Purdue University

NSF Awards · FY 2025 · 2025-03

The conference "Enriching Statistical Inference with Artificial Intelligence" will be held at Purdue University May 12-14, 2025. During the past decade, deep learning has revolutionized data science, with transformative applications in fields such as computer vision, protein structure prediction, and natural language processing. These advancements underscore the immense potential of deep neural networks (DNNs) while revealing a gap in the statistical understanding of their mechanisms and successes. This conference seeks to address the gap by fostering a vibrant platform for exchanging ideas, advancing statistical theories to illuminate DNN performance, developing innovative artificial intelligence (AI) tools, and promoting interdisciplinary collaborations that harness the power of AI to solve real-world problems. This conference will significantly enrich statistical inference with AI, while also contributing to the evolution of AI by improving its robustness, interpretability, and uncertainty quantification. This improved inference will then impact society broadly by improving the experience of users interacting with the products of AI. This conference will delve into cutting-edge research at the intersection of AI and statistical inference. Key topics will include investigating fundamental phenomena in deep learning, such as benign overfitting, and developing robust methods for uncertainty quantification in DNN models. The conference will also emphasize practical applications, leveraging DNNs to address foundational scientific challenges like causal inference and variable selection in complex systems. Participants will acquire state-of-the-art AI tools to tackle the complexities of contemporary data science while contributing to the advancement of modern statistical theory. More information about this conference may be found at https://www.stat.purdue.edu/news/2024/bff9.html. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This grant provides funds to support students for travel to the International Conference on Software Engineering (ICSE 2025), which will take place in Ottawa, Canada in April, 2025. ICSE is the flagship conference in the field of Software Engineering. The grant will provide travel and registration support for US-based students. The ICSE conference this year will have a doctoral symposium, student mentoring workshop, and a new faculty symposium. Conference attendance is important for the technical exchange of information and research conversations/collaborations made possible by the conference, as well as advances in the field made possible by these interactions. The conference provides opportunities for education, training and mentoring to build the next generation of researchers and practitioners in the field of software engineering. The international nature of this conference helps develop a globally-aware workforce of research and educators within the US and helps build the community of researchers in the field of Software 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.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY Salmonella enterica serovar Typhimurium is a widespread human enteric pathogen that has a broad range of animal hosts and environmental reservoirs. Meanwhile, the German cockroach, Blattella germanica, is the most common pestiferous cockroach species in human environments. B. germanica frequently harbors Salmonella spp. in nature, serving as both an environmental reservoir and a vector. Transmission of S. Typhimurium by cockroaches has been previously described as a passive, non-replicative process by which the bacteria are mechanically transferred from one surface to another. However, our laboratory recently demonstrated that S. Typhimurium actively colonizes the digestive tract of German cockroaches after being ingested. Colonization of the cockroach is characterized in part by an initial population bottleneck during which most ingested bacteria are eliminated. Subsequently, the surviving population of S. Typhimurium undergoes replication and persists in the cockroach gut, interacting with a highly diverse microbiota. The bacteria that persist are disseminated in the feces into the environment where they can be ingested by other cockroaches via coprophagy and/or acquired by a vertebrate host. The central hypothesis of this project is that cockroaches provide a unique environment for S. Typhimurium to undergo significant evolution as a result of both selection pressure from the host and interaction with its gut microbiota. Specifically, we hypothesize that evolution in the gut of the cockroach vector may favor the emergence of novel variants of S. Typhimurium with 1) enhanced ability for vector-borne transmission, 2) altered potential to colonize other animal hosts, and 3) antimicrobial resistance horizontally acquired from diverse constituents of the cockroach gut microbiota. We will pursue two specific aims to test our hypotheses. In specific aim 1, we will use whole genome sequencing to identify the adaptive genotypic changes that occur in S. Typhimurium after experimental evolution in a laboratory cockroach strain and we will examine the effects of these changes on fitness in the vector. In specific aim 2, we will determine if S. Typhimurium can horizontally acquire antimicrobial resistance from commensal bacteria harbored in the gut of field collected cockroach strains, and we will explore the mechanisms by which this may occur. Together, these studies will provide novel, naturally relevant insight into how underappreciated insect reservoirs may contribute to evolution of S. Typhimurium and may implicate insect control as a simple intervention to mitigate pathogen evolution.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY Dysfunction of either of the two phospholipase C gamma family members, PLCγ1 and PLCγ2 have been implicated as driving oncogenesis through activating mutants or oncofusion protein-induced upregulation. Additionally, PLCγ1 has been identified as an interaction partner with PD-L1, suggesting PLCγ1 may be a synergistic target for PD-L1 immunotherapies. While modulation of these two enzymes is a tantalizing target for a variety of cancers, unfortunately, no specific, potent inhibitors of PLCγ1 or PLCγ2 are known. One of the reasons for this is that HTS-amenable substrates have not been readily available to screen PLCγ1 or PLCγ2. Our lab has developed a novel fluorescent substrate, called C8CF3-coumarin, and currently has rapid synthetic access to up to multiple grams of the substrate. Additionally, our lab has worked with a chemical supplier to make an additional substrate, XY-69, commercially available. This substrate is incorporated into a liposome and for this reason it better mimics the enzymes' natural membrane-bound PIP2 substrate. To identify small molecule inhibitors of PLCγ1 and PLCγ2, we will employ the already developed and optimized HTS-amenable solution assay using our new C8CF3-coumarin to screen 200,000 compounds against both PLCγ enzymes. Primary hit molecules will be retested in the same assay conditions to confirm their activity. After eliminating any known promiscuous binders and compounds that interfere with the assay, these hits will be confirmed using the now commercially available XY-69 substrate in a liposomal assay. The confirmed hits then will undergo further testing to determine their dose-response, mode of action, and activity in other orthogonal assays including biophysical interaction and cellular assays. Compounds that advance through all of the orthogonal testing workflow will be designated as validated hits and an apparent path for future development will be determined by performing a preliminary structure-activity-relationship (SAR) study. Chemical scaffolds with a promising forward path will be designated as validated hit scaffolds. These validated hit scaffolds are the primary outcome of this project and will result in chemical matter that can be further developed into useful chemical probes or potential therapeutics upon further investment. To successfully identify validated hit scaffolds, the following specific aims will be employed: Specific Aim 1 – Identification of small molecule inhibitors of PLCγ1 and PLCγ2 via already developed and optimized biochemical high-throughput screening assays Specific Aim 2 – Validation of identified confirmed hit molecules via orthogonal assays Specific Aim 3 – Generation of preliminary structure-activity-relationship studies to determine the presence of a promising forward path towards the generation of chemical probes and/or therapeutic leads

