University Of Maryland Baltimore County

NSF Awards · FY 2024 · 2024-10

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

NSF Awards · FY 2024 · 2024-10

This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. NSF LIGO’s detection of gravitational waves from a binary neutron star merger in coincidence with electromagnetic signals from gamma-rays to radio opened a window into the interiors of neutron stars. These stars contain matter under the most extreme conditions since the Big Bang. Long-lived continuous gravitational waves associated with pulsars and supernova remnants and short bursts associated with gamma-ray flares of highly magnetized neutron stars, once detected, will yield even more information on neutron star interiors. The planned Cosmic Explorer detector will push the frontiers forward by enabling detection of many neutron star mergers and probably a variety of other signals. The science goals of this award are to conduct cutting edge searches for these novel signals and improve the extraction of astrophysical information from present and future signals. This award supports the training of graduate and undergraduate students at a Hispanic Serving Institution. Students will be trained not only in gravitational physics, data analysis, and astrophysics, but in cutting edge computational and statistical techniques that are transferable to many areas of science and technology. This award also supports public outreach through the Cosmic Explorer web site. The main research activity of this award is searches for continuous gravitational waves from young neutron stars, including r-mode oscillations of known pulsars as well as searches of supernova remnants and other likely locations of non-pulsing neutron stars. This award also contributes to searches for a variety of signals including neutron star binary mergers and bursts associated with magnetar flares and to the extraction of astrophysical information from detections present and future. In preparation for the detection of non-merger signals the award supports exploration of detectability and what can be learned about neutron stars now and in the era of Cosmic Explorer. 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 2024 · 2024-09

Project Summary Posttraumatic stress disorder (PTSD) and opioid use disorder (OUD) co-occur at very high rates (33-66%). Untreated PTSD among people with OUD is associated with elevated risk of overdose, suicidality, and other serious outcomes. Exposure and other evidence-based treatments for PTSD are effective for patients with substance use disorders (SUD), including OUD, but rarely implemented in residential SUD treatment, representing a crucial missed opportunity to address PTSD symptoms. Written Exposure Therapy (WET) is a brief, evidence- based intervention for PTSD that shows promise in residential OUD/SUD treatment settings, but our pilot work has indicated a need for additional refinements to adapt WET for this setting. The proposed multisite study will build on the investigative team’s prior research projects that have explored WET for PTSD in the context of residential SUD treatment. We will use the 8-step ADAPT-ITT structure to create and test Written Exposure in Substance Treatment (WEST), an adapted version of WET for use with people with OUD in residential SUD treatment. We will obtain candid feedback from key stakeholders on the PTSD-related treatment needs of people in residential OUD/SUD care and the components, implementation, and candidate modifications of WET for residential OUD/SUD treatment, develop an initial adaptation of WET, obtain expert feedback from the developer of WET on the adapted version, and conduct an uncontrolled pilot test of the adapted version with N=20 participants. After this preliminary phase, we will examine the efficacy of the refined WET protocol in the context of residential OUD/SUD treatment by enrolling N=224 participants in a randomized controlled trial across two sites. In addition to the primary outcome of PTSD symptom reduction, we will test secondary and exploratory outcomes related to OUD/SUD outcomes, moderators and mediators of symptom reduction, and catalog facilitators and barriers of WET implementation in a residential OUD/SUD treatment setting. By expanding investigations of WET into residential OUD/SUD treatment settings, this project will significantly contribute to our knowledge base of practical strategies to provide comprehensive and integrated behavioral healthcare and reduce the impact of the current opioid epidemic.

