Florida International University

NSF Awards · FY 2025 · 2025-08

The use of AI for abuse against persons is a growing threat. Decades of research in computer vision and AI has led to the broad availability of tools that can be misused to distort an image of a person, such as turning a clothed image of the individual into an unclothed image without the person’s consent. Attackers profit from this kind of abuse and use these abuses in a range of cyberattacks including blackmail, extortion, and ransomware. To address this problem, the research team is conducting technical and content analysis of the ecosystem of tools used to create abuse material, leveraging principles from decades of psychological research to deter attackers from creating and viewing this material, and engaging in technical forecasting to predict and stop future attacks. Overall, this project aims to protect Americans by technically characterizing the current and future ecosystem of online abuse attacks and developing sociotechnical mitigations against such attacks. This team takes a defense-in-depth approach to abuse reduction. The project aims to make current and future abuse tools harder to access and use and to decrease attacker’s inclination to use those technologies. They combine approaches from internet measurement and content analysis to scope the severity of AI use in abuse and identify signals that can be used to proactively remove abuse tools and content. They also rely on decades of psychological and communications theory and research to develop effective deterrence systems that target attacker motivations and are delivered at the time of action as a form of secondary intervention. They use a series of computational and human-subjects experiments to upper- and lower-bound future attacks that use AI for online abuse. Together, these efforts will develop a robust set of threat models, forecasts and interventions against online abuse. -- 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 · 2025-06

PROJECT SUMMARY Overview. This dissertation research study proposes to examine epigenetic profiles of cardiac dysfunction in people with HIV (PWH) who use and do not use cannabis. Specifically, we will examine microRNA (miRNA) profiles of cardiac dysfunction, and investigate how cannabis use, and its metabolites (e.g., CBD and THC), may alter these profiles. Significance. HIV-associated non-AIDS conditions such as cardiovascular disease (CVD) are increasingly prevalent among PWH. Evidence suggests that PWH have increased rates of CVD and earlier onset that may be attributable to traditional risk factors (including substance use), chronic inflammation and immune activation, and antiretroviral therapy regimens. Additionally, cannabis use is prevalent among PWH and may be associated with increased risk for cardiac dysfunction. However, scant research has been done to examine cannabis associated alterations in CVD risk among PWH, and even less has examined cannabis use metabolites’ role. miRNAs, which are non-coding RNAs that regulate gene expression, are integral to the development and (dys)function of the cardiovascular system. They may also represent a novel upstream biomarker of pre-clinical CVD risk. However, little is known about how cannabis use, its metabolites, and HIV may alter miRNA profiles and contribute to alterations relevant to cardiac dysfunction. Objectives & methods. Thus, this R36 dissertation research project will leverage extant samples to gain a better understanding of miRNA profiles relevant to cardiac dysfunction in PWH, and investigate how cannabis use (including dose-response associations with cannabis use metabolites) may alter this relationship. Aim 1 will characterize the association of miRNA profiles with cannabis use (versus no cannabis use) in PWH. Aim 2a will examine the association of miRNA profiles with a measure of cardiac dysfunction in PWH who use and do not use cannabis. Aim 2b will then determine the dose-response associations of CBD and THC metabolite levels with miRNA profiles and a measure of cardiac dysfunction among PWH that use cannabis. Samples for miRNA profiling will be used from an ongoing NIDA DP2 Avenir Award that is collecting demographic and HIV clinical information, and a measure of cardiac dysfunction (cardiac MRI with fast-strain-encoded imaging). miRNA profiles will be generated from next generation sequencing of stored whole blood samples of N = 100 PWH (n = 50 who use cannabis, and n = 50 who do not use cannabis). Innovation. This dissertation research proposal is among the first to examine the potential association of miRNA profiles with subclinical cardiac dysfunction among PWH. It will also leverage a novel measure of cardiac dysfunction, and next generation sequencing to characterize cardiac miRNA profiles. Findings from this proposal will provide foundation experience relevant to my goal of pursuing a career as a molecular epidemiologist with expertise in co-occurring HIV, CVD, and substance use.

