time. Students’ fragmented study time is often due to commuting, working, and managing family responsibilities. These often time-consuming and time-interrupting tasks can negatively impact students’ academic success and graduation, contributing to a national shortfall of well-prepared STEM professionals. This Level 1 Engaged Student Learning project aims to help all students by creating engaging video- and inquiry-based homework activities that make learning engineering concepts easier and more flexible. Further, this project plans to advance our understanding of engaging engineering curricula and methodologies to best support student learning. Enhancing engineering curriculum, boosting student success, and investigating conditions under which improved student learning occurs are important in training well-prepared engineering professionals that will serve national interests. The project team plans to fulfill three aims: Aim 1: To develop conceptual questions and homework modules of mechanical engineering courses with machine learning assistance. Aim 2: To implement the developed homework modules in the same mechanical engineering and evaluate the effectiveness of the homework modules. Aim 3: To modify the assessment module based on students’ feedback, implement the (modified) assessments, and analyze collected data to evaluate the effectiveness of the homework module affecting mechanical engineering course learning. The plan is to assess and disseminate immediate findings to peers, scientific communities, and the public through websites, workshops, publications, conferences, and social media platforms to broaden the impact of the project. The project is part of a long-term effort to establish innovative and sustainable pedagogies and curriculum improvement to boost students’ retention and graduation rates. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

With limited resources available to tackle various challenging problems, policy makers and stakeholders often have to prioritize which problems to address in what locations or domains. In statistical terms, this involves the ranking of sub-populations and regions when limited (or no) directly observed data is available, and the available data can be on multiple aspects and of diverse types and modalities. The investigators will address the core theoretical, methodological and algorithmic challenges in such problems of ranking entities in multiple contexts of interest to the nation and to public life. The investigators will also develop techniques for measuring and quantifying the variability and uncertainty of such advanced, data-driven, principled ranking techniques to aid policymakers and stakeholders. For data to be analyzed using hierarchical models, subject to multiple sources of variability and dependencies, the investigators will develop reliable estimates of the ranks of entities with an appropriate quantification of associated uncertainty. The proposed methodologies will follow a Bayesian framework or a resampling-based frequentist one. While these techniques are primarily computation-driven, the investigators will address theoretical foundations of the proposed approaches both in the Bayesian and in the resampling-based frequentist paradigms. Using scalable computational techniques and leveraging geometric and topological properties of data, the investigators will also develop novel methods for ranking and identification of extremes when multivariate responses are of interest, and address benchmarking for compatibility over hierarchy of domains in a principled way. 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

Aberrant bioelectrical slow-wave patterns (dysrhythmias) are linked to gastric dysmotility in several significant gastric motility disorders such as gastroparesis, chronic nausea and unexplained vomiting, and functional dyspepsia. Current therapies, including gastric electrical stimulation and pharmaceuticals, are delivered in an open-loop fashion through trial and error, resulting in inconsistent therapeutic effects. No existing tools can identify and deliver optimized electrical stimulation to restore normal stomach motility. This program aims at advancing knowledge and optimizing health treatments in gastric disorders (and more broadly in different organ systems) by developing novel wireless implantable technologies for adaptive, closed-loop recording and stimulation. It overcomes invasiveness of bulky implants, lack of objective patient-in-the-loop feedback in open-loop operation, absence of an interactive model between patient and device for informed therapy decisions, and lack of adaptive intelligent algorithms. This project will develop an intelligent adaptive closed-loop system (CyberGut) that identifies and delivers optimized stimulation for restoring normal gastric motility. Three specific aims include the development of wireless recording/stimulation with millimeter-scale implants; computational organ modeling and machine learning (ML) algorithm developments; and CyberGut system integration and validation. The research tasks will design, develop, and test 1) a network of millimeter-scale devices (called Gastric Seeds), each integrating an application-specific integrated circuit (ASIC) and a magnetoelectric transducer, endoscopically implanted within the stomach submucosal space for fully wireless recording and stimulation functions; 2) a wearable unit for wireless interrogation (powering, communication) of Gastric Seeds through hybrid magnetic-ultrasonic (MagSonic) links, and intelligent identification (through an embedded physics- informed ML model) and delivery of optimized electrical stimulation paradigm to Gastric Seeds; and 3) a computational virtual stomach model within a PC framework that accurately replicates stomach’s physiological/mechanical behavior for generating vast data in training the embedded ML model. RELEVANCE (See instructions): Current therapies (electrical stimulation, pharmaceuticals) for functional gastrointestinal disorders are delivered in an open-loop fashion through trial and error, resulting in inconsistent therapeutic effects. This project enables real-time monitoring, analysis, and control of gastric function through development of an intelligent closed-loop system (CyberGut) that replicates the stomach’s physiological/mechanical behavior.