NSF Awards · FY 2025 · 2025-01

The broader impact of this I-Corps project is the development of robust and explainable artificial intelligence (AI) tools for detecting deepfakes. Deep Fakes or AI-generated images, audio, and videos mimicking real people have emerged across various sectors, posing significant threats. The deepfake AI market is expected to undergo significant growth, rising from a value of $564 million in 2024 to an impressive $5,134 million by 2030. This technology is designed to meet the needs of industries highly vulnerable to deepfake threats. Journalists and news organizations require reliable tools to verify content authenticity, preserving credibility and public trust. In media and entertainment sectors, this technology protects intellectual property and ensures content integrity. The finance sector benefits from enhanced fraud detection, while cybersecurity firms can use the developed tools to strengthen defenses against sophisticated cyber-attacks. By providing robust and explainable AI tools, this project enhances detection accuracy, builds public trust, and holds significant commercial potential. 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 generalizable deepfake detectors by focusing on extracting common forgery features from various deepfakes. These features will be used to train a highly accurate deepfake detector, enhanced with robust approaches to improve detector reliability. This robust detector will be integrated with physical and physiological-based explainable methods to provide clear explanations for detection results. To achieve this, a comprehensive library of high-quality, reusable physical/physiological-based models is built. The library makes these methods easily accessible with readable, usable, and maintainable code. To simplify the end user's understanding of detection results, large language models are integrated into the system to provide concise textual explanations and reports. 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

Nontechnical description The demand for innovative semiconductors is growing in response to technological progress across various sectors. There is a need for semiconducting materials with superior optoelectronic properties, which are composed of earth-abundant elements, are easy to synthesize, and are stable under operation. Such materials will have an impact on the design and fabrication of diverse semiconducting electronic devices, including solar cells, light-emitting diodes, thermoelectrics, detectors, and thin-film transistors. Over the past two decades, an exciting class of new organic-inorganic lead halide perovskite semiconductors with exceptional optoelectronic properties have emerged. However, the poor air, moisture, light stability, and Pb toxicity of the lead halide perovskites have limited their wide-scale use. In response, a flurry of recent theoretical screening searches has identified alternative inorganic chalcogenide perovskites with similar crystal structures as lead halide perovskites and with a potential for equally promising optoelectronic properties. These Chalcogenide perovskites are particularly attractive as they are composed of earth-abundant elements and would be amenable to use on a large scale without material supply limitations. However, to date, methods to synthesize these materials as thin films at temperatures typically employed in the device fabrication are not available. The project aims to make such synthesis and fabrication methods for chalcogenide perovskite nanoparticles and thin films available, to enable the study of their material and optoelectronic properties and contribute to their widespread use, especially in solar cells. The project aims to fulfill the need for a chalcogenide perovskite database with experimental material and optoelectronic data along with experimentally validated models and tools to help in the tailoring and discovery of useful semiconducting materials for specific device applications by the material community. Furthermore, training graduate and undergraduate students in integrative research and education provides them with cross-disciplinary skills essential for developing innovative solutions as they contribute to U.S. leadership in the burgeoning field of semiconductors and electronic devices. Technical description To date, the synthesis of device-quality chalcogenide perovskite thin films at temperatures below 600 °C is unavailable. In this project, solution syntheses based on organometallic chemistry at manufacturable temperatures of below 600 °C and the ability to tailor thin film with the desired optoelectronic properties and device performance are being developed for the rich material space of chalcogenide perovskites ABS3 (A=Sr, Ba; B=Zr, Hf) especially BaZrS3. The project seeks to provide a new understanding of early transition metal-chalcogen bonding and ligand chemistry in molecular precursors, solvents, and reaction sequences during subsequent processing steps for the synthesis of an entire class of chalcogenide perovskite materials and their nanoparticles and thin films. The ability to tune optoelectronic properties through composition control is also being developed. Design insights for efficient light-harvesting solar cell assemblies promise to generate knowledge for efficient charge generation and transport over macroscopic distances across multiple interfaces within devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Daylight and window views provide comfort, health and well-being. However, children with Autism Spectrum Disorder (ASD) are often hypersensitive to environmental stimulation, which leads some schools for ASD children to keep windows covered to block natural light and avoid potential distractions. Guidelines for lighting in these schools could help improve the sensory behaviors and health of children with autism. This project will (i) discover evidence linking daylight and behavioral responses in autism educational settings, and (ii) develop and implement guidelines for inclusive indoor environments while harnessing the benefits of natural light for the children. The broader impacts of this project are: (i) to integrate knowledge from different disciplines, train cross-disciplinary students and provide a breakthrough in understanding the impact of daylight on behavioral response of ASD children; (ii) to implement findings in autism educational facilities in collaboration with local teachers and stakeholders; (iii) to create educational materials that motivate teachers, parents and caregivers to establish preventative protocols for outbursts and encourage healthy behaviors for ASD children with respect to daylight; and (iv) raise awareness in the autism community for using natural light to improve the quality of life for children with autism. The project will transform the visual environment design and operation in educational environments for children with ASD. Through a pioneer framework of experimental methods, computational predictive models and evidence-based reverse engineering operation, this research will establish a new paradigm and build the foundation for a universal platform to optimally design and control daylight for this sensitive population. First, a focused experimental study will be conducted with 50 children with ASD in well-designed educational settings under real daylighting conditions. A flexible sensing network will be deployed to monitor and process pixel-wise information of dynamic luminance distributions. At the same time, seven behavioral aspects and sensory profiles will be assessed along with the children’s task performance capabilities. The data will be used in probabilistic and machine-learning-based models, to predict and classify dynamic behavioral responses to daylight-induced stimulation conditions. In turn, a generalized daylight simulation approach coupled with reverse engineering operation and optimization will translate these findings into comprehensive daylighting design and operational guidelines implemented in real autism schools in collaboration with teachers, parents and autism community stakeholders. These engineering designs are expected to refine daylighting best practices for ASD children to improve sensory behavior, minimize outbursts, and provide better everyday life for this population. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project explores the gaps in knowledge, skills, and experiences that students may need to gain outside formal learning environments in computer science education and seeks to understand how these gaps impact students' success. By evaluating students' success as they navigate both formal (classroom) and informal curricula (e.g., makerspaces, internships, extracurricular clubs), we will develop learner-centered solutions to support their understanding of computing concepts and their gain of skills. The significance of this project lies in its potential to make CS education more comprehensive. In addition, this project addresses growing impact of artificial intelligence (AI) in education by examining the relationship of exposure to AI (e.g., large language models) outside of the class and student success in programming environments in the classroom. Our findings will benefit society by understanding and improving the educational experiences of all students and enhancing their success in computing programs. The three-year research program will investigate gaps in CS education through three primary strands: (1) identify factors from the formal and informal curricula, which when not available to students, could pose risks to students' mental health, such as anxiety, depression, and the impostor phenomenon; (2) study students' interactions with programming environments and large language models (LLMs) outside of class to characterize effective scaffolding strategies and address technical challenges in the classroom; and (3) evaluate the impact of makerspaces on students' creativity, exploratory skills, and sense of belonging. The project's methodology combines qualitative ethnographic methods, participatory design, and quantitative experimental and quasi-experimental approaches. This project will emphasize the experiences of all students in computing, aiming to create robust learning environments. The research will provide valuable insights and guidelines to improve CS education, ultimately reducing dropout rates and enabling students' success. 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