NSF Awards · FY 2025 · 2025-06

Sewer overflows are a serious threat to public health and the environment, causing contamination of drinking water, beach closures, and property damage. These overflows often happen during heavy rainfall when sewer systems are overwhelmed and spill into streets and waterways. This research aims to address sewer overflows by using artificial intelligence (AI) to predict and prevent them. By combining real-time data from sensors with advanced AI models, the project will help cities manage their sewer systems more effectively and stop overflows before they happen. This approach can reduce cleanup costs and make sewer systems run more efficiently. Preventing sewer overflows also keeps harmful pollutants from reaching rivers and oceans, ensuring cleaner water for drinking and recreation. As urban areas continue to grow, upgrading sewer systems has become even more important. Through AI, this project helps cities respond to changing conditions and manage health risks. The results of this research will be shared with government agencies, utility companies, and researchers for managing sewer systems. These technologies will give engineers and city planners the tools they need to fix and upgrade sewer systems, lower the chances of overflows, and protect the environment. Combined Sewer Overflows (CSOs) pose serious risks to both public health and the environment, requiring intelligent, data-driven solutions for effective prediction and mitigation. While artificial intelligence (AI) and machine learning (ML) offer transformative potential for optimizing sewer system operations, their practical deployment remains hindered by key technical barriers: insufficient guidance on the optimal spatial density and placement of Internet of Things (IoT) sensors for capturing unsteady hydraulic behavior; limited methods for distinguishing between physical events and sensor-related anomalies; and a lack of comprehensive field-scale studies validating AI/ML applications in operational settings. The goal of this proposal is to develop an integrated AI-based optimization framework that unifies IoT sensing, physics-based hydraulic modeling, and scalable ML algorithms to support proactive, system-wide CSO management. The research is structured around three core objectives: (1) identifying sensor configurations that balance spatial coverage with cost-efficiency; (2) developing high-precision anomaly detection algorithms that isolate non-physical noise from genuine hydraulic events; and (3) empirically evaluating AI and ML performance in mitigating CSOs across diverse operational conditions. Field deployments in two urban sewer networks, enhanced by high-resolution Computational Fluid Dynamics (CFD) simulations, will inform sensor deployment strategies and refine flow prediction models. AI-enhanced anomaly detection will improve data reliability, while ML models trained on heterogeneous datasets will enable accurate, real-time forecasts of CSO volumes and locations. Key deliverables include open-source, AI-physics hybrid modeling tools, advanced anomaly detection techniques, and a field-validated assessment of AI’s role in enhancing the resilience of urban wastewater infrastructure. Project outcomes will be disseminated through technical workshops and open-access digital platforms, fostering widespread adoption of AI-driven sewer management solutions and advancing the state of smart urban water systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This I-Corps project focuses on the development of innovative solar evaporation technology for small satellite propulsion. Traditional methods commonly rely on solar electric propulsion, where solar energy is converted into electricity before being transformed into thrust. These systems are limited in their energy conversion efficiency, and the thrust-to-power ratio is relatively low. This technology addresses these limitations by directly converting solar energy into thrust via a photothermal process. This improvement significantly increases efficiency, enabling a more powerful and cost-effective propulsion system for satellites, which is crucial for reducing launch costs and space requirements of satellite missions. Moreover, the system is compatible with renewable and non-toxic propellants, lowering costs, safety risk, and environmental harm. The widespread adoption of this technology could increase satellite mission capabilities while increasing safety and sustainability, supporting the growth of space exploration and habitation as well as advanced navigation. 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 represents the development of a high-efficiency, low-cost, solar evaporation method based on the ability for interfacial photothermal evaporation to concentrate solar energy at the surface of a porous material and use capillary action to draw water from the bulk to the heated surface. Solar energy is thus directly converted into thermal energy with a conversion efficiency of 85%, significantly surpassing the 50% efficiency of existing solar panels. Moreover, the direct solar-to-thermal conversion reduces energy loss, resulting in more efficient propulsion systems with a superior thrust to power ratio compared to traditional methods. The technology is compatible with abundant, inexpensive, and non-toxic propellants, such as water or ammonia. This advanced technology has the potential to revolutionize space propulsion by making satellite missions more cost-effective, sustainable, and capable of supporting a wide range of applications, from communications to Earth observation and deep-space exploration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Small molecules, including small biomolecules and bioactive molecules, such as adenosine triphosphate (ATP), hormones, neurotransmitters, and pharmaceutical drugs, play critical roles within biological systems. Detecting them and understanding their functions is important. However, compared to larger biomarker species, small molecules pose heightened detection challenges. Detecting and analyzing label-free small molecules under physiological conditions is even more challenging. This project will develop new sensing mechanisms and approaches for a nanopore sensor. The new sensor will be ultrasensitive and selective for various small molecules. It will be portable, inexpensive, easy to use, and applicable to a variety of areas such as protein analysis, pharmaceutical research, clinical molecular diagnostics, environment protection and preservation, and national defense and bioterrorism prevention. The knowledge and methods obtained from this project will benefit molecular biophysics and biochemistry, single-entity electrochemistry techniques and other stochastic sensors. The highly interdisciplinary nature of this research will provide excellent opportunities for academic training of undergraduate and graduate students and outreach STEM programs for K-12 students of Miami-Dade County. Nanopore biosensors have shown great promise as single-molecule sensors for a wide variety of biomedical applications. Glass nanopipettes, as a subtype of solid-state nanopore, have become increasingly popular due to their versatility, easy accessibility, and simple and cheap fabrication, but they are severely limited by their poor size resolution for the detection of small molecules. This project aims to use nanopipettes to develop a self-crowding and ligand-binding assisted ultrasensitive label-free electrical detection method for small biomolecules and bioactive small molecules at the single molecule level under physiological conditions based on their size, structure, charge, mobility and affinity. The project has two research tasks: (1) Understanding the unique ionic signals induced by self-crowding at the nanopipette tip and (2) Developing a small molecule sensing approach based on ligand binding. The proposed approach has the potential to solve the major challenges in nanopore sensing by slowing down the small molecules in the nanopipette sensing zone through self-crowding. The accumulation of small charged molecules in a confined nanoscale space also induces new nanofluidic phenomena, leading to new sensing mechanisms other than the volume exclusion mechanism (or Coulter Counter principle). The tunable crowding at the nanopipette tip and the enabled high sensitivity and structure resolution for molecular complexes also provide exciting opportunities to study the stoichiometry, binding strength and dynamics of small molecule involved interactions in a crowded environment at physiological 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 · 2025-05

Numerous windstorms, including hurricanes, tornadoes, and thunderstorms, strike the United States every year, inflicting extensive damage to buildings and other infrastructure such as structures in power, transportation, communication and information technology systems. This causes devastating life and property losses as well as prolonged interruption to critical societal functions. Even worse, future losses from windstorms are likely to increase because of the aging of buildings and other infrastructure as well as the growing population in hazard-prone regions. The Industry-University Cooperative Research Center (I/UCRC) for Wind Hazard and Infrastructure Performance (WHIP Center) is a consortium of major stakeholders for wind hazard resilience, which include academic institutions, government agencies and industry members from multiple sectors such as insurance, risk management structural engineering and renewable energy. By performing research of interests to these stakeholders, it addresses the multifaceted challenges presented by windstorms and develops solutions that can be expeditiously transferred to industry applications and generate societal impacts by enhancing the economic competitiveness of the U.S. and the well-being of its citizens. The WHIP Center adopts a broad-based approach aimed at serving its members and, through that, society. Its major research themes are characterization of wind hazards, assessment of performance and vulnerability of buildings and other infrastructure, improvement of community resilience, and reduction to societal impact by wind hazards. Within these themes, the faculty and students in the Center use analytical, experimental and numerical approaches to generate actionable solutions based on the needs of the industry members. As a partner institution, Florida International University (FIU) offers an extensive expertise in Natural Hazards Engineering. The FIU Site is home to the NSF NHERI Wall of Wind Experimental Facility (WOW EF), a 12-fan large open jet wind tunnel capable of simulating a Category 5 hurricane – the highest rating on the Saffir-Simpson Hurricane Wind Scale. FIU’s Extreme Events Institute (EEI) manages the WOW EF and fosters interdisciplinary research to build resilience against multiple hazards. Through research sponsored by the WHIP Center, the EEI and other FIU faculty and students will generate the envisioned societal impacts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This project examines the ways that different types of property rights are associated with strategies for conserving natural resources. When local communities maintain land rights to natural areas, there are opportunities for values and norms to influence conservation approaches and land use activities. This doctoral dissertation project examines multiple communities in settings that have been managed as protected areas with varying degrees of governmental involvement. The researchers comparatively focus on land ownership as a contributor to land use practices and biodiversity conservation. A complementary focus examines the ways that conservation organizations adapt their approaches to support local communities that have secured property rights. The findings are shared with community partners, and the project contributes to the education and training of an early-career scientist. This research considers the effectiveness of conservation strategies in relation to land rights and local communities that prioritize their values and norms. Using a range of qualitative methods, including observations, interviews, and archival analysis, the researchers examine how land ownership affects management and the interface with conservation organizations and governmental agencies. The project potentially elucidates new conservation approaches that can emerge in decentralized contexts. By examining multiple communities and strategies, the project considers the extent to which the success of conservation efforts can be evaluated according to local priorities and contexts. 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