NSF Awards · FY 2025 · 2025-09

Quantum entanglement is a powerful phenomenon in which particles become deeply linked, even when separated by great distances. This uniquely quantum effect forms the foundation for emerging technologies in secure communication, advanced computing, and precision measurement. However, sharing entanglement across many locations remains a major challenge. This project will develop new methods to reliably generate, store, and distribute entangled particles of light, known as photons, between multiple locations using devices called quantum memories. These memories act like temporary storage for quantum information and are essential building blocks for future quantum networks. By addressing a key barrier to building large-scale quantum systems, this work will help advance the progress of science and open new possibilities for future technologies. The project also contributes to national priorities in education and workforce development by training graduate and undergraduate students in cutting-edge quantum research. Technically, this project addresses a long-standing challenge in entanglement distribution: the mismatch between the broad spectral bandwidth of practical entangled photon sources and the narrow bandwidth of most quantum memories. The research will combine well-established broadband entangled photon sources based on Spontaneous Parametric Down-Conversion (SPDC) with an innovative “loop-based” quantum memory platform that can operate over a much wider frequency range. This hybrid system will enable efficient entanglement distribution across up to five spatially separated memory nodes— a substantial scale-up from existing two-node systems. Key research goals include developing low-loss dual-port loop-based quantum memories, demonstrating quantum interference with stored photons, correcting memory-induced errors, and transferring entangled states among different sets of memory nodes. The results will advance the capabilities of quantum networks and lay the groundwork for future scaling of entanglement distribution 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 · 2025-09