Subduction zone volcanoes occur where one tectonic plate goes beneath another. Many millions of people live near subduction volcanoes. This means that understanding subduction volcanoes and the hazards they present is important. Magmatic activity at a volcano is usually studied using methods from geophysics. One such method is monitoring how volcanoes change shape (volcano deformation) over time. Geologists can also study igneous rocks, which form from magmas, to learn about volcanoes and magmas. Extrusive igneous rocks form from magmas that erupt from a volcano. Intrusive igneous rocks form from magmas that crystallize beneath the Earth's surface. For this project, the research team will study an ancient subduction zone volcano in Washington where they find both types of igneous rocks. They will reconstruct the record of volcanic eruptions and subvolcanic intrusive activity. To do this, they will study the geochemistry, geochronology, and petrology of the rocks. They will use these data to understand three things. First, they will determine when the magmas formed and if the erupted magmas and intrusive magmas existed at the same time. Second, they will determine how the composition of the magmas changed through time. Third, they will determine how deep the magmas were beneath the Earth's surface. The research team will also make videos about the motivation, importance, and results of their research. They will work with Professor Nick Zentner (at Central Washington University) to make these videos. The research team will also work with the Indiana School of the Deaf to produce new lab exercises for their high school science courses. The ancient volcano that will be studied lies just to the north of Mount St. Helens and includes the upper crustal Oligocene Spirit Lake Pluton and surrounding volcanic rocks. It represents a deeply eroded portion of the ancestral Cascade Arc and was the focus of detailed 1:24,000-scale geologic mapping by the USGS in the 1980s and 1990s. Existing geo- and thermochronology constrains the duration of pluton emplacement to <1.5 Myr and demonstrates that eruptions of the surrounding volcanic rocks pre-dated, were coeval with, and post-dated the pluton. Existing whole rock geochemical data show a compositional range from quartz diorite to granite in the pluton, and a range from basalt to high-silica rhyolite in the volcanics. This research aims to produce a detailed chronological and geochemical record of pluton construction and associated volcanism to better understand when the intrusive rocks were emplaced, if intrusive activity affected the rate and/or style of volcanic eruptions, and if any of the erupted magmas were derived from the pluton. This project will produce a detailed timeline of events using high-precision U-Pb zircon geochronology for 15 samples each from the pluton and the associated volcanic section. These data, along with geochemical and textural observations, will allow the team to answer questions such as: 1) Was there a long-lived magmatic mush within the now solidified plutonic complex? and 2) Did emplacement of the pluton lead to changes in eruption style, composition, or rate? Geobarometric data will allow the team to directly test whether any volcanic eruptions were sourced from the same depth as the currently exposed pluton. Taken together these geochronologic, geochemical, and geobarometric datasets will offer a holistic record of how the magmatic system evolved over the lifespan of a single arc volcano. 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

Quantum computers, with their potential to revolutionize computation, are on the brink of addressing problems that are currently unsolvable by classical computers. Their unique ability to simulate molecular and atomic behavior for understanding material properties would enable researchers to accurately predict characteristics. However, the current utilization of quantum computing in materials science and chemistry is limited to a few researchers. For utility-scale quantum computing, it will be imperative to train the next generation of materials scientists and chemists to harness the power of quantum computing. This will open new avenues for envisioning and discovering materials, including those beneficial for quantum bits (qubits), the fundamental units of quantum information. The development of a diverse, quantum-ready workforce is a key priority in the National Quantum Initiative. It will prepare more individuals for jobs in quantum information science and engineering (QISE), enhance STEM education at all levels, accelerate the exploration of quantum frontiers, and expand the talent pool for future industries. This pilot project is creating a quantum computing training curriculum for undergraduate, graduate students, and researchers in materials science and chemistry. The curriculum covers quantum computing fundamentals, quantum simulations, and quantum machine learning utilizing both IBM quantum hardware and NVIDIA CUDA Quantum GPU-based simulators powered by high-performance computing (HPC), along with classical and quantum simulation tools for atomic-scale structures and material properties. Each summer, the team will host an in-person training workshop at either Purdue University or Arizona State University. Additionally, the project will provide virtual training tutorials throughout the academic year, reaching participants via NSF's ACCESS (Advance Cyberinfrastructure Coordination Ecosystem: Service & Support) program and other channels. The project offers multi-faceted impactful benefits, particularly in its outreach and educational initiatives. It will significantly enhance diversity in the QISE field by specifically targeting underrepresented undergraduate and graduate students, as well as researchers from Minority Serving Institutions (MSIs), including ASU. The project also includes a public forum as part of its summer schools, serving as an important platform for open discussion and knowledge exchange. The project also catalyzes the spread of QISE knowledge as trained researchers and students teach their communities, furthering the science beyond material science and chemistry applications, and an NSF ACCESS affinity group will promote sustained collaboration and growth in QISE. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