With the funding from the Chemical Catalysis Program of the Division of Chemistry, Dr. Bukhryakov of Florida International Univ will use an Earth-abundant first-row metal, vanadium, to develop catalysts for alkyne metathesis. Alkyne metathesis is a widely used synthetic method for the synthesis of chemicals containing carbon-carbon triple bonds, including advanced materials that have potential applications in organic light-emitted diodes, organic photovoltaics, organic field-effect transistors, and optical and molecular sensors. In addition, alkynes are essential starting points for producing a large variety of organic compounds, taking advantage of the chemical versatility of a triple bond. While the current alkyne metathesis chemistry exclusively relies on second- and third-row transition metals, this project focuses on earth-abundant first-row metal vanadium. This, in turn, will make essential chemicals more accessible to consumers and decrease the human environmental footprint. The proposed educational plan will focus on the research engagement of high-school and undergraduate students from underrepresented groups. It will include the "Recycle your PET" project, a summer research internship for high-school students, and establishing a research advising office at FIU. The project's ultimate goal is to increase the number of minority students who pursue a doctoral degree in science. The proposed research plan will focus on developing well-defined first-row transition metal catalysts based on V alkylidynes for alkyne metathesis, a transformation that has never been utilized, to offer improved reactivity and provide inexpensive and greener alternatives to existing catalysts. The team will perform a systematic and comprehensive study on the influence of size and the electronic characteristics of the ligand set in V-based catalysts on the alkyne metathesis. The proposed investigations will identify the crucial factors required for efficient, reliable, and selective V-catalyzed alkyne metathesis of a wide variety of olefins. The long-standing goal of the proposed research is implementing V-catalysts for alkyne metathesis in academia and industry, which will be achieved in three steps: 1) Catalysts development based on a comprehensive study of the influence of neutral and anionic ligands on the catalyst activity, selectivity, and stability; 2) Mechanistic investigation using kinetic studies of initiation, productive metathesis, and decomposition steps. 3) Applications of V-based alkyne metathesis to complement existing transformations and offer a distinct reactivity. The research program will aid to prepare the next generation of scientists and take a leadership role in ongoing efforts to revolutionize organic, organometallic, polymer chemistry, and catalysis for a sustainable future. 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 · 2025-02

The 39th Conference on Artificial Intelligence (AAAI-25) will take place from February 25 to March 4, 2025, at the Pennsylvania Convention Center in Philadelphia, Pennsylvania, USA, organized by the Association for the Advancement of Artificial Intelligence (AAAI), a non-profit scientific society focused on advancing the field of artificial intelligence. The event will bring together experts from across the globe. This event will feature technical presentations, workshops, panels, and invited talks across various AI domains, including machine learning, natural language processing, computer vision, robotics, and cognitive science. This project will support full student participation in the meeting.Participation in the Association for the Advancement of Artificial Intelligence (AAAI) conference offers significant advantages across various AI fields. Attendees gain invaluable networking opportunities, fostering collaboration among researchers, industry professionals, and educators. The AAAI-2025 conference serves as a key platform for sharing groundbreaking research and fostering intellectual exchange in the field of artificial intelligence (AI). This prestigious event will feature technical presentations, workshops, panels, and invited talks spanning various AI domains, including machine learning, natural language processing, computer vision, robotics, and cognitive science. It also presents a valuable opportunity for students and aspiring AI professionals. This collaborative environment is particularly advantageous for students and recipients of NSF travel awards, promoting groundbreaking research while enhancing career growth and visibility. Exposure to the latest AI trends and discussions at AAAI also enriches knowledge, supporting the development of educational curricula and ensuring students receive up-to-date AI education. The conference’s focus on ethical considerations equips attendees, including NSF travel awardees, to contribute to responsible AI development, societal impact, and policy making. 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