High-Resolution Transcranial Ultrasound Neuromodulation at Large Scale Neuromodulation has the potential to map neural functions; enhance our perceptual, motor, and cognitive capabilities; and restore sensory and motor functions lost through injury or disease. Despite decades of research and development, state-of-the-art noninvasive neuromodulation techniques still suffer from extremely poor spatial resolution (100-1000’s of mm3). This project includes scientific research that explores orthogonal crossed beams of ultrasound as a noninvasive transcranial means for unprecedented < 0.1 mm3 spatial resolution neuromodulation at large scale. Compared to its noninvasive counterparts, this crossed-beam ultrasound neuromodulation technology has the potential to improve the spatial resolution (focal spot) by several orders of magnitude. Therefore, it will yield a unique building block for a comprehensive set of noninvasive neural interfaces. It will open new opportunities in neuroscience with significant improvements in spatial resolution and coverage of noninvasive neuromodulation of the brain, initially in animals. Ultimately, it will also have huge translational potential for many clinical applications in humans, such as the treatment of neurological and psychiatric disorders and brain-machine interfaces. Leveraging the multidisciplinary nature of the research, this project also includes a significant integrated outreach and educational component created around a “Machine-Learning-inspired Physical Troubleshooting” framework to impact K-12 teachers/students, undergraduate and graduate students. The troubleshooting framework will stimulate the interest of K-12 students in electrical engineering to recruit more students to this major, will educate a broad audience from undergraduate students to K-12 teachers and their students in the science and applications of this research, and will enhance teachers’ and students’ research skills through systematic troubleshooting and problem-solving activities. Graduate curriculum on circuits and optimization-based machine learning will also be transformed with multidisciplinary projects and guest lectures to educate graduate students in the design and applications of smart integrated systems. This project proposes and explores high-resolution transcranial ultrasound stimulation (HR-TUS) system, in which extracranial ultrasound transducer arrays electronically steer ≤ 1 MHz crossed focused ultrasound beams, guided by imaging and machine learning models, at different neural targets with ultrasound pressure focal spots of < 0.1 mm3. Building on the investigators’ complementary expertise in integrated circuits, ultrasound-based systems, wireless neural interfaces, and machine learning for image analysis, this project will establish the fundamental basis for large-scale HR-TUS with orthogonal crossed ultrasound beams guided by imaging and machine learning models. This project will investigate fundamental limits of spatial resolution and coverage within a human brain volume in HR-TUS by developing numerical and computational models based on wave equations to explore the effects of different geometries, frequencies, and configurations of phased arrays and their interactions with the skull and brain tissue in the context of orthogonal crossed beams. This project will also explore imaging and machine learning models for accurate anatomical targeting, focusing, and beam crossing in the presence of skull/tissue effects on ultrasound beams and displacements in ambulatory subjects. To reduce the system complexity, size, and power consumption in three-dimensional stimulation of tissues at large scale, the novel solution of this project is a large two-dimensional array on a flexible substrate consisting of optimally arranged modular selectable linear arrays and their application-specific integrated circuits. A system-level demonstration at the end of this project will establish the feasibility of the HR-TUS. The image-guided HR-TUS system with machine learning model will provide a first-in-class platform for learning-based acoustically guided transcranial ultrasound neuromodulation (all acoustic) with high spatial resolution (< 0.1 mm3) at large scale (over the whole brain). 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 develops and implements a research administration internship program for undergraduate students at five institutions in Maryland and Delaware – a region with high research activity and high demand for research administration professionals. Students represent a large, well educated, but as-yet largely untapped pool to meet the workforce needs in research administration, a field facing a scarcity of trained candidates in the pipeline. Through this “grow your own” internship approach, students have a viable career pathway into research administration that also helps build capacity in research administration at their respective institutions. In addition, the project charts a course for a sustainable, replicable, and scalable internship model that other institutions can follow – particularly mid-sized, smaller, and emerging research institutions – as they aim to build their workforce and remain competitive in increasingly complex research environments. Over the long term, the broader impacts of the project include enhancing research infrastructure, building capacity in research administration, and strengthening research competitiveness for institutions across the U.S. The comprehensive internship program provides students with hands-on training and practical research administration skills through placements in research offices and centers across participating universities, thereby supporting research administration for the institution. Students also gain broader professional and academic experience via a cross-institutional, cohort-based component of the internship, which includes professional development, research activities, on-campus and external mentorship, and networking. Students are included as co-researchers on presentations and workshops at regional and national conferences and papers for research administration journals. Through these initiatives, student interns engage in scientific participation while gaining familiarity with the processes, obtaining relevant workforce experience, and developing strong networks in the growing field of research administration. 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

Marine phytoplankton are responsible for approximately half of the planet’s net primary production (NPP) and are undergoing rapid change in response to shifting surface ocean heat budgets. Satellite remote sensing has provided nearly three decades of surface ocean color data, enabling us to infer phytoplankton distribution and improve NPP estimates in the surface mixed layer with unprecedented spatial coverage. However, phytoplankton below the ocean mixed layer contribute substantially to global NPP, yet this contribution remains poorly constrained. This project will leverage satellite data, trained and validated with direct observations provided by the Biogeochemical-Argo autonomous float network, to detect signatures of stratification, deep fluorescence and deep biomass maxima in the surface ocean through physics-informed deep learning approaches. The approach is to examine multiple models with varying levels of abstraction and interpretability, additionally validated against independent ship- and mooring-based regional time series datasets. This study will enable identification of important knowledge gaps in the formulation of mechanistic modelling approaches to derive estimates of phytoplankton biomass, nutrient limitation and NPP beneath the ocean surface. This study will also examine these phenomena in basins with sparse direct observation coverage. The project will develop a novel pipeline using convolutional LSTM coupled with spatial and temporal transformer blocks to simultaneously predict multivariate data through space, time and depth. Two basic approaches will be tested; one that incorporates a priori knowledge guided feature engineering (e.g. inputs of mixed layer depth and nutricline depth) and one which uses posteriori physics and physiology guided constraints to regularize the weights in an iterative procedure. This approach will allow for a high dimensional AI based framework that can be generalized for hypotheses testing which has otherwise not been possible. Once predicted these profiles will facilitate discovery of anomalous biomass regions and other knowledge to evaluate the hypotheses. 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