This proposal investigates the underlying mechanisms of the medial olivocochlear (MOC) efferent system, focusing on its role in anti-masking and the impact of hearing loss on its input and output pathways. To leverage the potential benefits of the MOC system in hearing aids, it is essential to study the specific neural mechanisms within the MOC system and how they are altered by hearing loss. The MOC system dynamically adjusts cochlear gain based on multiple input pathways, including two major inputs from the inferior colliculus (IC) and the cochlear nucleus (CN). While the CN input has been extensively studied, the IC input remains less explored. IC cells in the midbrain are sensitive to low-frequency fluctuations (modulations) in auditory-nerve (AN) responses, potentially conveying spectral information encoded within these fluctuations. Computational modeling from my PhD thesis showed that this distinct information provided by the IC input to the MOC system can explain auditory phenomena that cannot be fully accounted for by considering only the CN input. The complexity and lack of detailed physiological data on MOC inputs, particularly from higher-level projections such as the IC, underscore the need for innovative physiological methods and approaches. To address this issue, this proposal outlines three specific aims, each involving novel physiological methods to isolate and manipulate individual MOC pathways. The goal is to create a comprehensive dataset that significantly enhances our understanding of the MOC efferent pathways. In Aim 1, we will explore the role of the IC input to the MOC system by varying modulation frequency within a forward-masking paradigm, while simultaneously recording transient-evoked otoacoustic emissions (TEOAEs) and envelope following responses (EFRs) to track MOC-induced changes in cochlear gain and neural responses, respectively. This will provide a detailed evaluation of the MOC efferent system, focusing specifically on the role of the IC input. Aim 2 will investigate the relative role of the CN input to the MOC system by using a temporary threshold shift (TTS) noise exposure animal model of cochlear synaptopathy, which will isolate the wide-dynamic range CN pathway while leaving the IC input relatively unaffected. Finally, Aim 3 will examine the effects of sensorineural hearing loss (SNHL) on MOC output projections to the outer hair cells (OHCs) by measuring how neural coding is influenced with and without efferent electrical stimulation in both hearing-loss and normal-hearing animals. I have developed a computational model of the MOC efferent system that integrates both CN and IC inputs during my PhD, which guides the design and interpretation of our measurements. The proposed experiments will significantly advance our understanding of the MOC system’s role in listening in noise and the broader effects of SNHL. These findings will have implications for enhancing hearing-aid algorithms. The physiological training gained through these aims will synergize with my computational background to provide a strong foundation for my career as an independent researcher exploring translational issues related to listening in noise.

NSF Awards · FY 2025 · 2025-01

The diffraction limit, a physical phenomenon caused by the wave nature of light, fundamentally constrains the highest resolution of imaging systems, including cameras, microscopes, and telescopes. Surpassing the diffraction limit is accordingly one of the most consequential problems faced by imaging systems today, and doing so could impact every aspect of our lives, from science to medicine, and from engineering to national security. This project will explore a new potential solution to overcome the diffraction limit using a novel optical element, metasurfaces, in conjunction with engineered data-processing computation. A metasurface is a flat, thin glass substrate coated with nanoscale transparent structures that bend the light that travels through them in specially designed ways, and preliminary studies suggest the possibility of using a metasurface along with jointly designed data-processing computation to achieve resolution higher than the diffraction limit. The project will carry out a comprehensive study of the proposed imaging solution from both the theoretical and experimental perspectives. It is anticipated that the project will provide a breakthrough in improving the resolution of imaging systems without needing a rigorously controlled environment, immediately opening the door to a broad range of scientific-imaging applications for disease studies, vaccine development, cancer diagnosis, cell discoveries, material manufacturing, and national security. The computational-imaging knowledge developed during this project will be integrated into K-12, undergraduate, and graduate courses. This project aims to develop a passive imaging solution, angular encoded imaging, that can overcome the diffraction limit without manipulating the illumination of the scene. Angular encoded imaging modulates the environmental light of every incident angle with a unique point-spread function such that the resulting imaging process is mathematically equivalent to a passive band shifting that shifts the high spatial-frequency components of the environmental signal to within the measurable spatial-frequency band controlled by the diffraction limit. Preliminary results show that passive band shifting is possible using metasurface technology, enabling wavelength-scale engineerable optical modulation. This project will establish the theoretical and computational foundations of angular encoded imaging as well as design and build the first angular-encoded-imaging prototype to demonstrate passive sub-diffraction-limit imaging. 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

Demand for spectrum has increased tremendously over the last decades, making the available spectrum a scarce and valuable resource. However, for a variety of reasons, spectrum has traditionally been managed in an inflexible manner, a situation that exacerbates this scarcity and results in undesirable behaviors of those that have access to spectrum. This creates a potential to stifle the incredible innovation and economic impact that wireless systems have created in the last 40 years, which presents a risk to the economy and potentially could result in disruptions to critical communication, radiolocation, public safety and defense services. To move forward, a radically more flexible paradigm is needed. And while the last few decades have seen great innovation in radio design and even in spectrum policy, the current inflexible management model has hamstrung everything. This project proposes a vision of a new spectrum era that relies on adaptivity and reconfigurability as its core elements. Adaptivity and reconfigurability must be used from the components on the device all the way through to the policies that govern the interactions of many systems. This concept can be envisioned similar to driving on roadways. Protocols and policies (road rules) are needed that enable real-time spectrum sharing in a mutually beneficial way for all involved users. A Spectrum Sharing and Management System, paralleling law enforcement officers and traffic signals, must be developed for real-time coordination between devices in more congested scenarios where collisions are likely if autonomous operation remains. Devices, circuits, and systems are required that can reconfigure their systems to automatically optimize performance upon changing operation to different spectrum. This proposed project will be the first demonstration of the capacity to directly map a set of desirable system metrics (e.g., frequency, bandwidth, beam pattern, power, impedance, etc.) to a nearly arbitrary layout of light-activated plasma pixel antenna radiators. The key enabling technology is a semiconductor plasma pixel phased array with the unprecedented ability to create nearly-arbitrary-shaped radiators, including the elimination of the radiators altogether. The key attributes include: (a) a pixel-by-pixel adaptive geometry using light-activated plasma pixel radiators, (b) reconfiguration at high speed, high linearity, and with high power handling, (c) ‘painting’ the system-informed radiator profile over a fixed feeding network, and (d) fast optimization approaches informed by Artificial Intelligence (AI) and Machine Learning (ML) to create array configurations mapped to desired radar resolutions, system performance, and dynamically imposed spatial-spectral limitations. The primary objectives include the following: (1) develop the plasma phased array focusing on feed network solutions, and (2) develop fast AI/ML supported reconfiguration algorithms to enable the integrated system performance-to-radiator mapping in real time. Broader outcomes of the proposed project include: (1) wireless and spectrum engineering education for high school students, (2) presentation of results at workforce development events including the NSF-funded “Spectrum Sizzle” Undergraduate Spectrum Workshop, (3) contribution to workshops and special sessions at international conferences engaging spectrum stakeholders, (4) integration of the multidisciplinary research topics and findings into microwave and computational intelligence courses, and (5) a commitment to promoting diversity in research teams and outreach programs to underserved schools to foster interest in STEM related fields. 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