PROJECT SUMMARY/ABSTRACT Crisis migration has increased significantly over the last decade, with urgent calls within the United States (U.S.) to develop better programs to support newly immigrated youth and their families seeking refuge.1 In recent years, patterns of migration from Venezuela to the U.S. have dramatically increased, as Venezuela continues to grapple with humanitarian, socio-political and economic crises.3,4,15 Many migrant youth from Venezuela have likely been exposed to dire circumstances (i.e., food insecurity) in their country,17,18 only to be met with further stressors such as, language barriers, and acculturation stress,6,20 upon resettling in the U.S. Given the unique pre and post migration experiences and stressors that can occur, it is critical to understand how to best support the mental health and wellbeing of newly arrived families from Venezuela. The proposed project leverages an existing NIMHD-funded R01 (MD015920) grant examining pre and post migration factors impacting adjustment in recently arriving Venezuelan families seeking refuge in the U.S. The mixed-methods dissertation project at the center of this R36 proposal seeks to understand the mental health impacts of crisis migration on recently arriving Venezuelan youth, and to examine key risk and protective factors that moderate these mental health impacts. Aim 1 will examine links between youth crisis migration experiences in Venezuela and subsequent youth anxiety and traumatic stress symptoms in the U.S. and Aim 2 will evaluate the extent to which cultural and community factors (e.g., ethnic pride, community cohesion) may impact the mental health effects of crisis migration (N=300). Building on these quantitative analyses, Aim 3 employs a qualitative approach to explore caregivers’ perspectives in their own voice (N=15) on individual youth, family, and community protective strengths, as well as caregiver reflections on intersections between social factors and their children’s mental health. Findings from this proposed project can help inform best practices for supporting Venezuelan migrants, as well as other youth populations in the U.S. adjusting in the aftermath of crisis migration.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY The primary objective of the proposed R36 is to elucidate molecular mechanisms whereby inflammation and mitochondrial dysfunction, both in the presence and absence of amphetamine-type stimulant use during acute HIV infection, can predict persistent depressive symptoms over 96 weeks of suppressive antiretroviral therapy. By 2030, it is expected that the two leading causes of disease around the world will be HIV and depressive disorders, and the excess burden among people with HIV (PWH) is estimated to be three times higher than among those without HIV. During AHI, gut immune dysfunction can lead to the release of proinflammatory cytokines into the periphery. These cytokines can cross the blood brain barrier, leading to damage in the brain and alterations of the functional connectivity of key circuits involved in emotional processes and the hypothalamic-pituitary-adrenal axis stress response associated with depressive symptoms. Several studies have also independently linked mitochondrial stress and depressive symptoms. When stressed, mitochondria activating potent inflammatory cascades, which contribute to the heightened inflammatory state that is often characteristic of AHI. Amphetamine-type stimulant use (ATS) is also known to increase HIV pathogenesis in the central nervous system, contribute to oxidative stress and increase serum biomarkers of inflammation. ATS can also lead to significant changes in the dopamine and glutaminergic systems, both of which are involved in substance use and depression. Taken together, these disturbances can increase oxidative stress and exacerbate neuroinflammation. Therefore, this study proposes to leverage the explanatory strength of proteomics to identify biological signatures underlying distinct trajectories of depression in PWH, by leveraging 100 plasma samples from the RV254/SEARCH010 cohort, the largest prospective investigation of individuals who have undergone extensive clinical phenotyping from acute through chronic HIV. Based on previous characterizations of depressive symptom trajectories over 96 weeks, Aim 1 will determine the associations of proteomic alterations relevant to inflammation and mitochondrial dysfunction of persistent [n=50] (versus resolved [n=50]) depressive symptoms over 96 weeks of suppressive ART. Aim 2 will examine if recent ATS use amplifies the associations of these proteomic alterations with persistent (versus resolved) depressive symptoms over 96 weeks of suppressive ART. Proteomic signatures will be identified using the SomaScan Assay, a highly multiplexed aptamer-based proteomic technology. The depth of the data that will be collected through this study will provide exceptional opportunities to further interrogate neuroimmune mechanisms relevant to persistent depressive symptoms and neurocognitive impairment in a planned K99/R00 proposal.

NSF Awards · FY 2024 · 2024-12

In today's rapidly changing environmental landscape, developing a skilled workforce adept at utilizing advanced cyberinfrastructure is critical for sustainable and transdisciplinary environmental science research. The EcoTern project addresses this need by pioneering the training of the next generation cyberinfrastructure workforce to be capable of integrating artificial intelligence and machine learning technologies into environmental and computer science and engineering research. This collaborative effort, involving Florida International University (FIU), North Carolina State University (NCSU), and the NSF-funded Artificial Intelligence for Environmental Sciences (AI2ES) Institute, aims to develop comprehensive training activities, including new degree programs, curriculum enhancements, reusable course content, summer bootcamps, seminars, and interactive hands-on exercises. These activities will provide trainees with the necessary skills to utilize cyberinfrastructure for predicting and mitigating environmental impacts, such as coastal flooding, hurricane disasters, and marine ecological changes. By promoting the progress of science and supporting national health, prosperity, and welfare, this project serves the national interest by preparing a diverse and knowledgeable workforce to address environmental challenges resulting from a changing climate and other causes. EcoTern’s innovative approach involves weaving cyberinfrastructure training into the new undergraduate Data Science program at FIU and integrating cyberinfrastructure, artificial intelligence, and environmental science training into nine existing graduate courses at FIU and NCSU. Course materials will be shared broadly, and the project will cultivate a network of collaborating institutions engaged in the overlap of environmental science, artificial intelligence, and cyberinfrastructure education. The project will host a two-week summer bootcamp, providing intensive instruction and interdisciplinary research opportunities. A series of specialized workshops and invited lectures from cyberinfrastructure and artificial intelligence experts will further enhance the training program. An online platform will be developed to offer personalized hands-on exercises and real-time learning progress tracking. Research objectives include advancing interdisciplinary environmental and computer science and engineering research, preparing a better scientific workforce for cyberinfrastructure-enabled research, and creating a ubiquitous and scalable educational and training ecosystem for online, dynamic, personalized lessons and certifications. By democratizing access to advanced cyberinfrastructure resources and promoting transdisciplinary collaboration, EcoTern aims to cultivate a diverse, knowledgeable, and skilled community capable of driving innovation and addressing emerging environmental science and engineering challenges. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and 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 · 2024-12

PROJECT SUMMARY Although Aduhelm and Leqembi were recently approved for treating Alzheimer's disease (AD) patients, the reversibility and long-term benefits to patients remain far from reality. Accumulating data suggest that insulin resistance and markers of senescence increase during normal aging and even more so in AD as reflected by colocalization of neurofibrillary tangles (NFTs) and senescence markers in the neurons of both AD patients and mouse models of AD. Also, the impaired autophagy-lysosome pathway (ALP) is known to accelerate senescence, and restoring ALP has been shown to reverse senescence. Therefore, ALP activators are predicted to be excellent drugs to ameliorate multiple pathologies of AD including senescence. Our recently published data suggest that TFEB, a master regulator of ALP, protein levels are reduced in AD brains, TFEB expression by genetic approach reduces lipofuscin, increases lysosome and mitochondria biogenesis, and improves cognition. Considering the enormous potential for TFEB activators, we developed a high-content screening assay and identified TPI-132 as a potent activator of TFEB and ALP. TPI-132 dose- dependently dephosphorylated TFEB, has relatively low toxicity, and rescues doxorubicin- and d-galactose- induced senescence in cell lines and primary neurons. However, TPI-132 has low brain penetration. We also found mTOR-independent and calcineurin-dependent mechanism of TPI-132 to dephosphorylate and activate TFEB. Interestingly, we found that TPI-132 administration in old 3xTg mouse model of AD rescued age- associated insulin resistance dose-dependently. More importantly, TPI-132 increased ADAM10/sAPPα levels in the mouse brain following chronic administration. Based on these data, we hypothesize that by modulating TFEB and autophagy activity, TPI-132 increases neuroprotection, mitigates insulin resistance and senescence thereby improving Alzheimer's pathology and cognition. Aim 1 is designed to hit to lead optimization to increase potency and brain permeability, by synthesizing six sets of new compounds with structural diversity. Two best compounds with increased TFEB activity, reduced toxicity, and improved brain penetration and solubility will be selected to test in Aim 2. In aim 2, the selected two analogs will be tested in primary neurons from AD mouse models and iPSC-derived neurons from AD patients on senescence, dendritic spines, and Aβ/tau pathology. In aim 3, TPI-132 and the two best analogs will be administered to 3xTg and PS19 mouse models of AD by i.p. injections at 2 ages, 11 and 17 months for one month. The compounds' effect on insulin tolerance, senescence, synapses, protein aggregates and ALP activity will be quantified and correlated with cognition and compound levels in the brain. TPI-132 is more promising than other reported TFEB activators because besides anti-senescence effects, it increases two powerful neuroprotective factors, ADAM10/sAPPα and thus can be further developed and tested in clinical trials in future studies as new class of drugs for AD.