The SFS program at the University of Maryland in Baltimore County (UMBC) is proposing to increase the number of qualified professionals who will be highly skilled in both cybersecurity (cyber) and artificial intelligence (AI), to strengthen United States’ security and competitiveness. America needs more cybersecurity professionals, and AI is becoming a critical aspect of all fields including cyber. UMBC’s SFS program attracts qualified students to UMBC’s undergraduate and graduate programs in cybersecurity, producing cyber professionals in service to the Nation in federal, state, local, or tribal government. In a broader context, the UMBC’s approach provides a model to other schools for active project-based curriculum enhancements, including using the campus network as a research laboratory. UMBC is an R1 university, as well a Center of Academic Excellence in Cyber Defense (CAE-CD) and a Center of Academic Excellence in Cyber Research (CAE-R) - designated by the National Centers of Academic Excellence in Cybersecurity (NCAE-C). UMBC’s strengths include its technical research capabilities and integration of the cybersecurity discipline with real-world experience through project-based learning, which will contribute to SFS students being prepared and placed into government jobs. Beyond coursework, the Empowering New Growth through Active Group Engagement (ENGAGE) program will foster theoretical and practical knowledge using project-based learning for students in cyber and AI. Each year, all UMBC SFS scholars engage in collaborative applied research study focusing on some aspects of the UMBC computing systems. In addition, each SFS scholar is expected to participate actively in a cyber- or AI-related UMBC research group or student activity. UMBC offers many project-based special topics courses on cyber, including malware analysis, data privacy, and INSuRE cyber research. In addition to working with individual faculty members, SFS scholars have opportunities to engage in research projects at partner organizations. This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal Government organization for the same duration as their scholarship support.  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

The increasing frequency and severity of natural disasters—such as forest fires, earthquakes, snowstorms, tornadoes, and hurricanes—are significantly impacting U.S. cities and coastal communities, displacing thousands of people and incurring billions of dollars in government spending. To address these growing challenges, this project proposes the development of TRACE (Testbed for Disaster Resilience Auditing and Crisis Evaluation), an innovative cross-cutting platform integrating Social Cyber-Physical Systems (CPS), Internet of Things (IoT), Robotics, and AI/ML technologies. TRACE is designed to assist multidisciplinary search and rescue teams in accelerating mission-critical response and recovery operations in post-disaster scenarios. The project's integrative approach aims to build a scalable, multidimensional, and resilient AI-ready testbed to assess the effects of natural hazards—including fires, earthquakes, floods, hurricanes, and tornadoes—on the built environment (buildings, bridges), infrastructure (roads, utilities), and communities. By combining TRACE with scalable AI/ML algorithms, the project will support community-level disaster resilience through: (i) Characterization of risks and vulnerabilities, (ii) Anticipation of failures and losses, (iii) Data-driven planning and decision-making in partnership with civic agencies. Resilience metrics will be quantified at both system and application levels and reevaluated in terms of community strategies across the five disaster management phases: prevention, preparedness, response, mitigation, and recovery. To achieve these objectives, the project will design, develop, implement, and evaluate the following components: (i) Novel AI/ML techniques for navigation in rugged terrains affected by wildfires, earthquakes, snowstorms, and hurricanes, (ii) Real-time multi-agent communication and coordination among robotic systems (UAVs, UASs, and UGVs), including operation in network-denied environments, (iii) Social sensing and human-in-the-loop feedback mechanisms to support intelligent decision-making and situational awareness, (iv) AI/ML algorithms for near real-time simulation of dynamic disaster environments using virtual-physical co-simulation platforms, (v) Responsible AI models for perception, awareness, and life-sign detection leveraging multimodal sensor inputs (e.g., camera, LiDAR, mmWave radar). The project will further develop spatio-temporal analytics, real-time streaming data analysis, and disaster mapping toolkits to empower emergency responders with actionable insights during natural disasters using the TRACE platform. Workshops and bi-weekly planning meetings with collaborators, domain experts, and guest researchers are being conducted during the initial phases to co-design the AI-Ready testbed TRACE and ensure its relevance and impact. 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