NIH Research Projects · FY 2026 · 2024-09

PROJECT SUMMARY Social drivers of health (SDoH) are the largest factors affecting our health and wellbeing but are difficult for healthcare systems to address. The lack of healthy food, inadequate housing, and sparse social supports disproportionately affect individuals burdened by health disparities, both exacerbating chronic conditions and preventing people from receiving the care they need. The nearly 90 million Medicaid recipients are at particularly high risk with overrepresentation of individuals vulnerable to health disparities, including those with low or no income, racial or ethnic minorities, children, the elderly, or individuals with disabilities. Health systems face two problems when reaching beyond clinical care to improve patient health outcomes. The first problem is one of identification; providers undercode social needs in existing schemas and ancillary data collection methods such as social screens are not common, standardized, or easily shared. The second problem is a lack of engagement between individuals and social services, which is especially frustrating since there are many evidence-based practices that community-based organizations (CBOs) use to address social needs. Our project will apply a precision medicine approach to the identification of, and engagement with, Medicaid recipients with social needs. We will enhance the health information infrastructure of a managed care organization that coordinates benefits for over 250,000 Maryland Medicaid members by: ● Developing and deploying a set of machine learning models that use multiple individual- and community-level data sources to predict which members use the emergency department to fulfill social or non-urgent needs as opposed to treatment for urgent medical conditions. ● Developing and deploying an engagement support system that identifies and displays the characteristics of members that are predicted to prevent them from engaging with a CBO. ● Implementing a continuous qualitative and quantitative improvement process that identifies recurring themes and disengagement points in cases where members did not complete their social intervention. The study team is well positioned to develop a social needs intervention protocol and will include rigorous evaluations to assess the effects of our intervention on the health and social outcomes of participating members by their demographic and geographic characteristics. Together, our Aims will help identify and address social needs and shrink disparities in health outcomes across a large, high-risk population.

NIH Research Projects · FY 2025 · 2024-09

The kappa opioid receptor (KOR) is part of the opioid neuromodulatory system that influences pain and mood perception. KOR agonists have been recognized for their analgesia properties but are also associated with dysphoria, limiting their potential therapeutic use. Deciphering the intracellular signaling events activated by KOR that modulate the therapeutic and aversive effects may help aid in the development of novel compounds. KOR is a G protein coupled receptor (GPCRs) that is endogenously activated by opioid peptides. The balance of heterotrimeric G protein activation and deactivation is central in dictating its cellular and behavioral responses. Regulators of G protein signaling (RGS) proteins serve as an endogenous antagonist of GPCR signaling, determining the extent, and timing of their signaling and may offer a way to fine tune intracellular signaling. Our preliminary data demonstrates that the R7 RGS family modulates KOR signaling and influences KOR-mediated behaviors. In this proposal we will characterize the role of RGS7 on KOR signaling by identifying the neuronal population that drives KOR-mediated aversion and determine its impact of phosphorylation on KOR signaling. In Aim 1, we will identify the neuronal population that RGS7 acts to modulate KOR-mediated aversion. This aim will utilize a well-established conditional place aversion test and novel operant task to assess responses to aversive stimuli. Aim 2 will investigate the impact phosphorylated RGS7 has on KOR signaling and KOR- mediated aversion. We will determine the extent to which phosphorylation affects the interaction with its binding partner R7-binding protein (R7BP), which is required for the stability and catalytic activity of R7 RGS proteins. In Aim 3 we will determine the contribution of G protein signaling on KOR-mediated behaviors. This proposal will utilize a combination of transgenic mouse model as well as viral and chemogenetic approaches to target RGS protein in a cell-specific manner and employ novel aversion behavioral tests. At the successful completion of the proposed research, the expected outcomes are to determine the contribution of the RGS-G protein pathway towards specific KOR-mediated behaviors and delineate the cell-specific contributions. The proposed research is innovative as we use an operant paradigm to employ aversive stimuli that allows us to address the long-term effects within a homecage environment. These results provide a strong basis for furthering our knowledge of KOR signaling and to delineate the impact of RGS-G protein signaling on aversive behaviors which is expected to have significant impact on the future therapeutic development.

NSF Awards · FY 2024 · 2024-09

This project aims to uncover the secrets behind the growth of supermassive black holes (SMBH) located at the centers of galaxies. These enormous black holes are crucial for understanding the evolution of the Universe, as they significantly influence their host galaxies by affecting star formation and the development of galactic structures. The primary focus is to explore how SMBHs grow, either through the accumulation of matter or by merging with other black holes. The research will also provide educational opportunities and foster diversity within the scientific community. By engaging with the Native Hawaiian community and other underrepresented groups, the project aims to inspire and nurture the next generation of scientists. The project's main goal is to understand the growth mechanisms of supermassive black holes. It has three specific objectives: 1) to determine the merger rates of SMBHs and relate these to gravitational wave observations; 2) to investigate the growth of SMBHs during the peak period of black hole activity (known as cosmic noon) and its connection to accretion rates; and 3) to map the complete history of SMBH accretion. The research employs a multiwavelength survey approach, leveraging optical, infrared, and X-ray observations to minimize bias against obscured black holes. Advanced AI techniques will be used to analyze large imaging surveys, identifying dual active galactic nuclei (AGN) and galaxy mergers. This will help predict gravitational wave events and measure black hole masses and luminosities. Data will be collected using various observatories, including privileged access to Euclid data and several ground-based telescopes. Results will be shared through AGN-DB, an AI-managed database, and supported by tools like THALES and AGNFinder for comprehensive data integration. The project also includes significant educational outreach and diversity initiatives, providing research experiences and professional development for underrepresented students in astronomy. 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