NIH Research Projects · FY 2026 · 2024-11

SUMMARY Malaria caused by Plasmodium parasites affects millions of people worldwide. The life cycle of the malaria parasites involves a vertebrate host and mosquito vector. Transmission through Anopheles mosquitoes is an obligatory step of the parasite’s life cycle and represents a vulnerable target for transmission-blocking strategies. After the ingestion of Plasmodium-infected blood, the parasites inside the mosquito’s midgut undergo gametogenesis and fertilization, and the resultant zygote transforms into a motile ookinete. Within about 24 hours, the ookinete must escape the blood bolus, penetrate the midgut peritrophic matrix and epithelium, and eventually lie beneath the basal lamina for sporogonic development. Despite the significance of midgut invasion for the successful transmission of the parasite, the molecular mechanisms of the invasion process are poorly understood. We hypothesize that the parasite-mosquito midgut interactive proteins serve essential roles in parasite invasion in mosquitoes and that disrupting these interactions will stop malaria transmission. To better understand the parasite invasion process in the midgut, we have performed large-scale screening of a midgut protein library and a parasite sexual-stage protein library and identified candidate proteins from each library that potentially mediate the parasite-midgut interactions. Based on these discoveries, this proposed research will study the molecular mechanisms of parasite-midgut interactions and evaluate these protein candidates as novel targets for transmission-blocking vaccines. We will functionally characterize these mosquito and parasite proteins to study their tissue distribution and subcellular localization and identify their binding partners. We will also determine their transmission-blocking potential using in vitro mosquito feeding assays and map the essential epitopes using a panel of monoclonal antibodies. Finally, we will design chimeric antigens that target both the parasite and mosquito antigens to achieve stronger transmission-blocking activities. The long-term goal of this project is to gain a significant understanding of the mechanisms of parasite-mosquito midgut interactions and develop new approaches to stop malaria transmission.

NSF Awards · FY 2024 · 2024-11

This planning grant will enable the development of a competitive and collaborative Centers for Equity in Engineering (CEE) Phase I project proposal. The Center proposal seeks to enable the research and practice of an Engineering for Community Development (ECD) that provides transformational experiences to all students, especially students of color. Through creating a distributed center across selected partner institutions, we are seeking to initiate and sustain a shift within ECD towards the principles of inclusive pedagogy. Such a shift will eliminate the limitations of traditional ECD, which can often exclude groups of peoples, reinforce stereotypes, and leave partner communities wanting more. Replacement with an inclusive ECD can specifically provide for students of color, their assets, and their needs while positively impacting partner communities to strengthen efforts of broadening participation in engineering. In conducting this research, we will understand the ways in which the limitations of service-learning’s ability to equitably educate all students can be overcome. More specifically, if service-learning is going to support the education of students from minoritized backgrounds, then we must research ECD with respect to the specific needs of students from these various backgrounds. This work will impact students, community partners, and institutions of higher education through promoting ECD that is inclusive to all, where students from historically minoritized demographics will be supported through transformational experiences that encourage them to remain and succeed in engineering. Through strengthening and creating a broad array of inclusive and culturally relevant ECD approaches, contexts, and data collection methods the research performed by the Center will enable effort across the research-to-practice cycle that systematically includes historically marginalized students and communities into ECDinitiatives. To accomplish this goal and maximize the chances of a funded proposal, the following primary objectives will be met within the planning proposal: Align core team through regular virtual meetings; Articulate core concept of research; Develop initial Delphi prompts; Plan and perform discovery phases of a Delphi method; Conduct an intensive two day workshop; And write and refine proposal using information from first two phases of Delphi. This effort will produce new knowledge on what an inclusive ECD is and how it can be accomplished in practice. Through supporting inclusive and culturally relevant ECD practice, students from historically minoritized demographics will be supported to obtain transformational outcomes within ECD initiatives and by extension increase the likelihood to remain and succeed in engineering. Ultimately, as promoted by the Office of Science and Technology Policy of the White House, federally funded research will, moving forward, try to include community knowledge and interest from its inception (generating research questions) to its conclusion (research deliverables) (National Academies, 2022). Hence, this inclusion of communities in research and design that ECD offers will challenge STEM (Science, Technology, Engineering and Mathematics) education at large to develop a student workforce capable of effectively interacting and co-developing with local communities and being diverse enough to empathize with the realities and challenges of racially, ethnically, and socio-economically diverse communities. Through developing and exploring inclusive and culturally relevant ECD contexts, this planning grant, and in turn, the full grant, can enable the use of ECD problem domains and contexts that critically engage issues of marginalization and social justice to promote diverse and equitable ECD participation within engineering education. Collectively, these impacts will strengthen engineering institutions’ ability to serve as vehicles for broadening participation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Future electronics manufacturing will not only encompass devices like chips and chiplets but also integrate passive components, sensors, and Radio Frequency (RF) front ends with power amplifiers into high-density heterogeneous packages. Currently, package components are tested individually and reassembled on high-density interposer substrates. However, future trends indicate that the interconnect pitch between devices will scale down to less than a micron. This shift towards high-density integration necessitates more sophisticated methods to mitigate electromagnetic interference (EMI) and ensure electromagnetic compatibility (EMC) within the overall package. With this in mind, this research focuses on thin film magnetodielectric materials and electronic package topologies, coupled with Multiphysics digital twin models, aimed at suppressing: 1) jittering and degradation of bit error rates, 2) switching noise in digital integrated circuits due to emissions or crosstalk, 3) close proximity coupling and radiation due to high-speed digital or analog interconnects, and 4) transmission line crosstalk due to conduction or ground bounces of electromagnetic fields. The primary goal of this project is to develop new techniques for predicting and suppressing signal interference in future high-density electronic packages. Concurrently, the project will develop a comprehensive multiphysics toolset to integrate circuit extraction methods and materials for EMI suppression with popular chip and electronic/RF circuit toolsets seamlessly. This research is expected to impact the rapidly growing U.S. semiconductor industry and enable the packaging of reliable, vertically integrated electronics. Additionally, it will substantially enhance the training of a broad range of students by leveraging Florida International University's unique position as the only Majority-Minority Carnegie R1 Research University in the continental U.S. This research will further advance educational efforts to broaden participation of a wide range of students in STEM through curriculum development and REU programs. The aim of this research is to pioneer disruptive EMI/EMC mitigation techniques in high-density heterogeneous semiconductor device packages through digital twin emulations. The goal is to advance knowledge and enable optimal embedded packaging with heterogeneous active and passive components to ensure future electronic package reliability and performance. To address these challenges, the project will investigate: a) new EMI/EMC ferromagnetic composite materials, b) novel packaging topologies, and c) advanced computational and circuits extraction methods. A number of innovations are expected: 1) multilayered films formed of cobalt nickel-iron alloy (CoNiFe) and copper (Cu) layers, but still only 5 micrometers thick, to achieve maximum shielding with minimal film thickness. As much as 30 dB more shielding can be attained using these composite films; 2) New shielding topologies for reliable interference suppression using prefabricated forms to enable rapid electronic package formations. These are aimed at suppressing high-power device interference and high-speed circuit radiation at the chip interconnects (as much as 40 dB or more); 3) New class of multiphysics toolsets that cut across electromagnetic, thermomechanical and thermal designs to provide system performance and reliability. Such multiphysics models are of critical importance as interactions among various physics domains imply design trade-offs needing quantification; 4) AI multiphysics models that combine model-agnostic meta-learning and physics-informed learning approaches using measured data as well as analytical and semi-analytical models for accurate and robust modeling; 5) Novel S-parameter extraction methods that uniquely account for coupling and external fields using new port excitations within the circuit network of the multiphysics model. 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