Filamentous fungi have a dramatic impact on the global economy (by one estimate, trillions of dollars annually) through both beneficial applications, such as pharmaceutical production and sustainable biomaterials, as well as harmful effects including crop destruction and human disease. In all these cases, fungi depend critically on their protective cell wall for success. Despite this importance, it is not fully understood how fungi respond to, and recover from, cell wall damage. This research investigates the fundamental biological question of how fungi detect wall stress, survive initial damage, and eventually restore normal growth. The research uses advanced microscopy, genetic tools, and computational modeling to uncover the molecular mechanisms that coordinate these responses in a model fungus. Understanding these processes will eventually enable "tuning" of fungal cell-wall properties for diverse applications, including: increasing productivity in bioprocess manufacturing, improving the physical properties of renewable mycelium-based materials that could replace petroleum-based products, and identifying new targets for antifungal drugs to protect crops and improve human health. The research also provides significant educational opportunities, training both undergraduate and graduate students in interdisciplinary approaches that combine biology, engineering, and computational sciences through collaborative teams across three universities. This project investigates how filamentous fungi respond to cell-wall stress, focusing on the model fungus Aspergillus nidulans. The molecular mechanisms involved in both immediate survival responses and subsequent recovery from wall damage are characterized using (i) advanced microscopy to visualize actin localization and dynamics during stress, (ii) genetic manipulation to identify key regulatory proteins, (iii) systems biology approaches to discover novel components, and (iv) mathematical modeling to integrate these findings into a cohesive network model. Specifically, the fungal response to inhibition of β-glucan biosynthesis is being characterized by testing the hypothesis that a two-phase response is involved. This includes an initial "survival phase," with rapid actin redistribution to form protective septa, which is followed by a "recovery phase" involving expression of specific proteins enabling growth resumption. In addition, a core set of stress regulators is being identified from proteomic analysis by comparing responses across multiple wall stressors, distinguishing universal responses from stressor-specific reactions. Finally, a hybrid modeling approach is being developed which integrates both mechanistic and machine-learning methods to infer the topology of regulatory pathways and their interconnections. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

This Institutional Research Training Grant (T32) application seeks funding of the UMBC-UMB CBI program that has an overall objective to provide a pathway to produce Ph.D.-level scientists trained across disciplines who are strong leaders capable of tackling the broad challenges of the 21st century in, or adjacent to, the biomedical sciences. UMBC and UMB are leaders in developing trainees within science, technology, engineering, and mathematics (STEM) fields at both the undergraduate and graduate levels, and the UMBC-UMB CBI program has contributed to this success by producing strong and thriving Ph.D. graduates in the biomedical workforce thanks to funding by NIH for nearly 20 years. More than 40 T32 fellows have been previously supported by this program in its entirety, while nearly 175 trainees have received partial support. The program retention and graduation rates both average 97%, and career placements have been strong, leading to trainees in industry, government, science policy, scientific publishing, and academia. The current inter-institutional CBI program mentors nearly 45 trainees per year by a strong network of 35 research-active faculty and is overseen by internal and external advisory boards. The future program has the following measurable objectives of those taking part in the UMBC-UMB CBI program: 1) to improve the percentage of those who complete their respective Ph.D. programs; 2) to improve the time to degree completion of those who complete their respective Ph.D. programs; 3) to increase the number of publications output; 4) to increase the number of conferences attended; 5) to improve the job outcomes of those who complete their respective Ph.D. programs. The trainees take part in a well-constructed CBI program comprising: didactic instruction at the chemistry-biology interface (CHEM/BIOL 603 “Introduction to the Chemistry-Biology Interface” and CHEM 715 “Issues at the Chemistry Biology Interface”); reinforced education in responsible, rigorous, and reproducible research; required implementation of hands-on cross-disciplinary research; and recurring support to attend national and international conferences, among other important programmatic components. Significant and meaningful supplementary monetary contributions are provided, including matching funds from the respective Departments and Institutions to afford the ability of all trainees to take part in every benefit of the program and to provide an outsized effect on all CBI trainees.