Massive hot stars are the greatest sources of energy and new material in the Galaxy. A collaboration of astronomers at the Monterey Institute for Research in Astronomy (MIRA), Florida Gulf Coast University, The SETI Institute, the University of Maryland Baltimore County, and the University of Wisconsin Madison, along with their international partners aim to determine the interior structures of these stars by the application of a new technique: polarimetric asteroseismology. Seismic waves bounce around the interiors of stars disturbing their surfaces as if in a perpetual star-quake. The collaboration will make observations of this phenomenon in key stars in tandem with ground- and space-based telescopes (including the NASA TESS mission). A new network of the World's most sensitive polarimeters, spanning a third of the Earth will be used to detect the surface oscillations caused by these seismic waves. The team will also build on established code to develop sophisticated new computer models to interpret the multi-faceted data. College undergraduate and high school students, including some from traditionally under-represented groups, will assist with the project and gain their first hands-on experience of observational astronomy and modeling. Citizen scientists will also be involved, and the project will form part of MIRA's public education programs. A very extensive data set will allow the team to determine the interior structures of about 10 beta Cephei and Slowly Pulsating B-type stars in various stages of evolution. This will be enabled by a large-scale coordinated high-precision polarimetric observing campaign. To achieve the needed phase coverage, it will involve multiple observatories, all equipped with state-of-the-art PICSARR polarimeters. To obtain the necessary S/N and frequency resolution (which depends on temporal baseline) will require many thousands of new polarimetric observations spanning more than 2 years, matched to corresponding photometry and spectroscopy – including new and archival data. The observations will be followed by an intensive multi-part analysis involving sophisticated radiative transfer modeling. Integral to the work is the creation of a new software program that combines pulsating star and polarized radiative transfer codes to properly account for the significant effects of rotation. This program will make mode identification feasible using polarimetry in rapidly rotating stars. The results will enable stellar evolution models to be properly calibrated and extrapolated to the supernova stage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Neurodegenerative diseases (for example, Alzheimer's disease, Parkinson's disease, multiple sclerosis) impact millions of people in the United States and result in hundreds of thousands of deaths. These disorders can affect people of all ages, although they are more common in older adults. Digital twin models, leveraging the exponential growth of biomedical data and artificial intelligence and data science techniques, are opening exciting avenues to obtain new insights into these diseases and revolutionize their treatment and prevention. The investigators will address multiple problems on this interface, and develop data science-driven theoretical foundations, methodological tools and algorithmic principles for several aspects of digital twin models towards better understanding of digital twins as a whole, and in particular in the context of their use in neuroscience and in prevention, treatment and better understanding of neurodegenerative diseases. They will also address the ethical, legal, and social implications of using digital twin models in the context of healthcare in general, and in studying neurodegenerative diseases using magnetic resonance-technology driven images (MRI) in particular. This research will greatly aid in the deployment of digital twins in medical and healthcare practice, and will significantly advance neuroscience and the study of neurodegenerative diseases. The investigators will address open problems in low-dimensional manifold learning, causal pathway searches and feature discoveries and selections, and develop multiple techniques for verification, validation and uncertainty quantification of digital twins using Bayesian techniques, data assimilation, resampling, empirical likelihood methods and topological data analysis. They will also develop dynamical system models, incorporating observational image data, for computational efficiency and synthetic data generation for ethical use of artificial intelligence and digital twin technology in studying neurodegenerative diseases. Additionally, they will develop knowledge graph driven systems for use by regulatory and other healthcare monitoring agencies for de-risking and easy implementation of data-driven modern technologies. The investigators will work in conjunction with regulatory and other healthcare governing agencies towards better understanding of neurodegenerative diseases and successful deployment of data-driven technologies to mitigate suffering from such diseases. The investigators will mentor, train and teach students on various aspects of digital twins, data science and neuroscience and their interconnections, and will help build a highly skilled workforce on these 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 · 2024-09