In the multi-billion-dollar storage industry, efficient operation of systems is essential for achieving application accuracy, reliability, and performance. Traditionally, this efficiency has relied on heuristics with adjustable parameters. However, as workloads and devices become increasingly complex, manual tuning becomes impractical. The DISCO project (which stands for “disciplined data science framework for storage I/O management”) will address how to systematically leverage data science (DS) to revolutionize the many facets of storage I/O decision making. More specifically, DISCO’s research objectives are to (a) pioneer a comprehensive data science pipeline tailored to enhance the storage I/O decision-making process by in-depth exploration of intricate concepts such as data augmentation, precise labeling, noise filtration, meticulous model engineering, drift detection, and many others; (b) target both classical I/O policies (e.g., I/O admission, prefetching) and open problems in the context of modern device features (multi-stream and KV-SSDs) as well as venture to “uncharted territories" such as investigating what data science can reveal from billions of performance data points; and (c) comprehensively encompass high-, medium-, and low-frequency decision making and address each of their own unique challenges, but at the same time address cross-cutting concerns such as all-in-one integration. The DISCO project will bring significant broader impacts, especially in training future storage data scientists. The Data Storage Research Vision 2025 (DSRV) paper from an NSF workshop emphasized "the deficit of the professionals who are knowledgeable in both storage and AI areas" where "the number of fresh graduate students with this combination of skills is small, and training existing staff takes time and effort" and "storage companies are also experiencing significant competition from other industries that require AI/ML knowledge." In this context, the DISCO project will train graduate and undergraduate students to be part of the next-generation storage data scientists. The project will also release open ML-for-storage testbeds along with a public storage data science curriculum. In terms of technology transfer, the DSRV workshop paper also states that “storage companies are excited by the opportunities of using ML to improve performance and reliability, and develop quality products.” The DISCO project will produce sophisticated ML-for-storage solutions for solid-state drive (SSD) systems, potentially making a positive impact to the SSD market that is forecasted to reach over $50 billion by 2025. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) have emerged as a promising technology for 6G wireless networks, aiming to improve user experience and enhance people’s lives. By leveraging millimeter wave (mmWave) communications, UAV-enabled ISAC systems are expected to deliver high-throughput, ultra-reliable, and low-latency wireless communications, along with highly accurate wireless sensing and localization within 6G networks. Simultaneously, artificial intelligence (AI) and machine learning (ML) are anticipated to transform platform-based ecosystems, business models, and services in future 6G networks. The key challenge is integrating UAV localization, mmWave communications, wireless sensing, and security with AI/ML for future 6G systems. A multidisciplinary team of six investigators from Auburn University (AU), Florida International University (FIU), the Indian Institute of Technology Kanpur (IIT Kharagpur), and the International Institute of Information Technology, Naya Raipur (IIIT, Naya Raipur) collaborate closely on a project focused on learning-assisted integrated sensing, communication, and security for 6G UAV networks. The educational plan of this project includes developing joint course materials on AI/ML for UAV networks and IoT, enhancing undergraduate and graduate-level courses at the participating institutions. Simulation tools and testbeds developed through this project offer students hands-on experience with cutting-edge technology. The project outcomes are disseminated via technical publications, conference keynotes/tutorials, IEEE distinguished lectures and seminars, a project website, and open-source repositories. The investigators are committed to encouraging participation from underrepresented groups through outreach programs at their institutions and the NSFBPC/REU/RET programs throughout the project. The project aims to develop deep learning (DL)-based localization and sensing in UAV mmWave networks, location-aided UAV mmWave communications, and joint UAV mmWave communication and radar co-design to improve mmWave spectrum utilization, wireless sensing performance, and UAV device security. The research agenda consists of five well integrated thrusts: (i) Learning-based mmWave UAV localization and wireless sensing; (ii) Joint design of location-aided UAV mmWave communications and sensing; (iii) Multiple UAV communications and sensing co-design; (iv) Learning-based RF fingerprinting for UAV security; and (v) Integration and assessment: the proposed techniques are implemented with both ray-tracing software tools (e.g., DeepMIMO), mmWave devices (e.g., TP-link Talon AD7200) and TI mmWave radars, Parrot AR Drone2.0 UAV, programmable (e.g. USRP) devices, and the NSF PAWR AERPAW testbed, and validated with extensive experiments in real, representative outdoor and indoor environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The success of combined antiretroviral therapy (cART) has drastically improved the life expectancy of people living with HIV (PLWH). With improved survival among PLWH, many are suffering from age-related neurocognitive complications. HIV-associated inflammation has been implicated in premature aging and increased risk of age-associated comorbidities even in cART-treated individuals. Autophagy, a conserved lysosomal degradation pathway, is known to increase the lifespan of model organisms. We have previously shown that the autophagy protein Beclin1 can regulate viral replication and the secretion of neuroinflammatory molecules in HIV-infected glial cell cultures and an EcoHIV-infected mouse model. Beclin1 interacts via its BH3 domain with the anti-apoptotic Bcl-2 family; thereby inhibiting autophagy. Studies have shown that disrupting the binding between Beclin1 and Bcl-2 can effectively increase autophagy, preventing premature aging, improving health span, and promoting longevity in healthy aged mammals. Thus, the goal of this exploratory grant is to investigate whether increasing autophagy via the inhibition of the Beclin1- Bcl-2 complex formation delays age- related phenotypes and improves cognitive decline associated with HIV in infected adult and old mice. To test our goals, an equal number of male and female Becn1F121A/F121A and C57BL/6J (control) mice at 6 months (Experiment 1) and 16 months of age (Experiment 2) will be infected with EcoHIV or equal volume Saline (control). After 2 weeks a subset of the EcoHIV-infected and Saline animals will receive a daily cocktail of ART and a subset will receive equal volume Saline. In Specific Aim 1, we hypothesize that reduction of the Beclin1– BCL2 complex will improve age-related phenotypes and cognitive decline induced by HIV under ART. In Specific Aim 2, we hypothesize that disrupting the interaction between Beclin1 and Bcl-2 will reduce the expression of key markers of inflammation, oxidative stress, glutamatergic neurotransmission, and altered synaptic plasticity in the pathogenesis of aging and cognitive impairments. The experiments described in this proposal will allow us to determine the role of autophagy in the prevention of age-related neurocognitive complications associated with HIV infection. The success of this study will enhance our knowledge of the mechanism of persistence of neurological complications associated with HIV in the aging population and could lead to the development of a Beclin1-mediated autophagy hyperactivation therapeutic strategy in the prevention of age-related neurocognitive complications associated with HIV infection.