NSF Awards · FY 2025 · 2025-06

This project aims to serve the national interest by improving curricula in undergraduate computing education to prepare students for the challenges of understanding and managing Technical Debt (TD) in software systems. Technical debt arises when software developers make technical compromises that may bring short-term benefits but result in lower software quality in the long term, often leading to challenges in maintaining and evolving software. By integrating technical debt concepts into computing curricula at multiple levels, the project intends to contribute to building a strong foundation for students to develop high quality software, and prepare them to become part of a more effective and competitive STEM workforce. The project plans to develop an innovative inquiry-based learning tool, called TD-Tutor (Technical Debt Tutor), to help students recognize, evaluate, and manage technical debt. TD-Tutor will enhance outcomes for student populations from different backgrounds and types of institutions, aligning with NSF’s mission to advance STEM education and workforce development. TD-Tutor will be implemented, used, and evaluated at three curriculum levels: introductory programming, mid- level software engineering, and senior level decision-making courses. The tool will feature annotated examples, interactive exercises, and conceptual feedback to guide student learning, and will incorporate guided inquiry and spiral learning approaches. Pre- and post-evaluations will assess the tool’s impact on student learning, skill development, and readiness to manage software quality. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

The EXPLORE REU Site is a multidisciplinary project that involves collaboration with multiple scientists from various programs and departments at the University of Maryland Baltimore County and partner institutions. The purpose of the EXPLORE REU Site is to benefit and attract a broad group of undergraduate students to the world of science by offering a wide range of scientific and networking activities within the field of Earth and Atmospheric Science. Students will have the opportunity to work on the development of new software and satellites, engage in data processing, utilize global numerical models, and conduct hands-on experiments for the characterization of atmospheric particles. Each student will be guided by a primary mentor and assisted by a supporting team and will work on a specific research project. In addition, a series of activities involving professional development, networking, and personal growth will be offered during the duration of the program. The EXPLORE REU Site will provide opportunities for undergraduate students to engage in high-level research projects encompassing a wide range of topics: atmospheric composition, atmosphere-ocean interactions, aerosol-cloud feedbacks, satellite remote sensing observations, and satellite development and design. Each project addresses critical questions currently under investigation by the scientific community. The program will directly address issues related to making the geosciences a viable career for all students and strengthening the future geoscience workforce. The program will leverage students’ preparation across many STEM fields to help strengthen the geosciences workforce. Specifically, this program will train and empower 30 REU students (10 students each year) with technical knowledge in space and environmental data analysis, quantitative methods, programming skills, instrument operation expertise, and effective science communication skills. The program will enable REU students to contribute to projects addressing critical issues for our society with implications for advancements in the areas of atmospheric composition, air pollution, human health, weather, and climate, as well as pressing environmental challenges, while developing practical skills in instrumentation, software, and data processing. 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 I-Corps project focuses on the development of an intelligent, non-lethal wildlife management system that enables coexistence between humans and wildlife in urban and rural environments. As human expansion encroaches on natural habitats, conflicts with wildlife result in property damage, safety risks, and ecological imbalances. Traditional deterrence methods, such as fencing or chemical repellents, are costly, ineffective, or environmentally harmful. This project introduces an adaptive, technology-driven approach using artificial intelligence and sound-based deterrence to create virtual boundaries that protect crops, gardens, and other sensitive areas while minimizing harm to wildlife. The potential commercial impact spans residential, agricultural, and commercial applications, offering cost-effective, scalable, and sustainable solutions for human-wildlife interactions. By integrating intelligent monitoring and deterrence, this innovation supports conservation efforts, promotes public safety, and provides a marketable solution to stakeholders seeking humane and efficient wildlife management strategies. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an artificial intelligence (AI)-driven framework that integrates real-time vision control, acoustic redirection, and cloud-based learning. The system employs computer vision to detect and identify wildlife species, combining this data with targeted ultrasonic deterrence to influence animal behavior without causing harm. The intelligent coexistence platform continuously adapts through cloud-connected models that refine deterrence strategies based on real-world interactions. The research supporting this technology builds advances in AI, edge computing, and acoustic engineering, demonstrating promising results in controlled laboratory conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Reproducing a software bug is necessary to ensure that the bug exists and to observe its behavior. It is also essential for further analysis to fix the bug. Reproducing system-level concurrency bugs requires not only input data but also the interleaving order of system calls. Manually reproducing this type of bug from bug reports is challenging due to its elusive nature and the need for supplementary details. Moreover, bug reports composed in natural language are frequently unstructured, posing a challenge when it comes to extracting essential information. Existing bug reproduction tools are incompatible with this type of bug due to their inability to deal with the specific interleaving schedule at the system call level. To address these challenges and improve the efficiency of reproducing these bugs from bug reports, a novel framework named RepSON will be developed. It will lessen the manual burden of the software developers to debug system-level concurrency bugs that happen frequently in modern software systems. Furthermore, this project will develop a technique for extracting information and generating executable inputs from bug reports that can also be applied to other types of software bugs. The technical goals of the project are divided into two major tasks. First, an empirical study will be conducted on open-source bug repositories to identify system-level concurrency bug reports and summarize their characteristics for guiding the automated debugging process. Second, an automated framework RepSON will be developed for reproducing bugs in multi-process applications by analyzing bug reports. Natural language processing, data mining, and dynamic program analysis techniques will be employed in the development of RepSON. It will take a bug report as input and reproduce the associated bug by generating input and instrumentation location. To achieve this, RepSON will analyze the bug report to generate the input script and extract system call names that may cause the bug. Subsequently, it will run the program, collect the system call trace to identify potential buggy interleaving, and instrument the code to run it again and reproduce the bug. 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 · 2024-12