Understanding how galaxies formed and evolved to produce the Universe we see today is a high priority for the astrophysics community. The investigators will study the role played by super-massive black holes. These reside at the centers of most galaxies, which become "active" when gas, dust, and even stars fall into the black hole. This project addresses several aspects of the “active” galactic nuclei (AGN), such as the nature of the very compact X-ray-emitting region known as the corona, the mechanism that launches highly relativistic radio-wave-emitting plasma jets in a subset of AGN, and the origin of radio-wave emission in radio-quiet AGN. This project seeks to determine the origin of the radio emission and to use it as a probe of the super-massive black hole. This program will provide training opportunities for junior scientists, supporting postdocs, a graduate student, undergraduate researchers and summer. The project's research and educational work will also benefit the larger community through outreach activities in partnership with the UMBC Campus observatory, local schools, and artists in the NASA/MICA partnership program. Several physical processes can contribute to the radio emission observed from AGN, including extreme star formation, hot gas associated with the compact X-ray emitting "corona" very near the black hole, shocks from relativistic winds, and nascent, small-scale, and/or failed relativistic jets. This project includes two complimentary investigations. The first is focused on a comprehensive study (multi-band radio and X-ray) of a statistically complete sample known as the PG quasar sample. The second investigation focuses in detail on two sources that are representatives of the rare class of "changing look" AGN, and for which we have recently amassed a very considerable amount of high-resolution radio imaging. Through both high-cadence time-domain investigations, highly coordinated multiwavelength observations, and multi-resolution radio observations, this project will lead to a better understanding of how the central engine functions in active galaxies. Identifying the processes contributing to the observed radio emission under different physical conditions and locations in AGN parameter-space will help us constrain models about AGN accretion, coronal emission, wind launching mechanisms and jet formation, all of which are major open questions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in their efforts to significantly increase the numbers of students matriculating into and successfully completing high quality degree programs in science, technology, engineering and mathematics (STEM) disciplines in order to diversify the STEM workforce and supports the production of scholarly research in STEM broadening participation. Particular emphasis is placed on transforming undergraduate STEM education through innovative, evidence-based recruitment and retention strategies, and relevant educational experiences in support of racial and ethnic groups underrepresented in STEM disciplines: Blacks and African Americans, Hispanic and Latino Americans, American Indians, Alaska Natives, Native Hawaiians, and Native Pacific Islanders. These strategies facilitate the production of highly competitive students motivated to pursue graduate education or careers in STEM. For the United States to remain globally competitive, it is vital that it taps into the talent of all its citizens and provides exceptional educational preparedness in STEM areas that underpin the knowledge-based economy. The University of Maryland, Baltimore County (UMBC) is a partnering institution with the University System of Maryland LSAMP. The UMBC Bridge to the Doctorate (BD) program will increase the participation of underrepresented groups in STEM doctoral programs addressing the national need for a more diverse and expansive STEM workforce. To ensure success of the BD Fellows, the project will provide research and mentor training, connections to graduate school organizations, peer mentorship and access to a robust alumni network. In addition, Fellows will have a unique opportunity to receive primary instructor experience, a meaningful advantage on the path to the professoriate. These activities will provide the Fellows with innovative ways to enhance their preparation for advanced level STEM careers. Program outcomes to be assessed annually include retention, GPA, credits completed, time-to-degree, completion of program milestones, number of publications and conference presentations, and number of external fellowships. Longer-term outcomes will include matriculation into Ph.D. programs for the Fellows and obtaining a position in the STEM workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The brain has the amazing ability to bring information together to make associations, which is important for learning how to gain things that are beneficial or pleasurable and avoid things that are harmful. Forming learned associations between rewarding stimuli like food and the circumstances under which those stimuli are encountered is necessary for survival, yet how the brain establishes and maintains these associations remains elusive. Neurons in the brain communicate with one another to regulate behavior, and one neuron often receives multiple inputs from other neurons, which underlies the brain’s ability to bring together information. The ability to modify the communication between neurons is a core mechanism by which momentary experiences can be transformed into long-lasting memories. However, investigations of the changes in neuronal communication that mediate behaviors like learning and memory have largely focused on one set connections at a time leaving a significant gap in our understanding of how different inputs converge to integrate information. The proposed work will utilize cutting-edge approaches to manipulate different neuronal connections to determine how two different inputs interact within a single neuron. The results of this study will answer the fundamental question: how does information come together in the brain? These findings have significant implications for how we understand brain function and how the brain brings together information to regulate behavior. In addition to scientific advancement, execution of this work will foster the development of trainees from diverse backgrounds through hands-on research opportunities and professional development. The main objective in this proposal is to determine the mechanisms responsible for mediating and coordinating plasticity at hippocampus (Hipp)- nucleus accumbens (NAc) synapses. This is a key site of convergence between spatial and contextual information and reward processing where plasticity is a critical mediator of motivated behavior. Hipp input consists of two independent pathways emanating from dorsal (dHipp) and ventral (vHipp) subregions with the prevailing belief that their innervation of and influences on NAc function are largely distinct. However, preliminary data demonstrate dHipp and vHipp innervate overlapping regions in the NAc. Individual neurons in these areas of overlap can respond to both dHipp and vHipp input, and plasticity at one synapse modulates responses in the other. These observations challenge the conventional belief that these two pathways are entirely independent and raise new questions regarding the mechanisms underlying plasticity at dHipp-NAc and vHipp-NAc synapses. Viral-mediated approaches will be used to specifically label and manipulate dHipp-NAc and vHipp-NAc pathways in the mouse brain to test the central hypothesis that dHipp and vHipp pathways converge in the NAc, and interactions between synapses coordinate plasticity within individual neurons. Successful completion of the proposed work will establish novel mechanisms underlying activity-dependent synaptic plasticity and their coordination within individual neurons that will be key to defining neuronal mechanisms that underlie motivated behaviors. Our multidimensional perspective will provide novel insight into the complex mechanisms responsible for mediating reward learning with substantial implications for advancing knowledge of the fundamental principles of brain function. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Quantum computing has emerged from science fiction into reality, promising transformative advancements across various sectors, including but not limited to aerospace, drug design, machine learning, and cybersecurity. However, quantum computing is a double-edged sword for cybersecurity. While it can potentially enhance security protocols, criminals can leverage its power to break modern cryptography and launch quantum attacks. Recognizing these challenges, the National Security Agency’s roadmap mandates a 10-year migration plan to post-quantum cryptography (PQC) for all national security systems. This initiative highlights an urgent need for skilled professionals in quantum information science and technology (QIST), specifically PQC. Yet, academia currently lacks the pedagogical infrastructure to meet this demand, particularly for STEM students across multiple pathways, creating a gap between educational preparation and industry needs. The Active and Immersive Learning of Post-Quantum Cryptography for STEM Students (AIM-PQC) program bridges the gap for STEM students across multiple pathways and pans out their career paths in QIST by offering innovative education and training models. This program comprises a six-week on-ramp training workshop featuring active and immersive learning, followed by eight weeks of applied experiential learning through internships with industrial partners or research labs, complemented with community building and mentoring activities. This structure not only equips participants with critical skills but also integrates them into a supportive community, fostering a pipeline of talented individuals ready to contribute to national security in the era of quantum computing. The AIM-PQC program will bring a cohort of community college students, transfer students, undergraduate and graduate students, instructors, researchers, companies, and government agencies to build a QIST community collectively, reinforcing the leadership of the USA in the global quantum race. This project aligns with the NSF ExLENT Program, funded by the NSF TIP and EDU Directorates, as it seeks to support experiential learning opportunities for individuals from varied professional and educational backgrounds to increase their interest in, and their access to, career pathways in emerging technology 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 · 2024-07