Yeasts are important in industry, impact human health and food safety, and play critical roles in ecosystems. Yeasts’ simple body shape, and unique foraging strategies enable them to colonize habitats where few other organisms can survive, and where few scientists tend to search for fungi: such as within lichens, in streams and pools, and in high salt environments. It is estimated that scientists have discovered only 1% of the yeast species thought to exist on earth. This gap in knowledge of yeast diversity and distributions makes it difficult to understand the evolutionary history of the fungal tree of life, including mushrooms, medicines and symbionts upon which human livelihoods rely. Systematically collecting and describing these yeasts facilitates accurate identification of pathogens, and of the cryptic biological diversity comprising “microbiomes” that reside inside plants and animals. This project will focus on isolating and describing yeasts in the Basidiomycete class Pucciniomycotina, which are only distantly related to baker’s and brewer’s yeasts, and much less studied. The research will leverage the environmental diversity of Hawaiʻi, and the evolutionary diversity of zoo animals to maximize recovered yeast diversity from plant, animal, and environmental samples. The project will use a combination of genome sequencing, physiological data, and culture characteristics to publish formal descriptions and phylogenetic analysis of hundreds of novel species. The research will assess whether, and to what extent, yeasts co-evolved with their animal hosts. New insights into yeast diversity will be used to predict global species diversity, host/habitat specificity, and diversity hotspots. The project will increase the participation and research capacity of underserved groups (particularly Native Hawaiian and Pacific Islanders) via support for postdoctoral and student researchers, and formal training opportunities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The size of a tropical cyclone (a.k.a. hurricane) leading up to landfall determines the footprint of its hazards, including wind, storm surge, inland flooding, and tornadoes. Hence, understanding how storm size responds to warming is important for both forecasting storm impacts and for assessing long-term risk. Tropical cyclones have been shown in observations and models to be intensifying more rapidly with warming, which makes forecasting and emergency planning more difficult. Could the tropical cyclone wind field expand more quickly in a warmer world, too? This outcome is predicted by recent theory, but it has yet to be tested in observations or real-world models. This represents a critical gap in our knowledge that is directly relevant to future changes in societal risk, as more rapid expansion rates would make storm impacts even more difficult to forecast and mitigate in the future. The outcomes of this work will help improve forecasts of tropical cyclone hazards needed for communities to prepare and evacuate ahead of a landfalling storm, and it will help improve our understanding of how the risk of impacts may change regionally as the climate warms. This project systematically investigates how the expansion rate of tropical cyclones on Earth depends on sea surface temperature in three settings: 1) in historical data; 2) in existing climate model simulations under both real-Earth conditions and idealized aquaplanet conditions; and 3) in simplified limited-area cloud-permitting model simulation experiments. The first two tasks quantify the dependence in the real-world, including both global-mean warming and regional warming relative to the mean. The third task more carefully tests the underlying physics of the outcomes of the first two tasks to raise our understanding of the fundamental mechanisms of tropical cyclone size expansion under climate warming. This work seeks to bridge the gap between theory and the real-world need to better forecast tropical cyclones and their impacts. The project explicitly foregrounds real-world observational and climate-model data in order to make the results as usable to real-world interests as quickly as possible. 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

In recent years, topological data analysis (TDA) has evolved as an emerging area in data science. The technique works by extracting hidden connectivity among the data points dictated by topology. This information is more global in nature and thus become a robust signature for data. Over the last two decades, this technique has been studied from various angles and has been used to learn from data in a wide variety of applications. However, commonly used algorithms in TDA have been limited to data that vary by a single parameter. To handle more complex and diverse data, extensions of the original notions have started to appear in the TDA literature. Although a great deal of mathematical theory behind these extensions got developed, the same cannot be said about the algorithmic advances. The main thrust of this project is to address this gap. The resulting TDA methodologies can complement and augment traditional data analysis approaches in fields such as machine learning and statistical data analysis. The educational aspect of the project will be enriched by the synergy between mathematics and computer science. Graduate students supported by the project will be trained to develop skills in mathematics and theoretical computer science, most notably in algorithms and topology, write efficient and usable software, and analyze real-world data sets. It is planned to provide best practice in recruiting and mentoring students from underrepresented groups. The research engagement will be broadened via workshops or tutorials. Software tools will be developed for prototypical uses by the research and industrial community. This will not only equip the participants in the project with the twin skill at developing theories alongside coding but also enhance the use of TDA in applications. Although traditional topological data analysis (TDA) techniques involving 1-parameter persistence is well understood from both mathematical and algorithmic point of view at the moment, the same is not true for its higher order or more general extensions. We propose to study three such extensions in this project, namely, (I) Zigzag persistence which allows not only additions but also deletions in the simplicial filtrations, (II) Multi-parameter persistence which allows more than one parameter akin to multivariate analysis, and (III) Combinatorial dynamical systems persistence which allows merging the theory of persistence with the theory of discretized vector fields. Each of these three areas is in a stage where mathematics has advanced to a point where more algorithmic ideas are required to bring the mathematical theory to practice. The investigator has actively worked on the algorithmic aspect of TDA and has made significant contributions in that direction. Time is ripe to strengthen this effort to the three extensions mentioned with the overarching goal of developing actionable and practical tools. The geometric and topological ideas behind the proposed work represent novel directions and inject new ideas and perspectives to the important field of computational data analysis. In particular, the effort is likely to inspire novel mathematical concepts alongside algorithm designs to address various challenges appearing in the aforementioned topics. 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