This project will quantitatively assess the influence of salt on the aqueous partitioning of water-soluble organic gases in the atmosphere. The work will be conducted through field and laboratory measurements, reanalysis of prior atmospheric chemistry field campaign data, and the development of parameterizations for implementation in chemical transport models to improve predictions of atmospheric trace gases and aerosols. This work could improve the understanding about how polluting aerosols and gases are formed in the atmosphere. The research has the following objectives: (1) Measure the effects of atmospherically relevant inorganic salt mixtures on partitioning of ambient water-soluble organic gases in diverse environments; (2) Characterize the salting effects on partitioning of key oxygenated organic compounds through detailed laboratory experiments; (3) Explore in detail evidence for salting impacts on organic compound partitioning through reanalysis of atmospheric chemistry field campaign data; and (4) Develop a salt-influence parameterization for organic partitioning based on laboratory findings and conduct simulations of the U.S. This effort will support the training of a postdoctoral scholar and a graduate student at the University of California Irvine and both graduate and undergraduate students at the University of Maryland Baltimore County. 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

A new research and education partnership in astronomy will be developed between the Rochester Institute of Technology (RIT) and Texas Tech University (TTU) with the aim of exploring how to break both disciplinary and cultural boundaries to solve key questions in neutron-star astrophysics. RIT hosts the National Technical Institute for the Deaf, one of the premier deaf education institutes in the world and TTU is a Hispanic Serving Institution. The two-year program will explore authentic pathways for Deaf/Hard-of-Hearing and Hispanic undergraduate students to join the RITTU partnership and participate in academic year preparation programs and summer research experiences. The students will acquire a set of skills that cross between theory and observations and will be supported by a dedicated mentoring team, thereby placing them in competitive positions for graduate programs or other STEM careers. The next few years will herald a golden age for the astrophysics of neutron stars, which are compact stellar objects, often synonymous with pulsars, and which are one of the end stages of massive star evolution. Neutron stars and mergers of binary neutron stars can be observed through multiple messengers: gravitational waves (GWs), electromagnetic radiation and potentially neutrinos—offering unparalleled opportunities to answer fundamental questions in astrophysics. The program will involve two inter-related studies. The study of binary neutron star mergers will reveal the formation mechanism of the Universe’s heaviest elements, probe the generation and structure of the most powerful astrophysical jets, and elucidate the characteristics of the remnant population of massive stellar evolution. In the topic of neutron star astrophysics, the team will develop new tools to shed light on pulsar glitches and use pulsar timing observations to guide searches for burst and continuous GWs. This award advances the goals of the Windows on the Universe Big Idea. 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