This project conducts exploratory development towards a prototype Community Infrastructure to Strengthen AI for Audio Deepfake analysis (CISAAD) particularly for English language audio by increasing access and availability to datasets and support for audio deepfake analysis. Deepfakes, or AI generated content, are widely recognized as a major societal concern and challenge. This project 1) addresses the challenges of limited data availability and human augmented data through open datasets shared by the community, 2) enables both single and multi-speaker deepfake analysis across various use cases, and 3) addresses ethical, social, and political challenges associated with deploying deepfake technology developed from open-sourced community data. The development of CISAAD will advance cybersecurity research and information trustworthiness focusing on audio deepfakes, an underexplored type of AI generated content, and employs a new transdisciplinary approach to strengthening AI models by incorporating human knowledge. CISAAD plans to strengthen AI for English language audio analysis in both generative applications, such as voice reconstruction, and discriminative applications, such as audio deepfake detection. The project advances the current state of the art in deepfake analysis by enabling unique and compelling research opportunities in audio deepfake analysis otherwise inaccessible to the CISE research community, such as human knowledge-augmented deepfake models, auto-annotation of linguistic features, and multi-speaker deepfake models, in addition to single speaker use cases. The work will help the community in understanding audio deepfake as a societal concern and challenge, and also offer opportunities for content generation in positive applications such as voice restoration and smart and connected community research. With an interdisciplinary team across AI, linguistics, cyber infrastructure and human centered computing, the project aims to do the exploratory work towards an innovative infrastructure for expanding research informed by types of audio deepfakes. Together, our research and dissemination efforts expand formal and informal learning in AI and STEM fields related to cybersecurity analytics at the intersection of technology, language, behavior, and society. The principles developed through this project will expand to multiple types of deepfakes and support media and communications experts working to address challenges related to information integrity. CISAAD is available at https://cisaad.umbc.edu as a prototype community resource; it includes a deepfake data catalog and repository for English audio data, tools and models for deepfake audio analysis use cases, and educational materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY Oxygen is essential for the life of many organisms, as it used during aerobic respiration to generate cellular energy in the form of adenosine triphosphate (ATP). Low oxygen (whether caused by environmental or cellular hypoxia) prevents adequate ATP production and can consequently cause cell death. Paradoxically, excessive oxygen, which often occurs post-hypoxia, is equally damaging, due to a surge in reactive oxygen species (ROS) that is damaging to biomolecules. Hence, aerobic organisms such as humans are susceptible to fluctuations in oxygen levels, which is manifested in many disease states, including chronic kidney disease, acute kidney injury, sickle cell anemia and stroke. Stroke alone was the 5th leading cause of death in the US in 2017. Thus, an understanding of how cells can adapt to changes in environmental oxygen under different circumstances, and how this response can be manipulated therapeutically, is a major challenge for improving human health. Despite the pathology caused by low O2, evolutionarily conserved mechanisms exist that protect organisms from hypoxic injury. One such mechanism is metabolic suppression, which involves the downregulation of energy-demanding processes to match reduced ATP production. The signaling pathways that trigger and maintain metabolic suppression are mostly unknown. The Brewster laboratory recently uncovered a novel role for the adapter protein N-myc Downstream Regulated Gene 1a (Ndrg1a) in promoting long-term adaptation to anoxia (zero oxygen) in the zebrafish embryo via metabolic suppression. The specific aims of this proposal are to deepen our mechanistic understanding of how Ndrg1 functions. Specifically, we will: (1) Uncover how Ndrg1 regulates trafficking of the sodium-potassium ATP-ase (NKA) pump in response to anoxia and re-oxygenation; (2) Investigate the broader function of Ndrg1a and Ndrg3a in hypoxia adaptation; (3) Determine whether lactate is necessary and sufficient to prime cells for anoxia survival. In addition to these research goals, the PI will leverage the University of Maryland Baltimore County's outstanding commitment to Inclusive Excellence and her own position as the Director of the Graduate Research Training Initiative for Student Enhancement (G-RISE) Program to enhance the recruitment and retention of PhD students from historically underrepresented (HUR) groups at UMBC and will train HUR students at all levels in hypoxia research in her laboratory. These students will be an integral part of her research group; will work on independent projects and author publications.