The broader impact of this I-Corps project is the development of a new laboratory management system that combines essential lab functions into one easy-to-use tool. As labs generate more data and use a variety of tools to store and manage their data, this system simplifies the process. The technology allows lab personnel to keep their data and notes in one place and makes it easier for them to stay organized. Further, this system can automatically accumulate, upload, and back up data, foster collaboration among lab members, and monitor the lab environment in real-time, so lab directors can keep their labs running smoothly and keep on top of progress even when they are busy with other responsibilities. By using game-like features, the system rewards staff for following lab recording guidelines, maintaining lab inventories, and completing lab maintenance tasks, encouraging everyone to enter data completely and accurately. This technology can help labs run more smoothly and produce better research, setting a new standard for lab operations, improving the reproducibility of science, and increasing public confidence in scientific work. 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 a system specifically designed to meet a lab's needs by combining data collection, lab monitoring, and team collaboration tools in one connected platform. It links all data points, from experimental results to lab conditions, so that lab staff and managers get a complete view of lab operations. This system is flexible enough to meet the unique needs of each lab and makes it easy to track data over time and across projects. Early testing shows that it can handle large amounts of data efficiently, with automatic updates and feedback, which makes data collection faster and more consistent. This technology promotes clear and reliable lab work, helping labs work together more easily and enabling new scientific discoveries through improved teamwork and data-sharing. 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

The broader impact/commercial potential of this I-Corps project is the development of a robotic educational platform to engage students in assistive and rehabilitation robotics learning experiences early in their education. Rehabilitation and assistive robots are becoming a necessity for the rapidly growing elderly population and their caregivers, and industrial collaborative robots (cobots) are increasingly being used to augment the skills and physical capabilities of the workforce. The proposed educational system (pedagogical platform and lesson plan) is designed to provide engineering education material aimed at teachers of engineering electives. It is appropriate for 8th and 9th grade students (approximately 8 million in the United States) who are taking engineering by design and foundation of technology courses as well as upper grade students (10th-12th) in subsequent tech elective classes. The proposed technology allows students to learn about robotics and assistive technology devices that aim to amplify or compensate for human capabilities. In addition, the platform and associated curriculum enable teachers to meet multiple objectives of educational standards by leveraging interdisciplinary projects that aim to improve quality of life. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a neurally controlled manipulator. It is a two-degree of freedom robotic arm used to teach robotics and collaborative robotics principles, primarily in secondary education technology classes. Through this platform, students learn to assemble a robotic arm, microcontroller, sensors, and software to mirror the characteristics of a human arm. In addition, students learn to command their intention and move the manipulator using their muscles' surface electromyography signals (sEMG) as the biomechanical metrics. The proposed technology also includes a curriculum that describes each lesson, multiple hands-on classroom projects, and optional bonus activities for students wanting to explore further. This platform may allow students to design, build, and experiment with readily available lightweight and durable components. Its interdisciplinary modular nature accommodates various hands-on experiential learning activities to teach core science and engineering concepts. 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 aims to revolutionize live-cell imaging by harnessing the power of nanodiamond quantum sensing and advanced microscopy. By developing nanodiamond quantum sensors with exceptional sensitivity, this project will study intricacies of life processes with unprecedented detail. This research aligns with NSF's mission to promote the progress of science and serves the national interest by advancing the understanding of fundamental biological mechanisms. While traditional fluorescence microscopy provides valuable insights into cell structures and functions, nanodiamond quantum sensors with ultra-high sensitivity to local electromagnetic fields offer a new dimension to see through live cells and reveal the underlying physical mechanisms of life processes. By integrating nanodiamond quantum sensors with advanced imaging techniques, this project will capture four-dimensional (4D) information, encompassing three-dimensional spatial data and an additional temporal dimension. This has wide-ranging implications, from enhancing cancer immunotherapy through the monitoring of T cell activity to unraveling the mysteries of membrane potentials in cardiac and neuronal cells. Furthermore, this project extends beyond scientific discoveries. It encompasses comprehensive educational and outreach programs, with a particular focus on fostering diversity in STEM fields. By engaging underrepresented minorities in quantum-related studies, this team aims to create a vibrant and inclusive community of quantum scientists and engineers. This project not only supports education but also benefits society at large, offering new avenues for biomedical research and applications. The research will involve the fabrication of scalable nanodiamond sensors with biocompatible interfaces, uniform sizes and shapes, controlled color center densities, and minimal impurities. By integrating optically detected magnetic resonance (ODMR) spectroscopy of nanodiamond quantum sensors with light-sheet microscopy (LSM), this team will achieve high spatiotemporal resolution and low phototoxicity, enabling precise imaging of live cells. The technical approach includes the utilization of machine learning algorithms and image processing techniques to analyze the acquired data and extract valuable insights into the dynamics of live cells. Particularly, this team will apply the developed ODMR-LSM quantum sensing imaging technology to study T cell activity in cancer immunotherapy and measure membrane electrical potential. The synergy between nanodiamond quantum sensing and advanced imaging techniques will deepen the understanding of complex biological processes. The proposed nanodiamond quantum sensing system, with the ability for correlating the ODMR spectroscopy and the spatiotemporal imaging of LSM, allows for revealing 4D live-cell dynamics which have not been studied before. This project will bridge the gap between fundamental quantum science and applied bioengineering and bring quantum sensing into rich applications in biomedical fields. 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