NIH Research Projects · FY 2024 · 2024-09

Florida International University (FIU) is a major public research institution in Miami-Dade County, Florida, serving more than 54,000 students and employing over 1,100 full-time instructional faculty. FIU supports scientific research on conditions of broad public health importance, including HIV, mental illness and cancer, aims to expand capacity in cutting-edge genomic science. Although FIU’s investigators bring strong perspectives to genomics, research depth is constrained by limited infrastructure and sustained support for high-throughput, secure, and reproducible analyses. To address this, we propose establishing the FIU Center for Genome Research (FIU-CGR) to accelerate discovery and translation in genomics. The initiative will proceed in two phases. Phase I (UG3) will develop detailed plans and test feasibility for: (i) launching FIU-CGR to conduct innovative, state-of-the-art genomic studies; (ii) advancing career development for trainees and investigators at all levels; (iii) enhancing genomic infrastructure and computational/analytical capabilities; and (iv) establishing sustainable partnerships and disseminating resources and findings. Phase II (UH3) will implement the Center, supporting an Administrative Core, a Workforce Development Core, a Community Engagement Core, and three interrelated, innovative research projects. Anticipated outcomes include: (1) substantially increased genomic research capacity at FIU, enabling a larger volume of high-quality genomic studies; (2) growth in the size and capability of the genomics research workforce; (3) advancement of genomic science through innovative methods and robust, reproducible, high impact findings; and (4) increased student interest and broader participation in genomic studies, leading to improved translation of research into measurable improvements in health outcomes.

NIH Research Projects · FY 2025 · 2024-09

This project supports the ABCD-ReproNim Educational Program, which provides research training in responsible and reproducible analyses of data from the Adolescent Brain Cognitive Development (ABCD) Study. ABCD-ReproNim was established in 2020 as a NIDA-funded educational program and structured as a collaborative partnership between ABCD investigators and ReproNim, a NIBIB-funded P41 center for reproducible neuroimaging whose vision is to help researchers achieve more reproducible data analysis workflows and outcomes. With over 1K registered students and >15K YouTube views, the previous version of ABCD-ReproNim was well-received by the community and successfully achieved its goal to provide ABCD data training while promoting skill development in reproducible analyses that support efficient, re-executable design and FAIR practices. However, with advances in the field and increased data availability, we have identified gaps in our previous curriculum. First, while one of our lecture modules focused on ABCD’s cultural and environmental variables, additional instruction is needed to train students on statistical and socioenvironmental considerations for responsible data use. Second, one of the most significant developments in emerging, population-based datasets, such as ABCD, is the availability of longitudinal data; however, many trainees lack hands-on instruction for appropriately handling multiple time points in complex, large datasets. Third, our previous students provided valuable feedback regarding challenges they experienced. We have addressed many of their concerns by providing a more flexible course schedule, expanding support by adding a fellows program, and increasing the quality and duration of hackathon preparation to prepare students to conduct their own data analysis projects. Moving forward, we propose to refine and extend the innovative ABCD educational approach. Participants will first receive didactic training across a 16-week online course that includes lectures, readings, and data exercises; at the completion of the course, participants will contribute to team-based, collaborative data analysis projects during a five-day hackathon at the host institution, Florida International University, in Miami, Florida. Through this research educational program, we aim to provide: instruction techniques that enhance the validity and reproducibility of research methods; a comprehensive understanding of the ABCD dataset; support of interdisciplinary, team-based collaborations; and dissemination of the course, project materials, and research findings. To achieve these aims, we have assembled an interdisciplinary team of instructors and evaluators that includes ABCD Study Investigators, ReproNim team members and collaborators, and non-ABCD/ReproNim researchers. Success will result in the generation of a cadre of investigators that are well trained in the techniques that support responsible, reproducible, and valid analyses of ABCD data.