NIH Research Projects · FY 2025 · 2024-07

Project Summary/Abstract The overall goal of this project is to understand how HIV-1 packages its RNA genome. During virus assembly, two copies of the viral RNA are trafficked to the plasma membrane and anchored to assembly sites by a small number of viral Gag polyproteins (~24 or fewer). The resulting complex functions as a nucleant for further virus assembly, recruiting additional Gag molecules and promoting budding. Although mechanistic and atomic level details are unknown, studies suggest that assembly of the Gag:RNA complex is mediated by a combination of intermolecular interactions between the capsid (CA) domains of Gag, and between Gag’s nucleocapsid domain (NC) and an RNA packaging signal (ΨCES) located within the 5′-untranslated region of the genome. The proposed studies aim to (i) identify the structural determinants and mechanism of Gag assembly on the viral RNA packaging signal during different stages of assembly (supported by the K99). Additionally, the proposal aims to (ii) collect more structural information of host cell factor interaction, specifically Staufen1, with the viral RNA (R00), raising potential novel target sites for drug development. The K99 research, conducted under the mentorship of Dr. Michael F. Summers (primary mentor at UMBC) and Dr. Owen Pornillos (secondary mentor at the University of Utah), will employ an integrated structural biology approach, where isotope-edited nuclear magnetic resonance spectroscopy (NMR) will be used to determine key intermolecular Gag:Gag and Gag:RNA interactions that promote assembly, and Cryogenic Electron Microscopy to visualize atomic-level details of the Gag:RNA complex that nucleates virus assembly. Mechanistic hypotheses will be tested by in-cell studies under the guidance of virologist Dr. Alice Telesnitsky (member of the advisory committee at University of Michigan). Expertise developed in NMR, Cryogenic Electron microscopy, and cellular techniques will prepare me for an independent career to work within the broader area of host-virus RNP interactions. Overall, these studies will provide the first structural and mechanistic insights for the ribonucleoprotein complex that nucleates HIV-1 assembly and its interactions with essential host factors and will provide Dr. Hollmann skills to establish an independent and inclusive research program.