For most electricity markets in the U.S., the marginal cost and carbon emission intensity of electricity generation exhibit opposite diurnal trends: during peak demand hours, the electricity price is high while the carbon marginal emission rate is low due to the operation of costly but less polluting natural gas “peaker” plants. Energy consumers may respond to the conflicting price and emission signals quite differently. Understanding the behavioral heterogeneity in energy use and its impact on sustainability of the electrical infrastructure is of critical importance to accelerating global energy system decarbonization. This project will establish an altruistic game theoretic framework to understand the interplay of an individual’s financial and environmental goals in shaping their energy use behaviors and to evaluate the impact of behavioral heterogeneity on system-level performances of the electric grid. The altruistic game framework models each energy consumer as a partially altruistic entity whose perceived cost is a weighted sum of his/her direct electricity cost and the social cost of energy-related carbon emission. The weighting factor characterizes an individual’s valuation of energy-related carbon emission, which in turn influences their energy use behaviors. Customer behavioral models will be developed from data collected through (1) online human subject tests with a custom designed demand response game and (2) sociotechnical experiments in a multi-family apartment complex within a historic African American community in downtown Oklahoma City. Project goals include: (1) generation of new knowledge on residential customers’ valuation of energy-related carbon emission and its impact on energy use behaviors; (2) development of a statistical behavioral model that characterizes how climate altruism correlates with socio-demographic variables; (3) synthesis of learning-based model predictive control strategies to enable automated demand response; (4) establishment of an altruistic game theoretic framework to facilitate impact analysis of behavioral heterogeneity on financial and environmental performances of the electric grid; and (5) design of distributed and privacy-preserving Nash equilibrium solution algorithms to accommodate distributed decision making for flexible load control in electricity markets. The project aims to increase public awareness of load flexibility and the associated environmental impact through a series of interrelated research, educational and outreach activities. The data-driven predictive control strategies are designed to unlock the residential flexibility potential through technological development, while the game theoretic framework supports design and assessment of demand-side carbon reduction technologies, programs and policies with socio-economic insights. The field experiment will directly engage 50 and indirectly affect more than 300 households in Oklahoma City providing technology solutions to and educating a population that does not usually engage in the early stages of technology adoption. Through collaboration with the community, developers and other partners, the project will demonstrate social drivers that affect large infrastructure systems, with the results potentially scalable and applicable on a national level in the residential sector. Through the education program development, this project will provide opportunities for K-12, underrepresented, undergraduate, and graduate students to acquire cross-disciplinary skills that are critical to addressing future engineering challenges. This project is jointly funded by the CBET Environmental Sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR). 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Claridge of Purdue University will design molecular building blocks that allow precise control over the surface chemistry of technologically important soft materials. The proposed work develops a new approach to on-surface diacetylene polymerization reactions, designing building blocks that exhibit long-range order, and that allow for atomic-scale motion during reaction, greatly improving reaction efficiency. Together these capabilities create large polymers that efficiently transfer to the surfaces of materials with stiffness similar to human soft tissue. Ultimately, these building blocks could enable design and fabrication of surfaces with well-defined chemical instructions for processes ranging from the assembly of materials for solar energy conversion to materials that scaffold the growth of cells to repair injuries. In this project, the progress of a class of surface reactions will be monitored using specialized microscopes that enable imaging at the scale of individual molecules. This information is integrated with larger-scale experimental techniques including fluorescence microscopy, providing a molecular-to-a macroscopic view of the reaction progress. The proposal also includes an assessment/outreach strategy aimed at addressing gaps in national preparation of undergraduate students for STEM careers such as nanoscience, including students from underrepresented backgrounds. This project utilizes striped phases of functional alkyl diacetylenes known to assemble on graphite and other two-dimensional materials as a basis for functionalizing the surface of amorphous elastomeric materials such as polydimethylsiloxane (PDMS) that exhibit substantial nanoscale heterogeneity. In this strategy, the diacetylene monomers are assembled on graphite, photo-polymerized by irradiation with ultraviolet light, then covalently transferred to the PDMS surface using the hydrosilylation reaction that is the basis for PDMS curing. This project focuses on a class of monomers with a bioinspired architecture in which two alkyl diacetylenes are linked through a glycerol or similar core, since these monomers appear to undergo exceptional ordering and reactivity. The first two aims of this project are to understand the relationship between alkyl chain structure and core/linker structure, and polymerization efficiency. The final stage of the project designs monomers with a new core architecture that leads to formation of 2D sheetlike polymers. The new classes of building blocks developed through this project have to the potential to lead to soft material surfaces that carry embedded chemical instructions for applications including wearable electronics, chromatography, and cell culture. 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 · 2024-12

Project Summary / Abstract Plastic waste is a global environmental and health concern. Degradation of plastics results in the generation of micro- and nanoplastics which are currently ubiquitous within the environment and have diverse properties in terms of composition, shape, size, and inclusion of manufacturing additives. Humans are exposed to micro- and nanoplastics through numerous routes of exposure including oral, inhalation, and dermal via their presence within water, foods, and air. Measurable amounts of plastic particulates have been found in human volunteers in various tissues and safety evaluations of representative nanoplastics utilizing cell culture and animal models have demonstrated the potential for toxicity. The consequential biological and adverse health effects associated with these emerging nanoplastic exposures are not well understood. Currently, most toxicity assessments evaluating nanoplastics utilize pristine representative polystyrene nanoparticles which may not represent environmental exposure scenarios and the biological mechanisms induced following exposures. To address this significant limitation, we have developed a novel procedure to reproducibly generate micro- and nanoplastics of increased environmental relevance from common plastic waste materials. Our assessment supports these plastic particles have appropriate physicochemical properties (composition, size, shape, charge) and induce differential in vitro toxicity including cytotoxicity, inflammation, and oxidative stress. This procedure for generating nanoplastics is innovative and allows for the investigation of fundamental and translatable mechanisms of toxicity. Specifically, evidence suggests nanoplastics can adsorb hazardous environmental contaminants on their surface enhancing their toxicity. This adsorption is likely governed by properties of the nanoplastic and the environmental contaminant. Further, following entry into the body particulates associate biomolecules which may facilitate unique cellular interactions and toxicity. Our ability to produce environmentally relevant nanoplastics with differing physicochemical properties allows for the evaluation these nanoplastic-environmental contaminant and biomolecule interactions which may govern subsequent toxicological consequences. Our preliminary data support physicochemical modifications influence environmental contaminant and biomolecule association influencing cellular interactions and responses. This proposal examines the hypothesis that environmentally relevant nanoplastics will induce toxicity dependent on physicochemical variations (composition and size) via modulation of environmental contaminant and biological interactions. The hypothesis will be tested through completion of two main goals: 1) Quantification of nanoplastic interactions with common environmental contaminants and the toxicological consequences; and 2) Determination of nanoplastic-biomolecule interactions modifying cellular responses. These interactions represent key initial regulators and mechanisms governing subsequent cell recognition and toxicity. Completion of the project will generate new knowledge of environmental significance necessary for the understanding of nanoplastic human health effects.