NIH Research Projects · FY 2025 · 2024-09

Development of Biotherapeutic Nanogels for Alzheimer’s Disease Treatment. PROJECT SUMMARY (ABSTRACT): Alzheimer's disease (AD) is a neurodegenerative disease that has affected about 44 million people worldwide. Available treatments temporarily improve the associated symptoms, such as memory loss and difficulty in thinking and reasoning. Even with the recent approvals of Aduhelm and Leqembi targeting Aβ which may benefit only a subset of patients, novel interventions based on multiple other mechanisms are still needed due to the heterogeneous nature of AD. Most importantly, AD is characterized by β amyloid (Aβ) deposition, directly linked to oxidative stress and neuroinflammation. The need is to develop therapies that target the identified biomarkers of AD, which can stop or significantly delay the progression of the disease. This proposal utilizes the patented state-of-the-art autofluorescent biopolymeric nanogels (US Patent 10, 344,100; 2019; WO/2020/247730) with inherent anti-viral properties, biodegradability, and cellular biocompatibility. These nanogels have demonstrated salient features of biocompatibility, anti-viral, stability, and cellular uptake by microglial cells and also can transmigrate across the blood-brain barrier (BBB). The preliminary molecular dynamics simulations studies of the polyol-Aβ complex obtained by docking polyol to Aβ fibril showed that polyol binds to the interface where Aβ peptides stack, potentially inhibiting the Aβ formation. Given these findings, we hypothesize that the increased content of polyol and decreased polydispersity index of the nanogels will significantly increase the transport of nanogels across the BBB. The linseed polyol will play a significant role in inhibiting the growth of β amyloid (Aβ) aggregates, and the nanogels will be used to target the identified biomarkers of AD-like amyloid β, neurofibrillary tangles as well as inflammatory markers such as tumor necrosis factor-α (TNF-α), NLRP3 and NF-kB levels in the brain. To this end, In Specific Aim 1 we will improve our developed biotherapeutic nanogel for enhanced transmigration across the BBB and study the efficacy of the developed nanogel to ameliorate the oxidative stress in Alzheimer's disease. In Specific Aim 2, we will test the in vivo efficacy of the developed nanogel on amyloid and tau pathology as well as neuroinflammation, dendritic spines, and learning skills in a 3xTg mouse model of AD. Altogether, we propose to develop a novel nanogel system with enhanced transmigration across the BBB with more effective biotherapeutic efficacy to ameliorate oxidative stress and neuroinflammation associated with AD, which is a very important unmet clinical need, and it is an area of high priority in AD/ADRD and is within the mission of NIA. The above system will act as a multifunctional nanogel system inhibiting Aβ aggregate, and fibril formation thereby rescuing from neuroinflammation, loss of dendritic spines, and cognitive decline in AD.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Suicidal ideation (SI) is prevalent and impairing in youth and can lead to restrictive and intensive care. There is a critical need to develop and test scalable brief interventions conducive to enhancing least-restrictive, cost-efficient, and accessible support, and which target drivers of SI in youth. This R61/R33 application addresses this need via a brief intervention module for youth experiencing subacute SI (i.e., SI which does not necessitate intensive, restrictive services) which targets an established driver of SI, perceived burdensomeness toward others (PB). This R61/R33 proposal builds on pilot findings by first demonstrating the ability of the brief module to engage the target now in a rigorous randomized controlled trial (RCT; R61) and then evaluating the module embedded within a larger CBT protocol in preparation for a larger clinical trial (R33). We will measure the target using established and novel measures recently developed and validated by the MPIs with an eye toward establishing multimethod measurement of PB for a possible larger clinical trial. The proposed intervention module addresses current empirical and clinical gaps in the following ways: The intervention module (1) precisely targets a suicide-related interpersonal risk factor, leveraging the MPIs recent clinical trial findings and in congruence with a large body of work on suicide etiology and theory; (2) is easily combined with standard therapies via simplicity from a streamlined manual and intuitive concepts, (3) and is highly scalable via a cognitive-behavioral approach with high resource efficiency (e.g., minimal therapist training, little session time needed). The study is therefore congruent with NIMH Strategy 3.2.A: “Developing multi-modal intervention strategies that combine the simultaneous application of established or novel [psychosocial interventions] to selectively access specific therapeutic targets.”

NSF Awards · FY 2024 · 2024-09

The second quantum revolution is paving the way for a future with unprecedented technological capabilities. The National Quantum Initiative Act catalyzed the demand for a skilled and diverse workforce by identifying quantum technologies as a national priority. The practical realization of each of these technologies cannot be accomplished without a toolbox of materials building blocks that display controllable quantum properties demanded by each application. This National Science Foundation Research Traineeship (NRT) award to the Florida International University will provide a coherent research and education framework to address these imperative needs in quantum information science and engineering (QISE) by training graduate students for the quantum workforce. The project anticipates training one hundred and ninety (190) M.S. and Ph.D. students, including thirty (30) funded trainees, from Materials Science and Engineering, Chemistry, and Physics programs. The research focus of the NRT Q-STAR project is on novel quantum materials discovery, synthesis, modeling, and validation of their quantum behavior. The educational component involves a 360-degree student-centered graduate education model encompassing elements that deliver: A. Technical expertise training: these elements are team-research projects, internships at partner institutions, and a designed coursework; and B. Career-success training including the ability to lead projects, work in teams, communicate science to public and policymakers, and make ethical decisions. Specific elements include training mechanisms to promote learning of science communication, ethics, and teamwork, and a workshop on transferable skills including leadership and entrepreneurship is included. The program will provide all trainees a pathway toward technical and leadership roles in QISE across the nation. The convergent research will bridge the chemistry and materials science that will enable novel quantum materials, with the physics of the quantum phenomena that will render these materials useful in future quantum technologies. The discovery process will be data-driven, using machine learning and density functional theory (DFT) to guide experiments. The scientific advancements anticipated by exploring novel non-centrosymmetric chalcogenide materials for quantum frequency converters, 2D and 3D semiconductor qubits, and materials for quantum battery technologies will be complemented by new knowledge gained through the implementation of a 360-student training model. At the core of the program are proven strategies to enhance recruitment, retention, and persistence in STEM of all students, including the cohort model, team-training, and a comprehensive, Individual Development Plan-based mentoring program. This project will strive to extend the professional development and career-choice program elements to all participants, thereby benefiting a large population of FIU graduate students, especially through the QISE concentration to be developed. The project will foster collaborative efforts with industry, government laboratory partners, and other academic institutions, offering trainees a wide range of opportunities for internships. The training model outcomes will be evaluated and disseminated to enable its facile implementation to other departments at FIU and other institutions, especially minority serving institutions (MSIs). The program will advance transformative research in QISE while training a diverse population of graduate students in this area of high priority to the nation. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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.