NSF Awards · FY 2024 · 2024-06

Non-convex optimization problems are ubiquitous in science and engineering. They often present significant challenges for many existing classes of algorithms due to the presence of multiple suboptimal, undesirable solutions. The methods emerging from this project will circumvent some of these challenges due to their ability of bypassing more efficiently suboptimal solutions using a novel set of techniques. They will contribute to the numerical solution of non-convex optimization problems that can be found in a very wide range of applications, such as computer-aided design (shape and topology optimization), radiation therapy, optimization of manufacturing processes, inverse problems, optimal control of partial differential equations, statistics, and artificial intelligence. Open software will be shared with the community in order to facilitate the reproducibility of the results. One summer undergraduate student and one graduate student will benefit from training in areas that are relevant to topics of current interest to both academia and industry. Special attention will be given to the recruitment of students from underrepresented groups. The project is centered around developing and analyzing a novel class of first order methods for solving optimization problems, called low-rank-gradient-flow (LRGF). The idea behind the method consists of developing, at each step, quadratic surrogates with low-rank Hessian, and computing analytically the gradient flow on that surrogate. The step will conclude with a line search along the curvilinear gradient flow, with the purpose of finding a point satisfying the Wolfe conditions. Convergence will be accelerated using a multilevel approach based on reduced order models. The convergence properties of the method will be studied, addressing both questions related to global convergence, efficient construction of low-rank models, as well as convergence rates. The method will be applied to maximum likelihood estimation, optimization of hyperbolic partial differential equations, and training of deep neural networks. 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 · 2023-09

PROJECT SUMMARY The deterioration of protein homeostasis is a signature of aging cells and underlies the etiology of numerous aging-related diseases. Proteostasis is normally maintained through a well-coordinated network of factors that include protein synthesis regulators, molecular chaperones that promote proper folding, quality control and proteolytic degradation machinery. This proteostasis network serves to prevent the accumulation of misfolded, non-functional, and aggregated proteins, which is particularly critical during stress and as cells age. Proteins which function within the proteostasis network are highly-regulated by different mechanisms including their post- translational modification. The modification lysine methylation has been identified on many factors that contribute to proteostasis, though the molecular role for methylation within the proteostasis network remains poorly-defined. The conserved SMYD family of lysine methyltransferases are known to methylate factors important to proteome integrity, such as chaperones, although their function in maintaining proteostasis is not well-understood. We have investigated the function of an orphan yeast SMYD lysine methyltransferase, Set6, and determined that it is a critical regulator of the proteostasis network via its methyltransferase activity. The overarching goal of the proposed work is to identify the specific regulatory role for Set6 in proteostasis and to define its catalytic activity on factors important for proteome integrity, including the cytosolic chaperone Hsp70. To address this, we propose to use a series of molecular and cell biological assays to dissect the molecular contribution of Set6 and its catalytic activity to the proteostasis network under normal and stress conditions. We will also use quantitative proteomics, structure-function analysis, and genetic approaches to define regulatory mechanisms directing the activity of Set6 under normal and stress conditions, with a focus on its protein-protein interactions. Finally, we will define the molecular consequences of Set6-dependent methylation on Hsp70 using molecular, biochemical, and genetic approaches, and test the hypothesis that methylation by Set6 alters the Hsp70 interactome during stress. We will also investigate whether Set6 has additional substrates in the proteostasis network using biochemical, proteomic, and genetic assays. Altogether, these studies will reveal new regulatory mechanisms governing the proteostasis network and will advance our understanding of the roles for SMYD enzymes in proteome integrity. This work will also provide insight into aging-related pathologies characterized by misfolded or aggregated proteins and may uncover new mechanisms that can be targeted therapeutically to promote healthy proteostasis as cells age and prevent age-dependent diseases.