NSF Awards · FY 2024 · 2024-11

Communications and sensing were developed and designed independently in the last several decades, each with vastly different design objectives. Specifically, communications aim to deliver information data to destination(s) in the forward propagation of electromagnetic (EM) waves, while sensing is to extract information of the environment from the backward propagation of EM waves upon reflections of significant targets. In the next generation of wireless networks, it is desirable to integrate both tasks of communications and sensing under a unified framework, which ensures that the EM waveforms of both communications and sensing share the same spectrum and power, thus substantially improving the resource utilization of wireless systems and lowering the implementation cost with shared hardware architecture. This technology is called integrated sensing and communications (ISAC). The major challenge of ISAC is how to interleave different tasks of communications and sensing in the same signal, transmitter and receiver designs. The proposed approach is tested in both software and hardware, and then employed in the context of urban air mobility (UAM). The success of this project will discover new fundamental understandings of ISAC, lead to new ISAC designs with substantially performance improvement over the state of the art, and lower the complexity and cost of numerous ISAC applications, including urban air mobility, autonomous driving, smart agriculture, and Internet of Things in general. The key approach of the project is to consider the ISAC transmitter as broadcasting information to a genuine communication user and a virtual sensing user, and thus seamlessly integrate both tasks in the same signal using various techniques of the information-theoretic broadcast channel framework. The following research tasks are carried out accordingly: Task 1. Identifying and quantifying the fundamental trade-offs between communications and sensing; Task 2. Adopting an information-theoretic broadcast channel framework for efficient designs and implementation, such as dirty paper coding, coded-division multiple access (CDMA), windowed phase shift keying (PSK) orthogonal frequency division multiple access (OFDM). Task 3. Collaboration between communications and sensing that harvests the gains of active feedback across the control plans of ICAS, and results in a joint design that is greater than the sum of its parts. Task 4. Software and hardware testbed and experiments in the context of UAM. This multidisciplinary project develops new fundamental analysis, generic first principle of waveform designs, and concrete algorithms and protocols, which would directly impact the research paradigm of ISAC. The results of this project are incorporated into graduate-level course curricula, and the activities are used to broaden participation of high-school and undergraduate students through pre-college programs and Purdue Vertically Integrated Projects, a campus-wide undergraduate research course, respectively, including providing mentorship and career consultation for participating high-school and college 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 · 2024-10

This project aims to serve the national interest by developing resources and approaches to support teaching and assessing cross-disciplinary skills and dispositions within a computer science degree program. Prior research has shown that cross-disciplinary skills (e.g., teamwork) and professional dispositions (e.g., adaptability and resilience) are crucial in STEM occupations. Yet, fostering and assessing undergraduate success in developing non-technical competencies is challenging and best achieved when embedded across the curriculum. The goal of this project is to work with the Tuskegee University’s Computer Science Department to integrate essential cross-disciplinary skills and dispositions into its computer science degree program as part of a general continuous improvement model. This project is a partnership of a long-standing computer science program of study with a clear vision for continuous improvement and a team experienced in the design of instruction, assessments, and program evaluation. Cross-disciplinary skills and dispositions must be clearly defined in order to be taught and assessed consistently. The project's activities are framed around six goals. First, is to identify behavioral indicators (criteria) and assessment approaches for cross-disciplinary-skills and dispositions. Second, is to incorporate cross-disciplinary-skills and dispositions across the Tuskegee CS curriculum. Third, is to incorporate teaching strategies and assessments of cross-disciplinary-skills and dispositions into Tuskegee CS core courses, based on behavioral indicators. The fourth project goal is to enhance Tuskegee’s longitudinal evaluation and continuous improvement model. The fifth and sixth goals are to develop open education resources and to conduct evaluative research. The project team will develop and share research-based teaching, assessment, and evaluation instruments and protocols, in addition to delivering academic presentations and contributing papers to the computer science education research literature. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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 2024 · 2024-10

Non-technical Description: Advances in photonic materials that generate, process, or detect light can transform diverse areas of science and engineering, including lasers, optical fiber communications, augmented and virtual reality displays, solar energy harvesting, as well as quantum computing and sensing. By rationally engineering the composition and/or structure of materials at various length scales, it is possible to dramatically enhance their optical responses and performance. However, the traditional approach for the discovery and development of new photonic materials relies on trial-and-error and case-by-case explorations, which are often time consuming and ineffective. This project will use advanced artificial intelligence techniques to develop new artificial photonic materials that can be engineered to have prescribed properties and surpass naturally occurring materials. The research seamlessly integrates materials science, photonics, engineering, physics and artificial intelligence. In tandem with research, the team will develop a multi-channel education program to enhance the learning experience of a broad spectrum of the society, and prepare the next-generation workforce and technology leaders. Technical Description: The project aims to accelerate the pace of the discovery, design, and implementation of new engineered photonic materials, particularly photonic metamaterials, with user-defined spatial, spectral, linear, non-linear and quantum properties through a data-driven approach. This approach will consolidate properties of constituent material compositions, their geometric structures spanning atomic length scales to micrometers, and their underlying symmetries and topology. The project consists of three research thrusts, including (1) establishing deep learning frameworks to construct photonic metamaterials with high efficiency and accuracy; (2) integrating information on the tailorable optical properties of the constituent material platform into deep learning models, to benefit the design and development of reconfigurable metamaterials; and (3) investigating hybrid material systems that couple topological photonic structures designed by deep learning with quantum emitters and optical nonlinearities. The team will accomplish the interdisciplinary research by fusing theory, computation, deep learning, materials engineering, fabrication and experimentation in a closed-loop manner. Through the project, new fundamental knowledge and insights about the interdependent relationships among structure, properties, performance, and processing across different scales will be gained. In alignment with the Materials Genome Initiative (MGI), the project will create a comprehensive library of different artificial meta-atoms and meta-molecules and their optical responses, and eventually drive transformative applications of photonic metamaterials for classical and quantum information 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.