A sustainable society needs a diverse and competent workforce equipped with skills to extract patterns from large and complex datasets, turn them into actionable insights, and develop solutions to solve real-world problems relevant to society. With the advancement of generative artificial intelligence (AI), machines are increasingly capable of writing code according to specific instructions and performing specific data analysis tasks. Higher-order problem-solving skills are becoming increasingly important to develop among students as they are less likely to be replaced by AI. Thus, a scalable, innovative solution is urgently needed to help graduate students develop their critical-thinking skills. This National Science Foundation Innovations in Graduate Education (IGE) award to the University of Maryland Baltimore County (UMBC) and the University of Central Florida (UCF) will augment, refine, and pilot Caselet, a scalable case-based practice tool, by leveraging AI, machine learning, and data analytics approaches, including large language models (LLMs). This project will support development of data science problem-solving skills in both cognitive (the knowledge and skills themselves) and metacognitive domains (the skills for learning how to learn). The project will address the rapidly changing landscape of education in computing and data-intensive courses in terms of both “what we teach” and “how we teach.” This project will augment and refine the Caselet practice tool in three dimensions to support scalable deployment and adoption through an iterative design and test framework. The research team will enhance the Caselet tool with new features, to be piloted and tested by up to 1000 students drawn from three graduate programs over a three-year period at the University of Maryland Baltimore County, a minority-serving institution. The project will focus on three tasks to address scale-up challenges. The first task will explore the approach to help scale up the authoring of Caselet using Large Language Model (LLMs). This approach aims to expedite the authoring process by identifying appropriate case studies and drafting relevant questions and explanations before submitting them for expert review. The second task aims to scale up the cognitive skills assessment in data science problem solving using machine learning models to track students’ skill mastery at a refined level of precision. The third task will focus on the scalable assessment of metacognitive competencies related to data science problem-solving through multichannel multimodal data collection in controlled lab environments and course-based and self-paced settings. Along with technology development, the research team will conduct pilot studies among UBMC graduate students from three different programs in various educational contexts, including online vs. in-person, instructor-led, or self-paced. In addition to the research findings, a guidebook will be created to support the adoption of Caselet by students and instructors from other educational institutions. The findings and pedagogically enhanced Caselet and associated data science problems stemming for this project will be disseminated to graduate-level faculty across UMBC, UCF, and other partnering institutions as well as scholarly conferences. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader 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 2024 · 2024-10

Data-intensive scientific research projects often involve multiple collaborative parties. Some parties may demand confidential processing of their sensitive assets to protect intellectual property, embargo data (or algorithm) sharing before publishing a paper, conform to legal requirements, or avoid the responsibility for releasing sensitive data. However, integrating confidential computing into scientific workflows raises significant challenges. (1) Most science domain developers find it challenging to learn specific confidential computing frameworks and secure their code to protect from side-channel attacks. (2) The interplay between the private components and other components in a collaborative workflow may enable new attacks and side channels for adversaries to explore. The proposed project aims to address these challenges with a scientist-friendly development framework for confidential computing and a holistic attack study and mitigation framework for collaborative workflows. The success of this project will enable domain scientist developers to adopt the best confidential computing practices easily and use publicly available resources without the concern of confidentiality and privacy breach, boosting the idea of open, collaborative science. Specifically, the proposed research focuses on the scientist-oriented trusted-execution-environment (TEE) based development and studies its integration with collaborative scientific workflows. (1) The project explores different protection and usability solutions for domain scientists and allows them to tradeoff between their research goals and security and privacy concerns. (2) It develops an efficient and transparent TEE access-pattern protection framework that uniquely combines the best practices in data-intensive computing and framework-based mitigation methods. (3) It takes a holistic approach to study new security and privacy threats around confidential components in a collaborative workflow, covering stages including task execution, logging, provenance analysis, and reproducibility verification. The solutions will integrate techniques like TEE, blockchain, and differential privacy. (4) It is science-driven, motivated, and validated by collaborative research projects in biomedical sequence processing, image-based remote diagnosis, and healthcare data analytics. This project will generate open-source toolkits and demonstration systems. It also includes several educational and outreach initiatives to enhance cybersecurity and data science programs, attract underrepresented students, help local high school CS education, and strengthen industrial collaborations. 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.