NIH Research Projects · FY 2024 · 2023-09

PROJECT ABSTRACT Reliable diagnostic digital tools are needed for the early detection of cognitive dysfunction and stratification of early Alzheimer’s Dementia (AD) risk among older adults at risk for Type 2 diabetes (T2DM). T2DM is a well- known accelerator of cognitive decline and AD risk: T2DM is linked to dysfunction in episodic memory and executive functions, and proffers a 2- to 4-fold increased risk for AD. The prediabetes stage may be key to understanding this accelerated aging as it could provide an optimal window into the initial pathophysiological changes that trigger cognitive dysfunction and AD. However, uncertainty surrounds the role of hyperglycemia in the prediabetic stage, perhaps because only assessing peaks in glucose sporadically using single time-point measurements like hemoglobin A1c (HbA1c) and fasting glucose leaves key aspects of dysglycemia unexamined. These limitations open the possibility that more precise measurement of dysglycemia will yield a more definitive understanding of the mechanisms by which T2DM pathophysiology modifies cognitive function and AD risk, which are presently unknown. We will be the first to leverage cutting-edge Continuous Glucose Monitoring (CGM) technology to investigate the precise associations between dysglycemia, cognitive function, and key AD biomarkers in older adults with at risk for T2DM. CGM allows for the precise assessment of fluctuations in glucose levels to show individualized patterns of hyper- and hypoglycemia over days- a major component of dysglycemia not reflected in fasting glucose or HbA1c. Because CGM technology has almost exclusively been used by people with a T2DM diagnosis, examining those at risk for T2DM is innovative. Our established multidisciplinary research team, with expertise in behavioral medicine, endocrinology, geriatrics, neuropsychology, and neurology, and numerous years of collaborative clinical research experience, is well- positioned to examine among 40 older adults at risk for T2DM (a) the association of glycemic fluctuations with cognitive dysfunction in episodic memory and executive functions, key domains that show decrements both early in the AD trajectory, and in prediabetes, and (b) explore, for the first time, the association of glycemic fluctuations with well-established biomarkers of early AD risk. These plasma-based AD biomarkers of tau phosphorylation and amyloid burden are cost-effective, require minimally invasive blood draws, and minute amounts of brain-specific proteins in blood for use with ultrasensitive immunoassays. By leveraging precise, scalable technology to assess early glycemic fluctuations, and sensitive screening tools for early AD risk, this innovative proposal stands to make both scientific and technological advances in aging and AD risk research. Support for our hypotheses would introduce cost-effective, user-friendly CGM technology as a novel, sensitive, digital biomarker for the early detection of cognitive dysfunction and stratification of AD risk, ultimately, helping older adults preserve cognitive function into later life.

NIH Research Projects · FY 2025 · 2023-08

Project Summary Our main goal is to understand how internal ribosome entry sites (IRESs) and 3' cap-independent translation enhancers (3'CITEs) promote cap-independent translation of genomes in (+)-strand RNA viruses. Despite highly diverse sequences and predicted secondary structures, IRESs and 3ʹCITEs from evolutionarily distant viruses recruit the same components from the host to initiate genome translation. Structural information for viral IRESs that bind directly to the ribosome is limited and understanding of 3-dimensional (3D) structures and interactions of IRESs and 3'CITEs that promote translation by other mechanisms is largely unknown. We will use X-ray crystallography to determine the high-resolution crystal structures of these RNAs, focusing on the type I and type II picornaviral IRESs and tombusvirus 3'CITEs due to their unique mechanisms of recruiting translation initiation factors and the ribosomal subunits through a multistep, dynamic assembly process using modular RNA domains. Our strategy employs Fab fragments as chaperones to crystallize and determine structures of RNAs and RNPs, an extension of a technology that PI helped develop as a postdoc. Since moving to UMBC, we have obtained crystals of coxsackievirus IRES domain V (an example of type I IRES) in a complex with Fab BL3-6 that diffracted to 3.36 Å resolution (the first 3D structural information for type I IRESs). A partial structure of the dV contains a 3-way junction analogous to that observed in cardiovirus J-K and hepatitis A virus dV structures (PI's previous work). Optimization of crystallization conditions to obtain high-resolution diffraction data, analysis of SAXS data to access in-solution structural information, and purification of the human eIF4G HEAT-1 domain for binding studies are underway. Recently, we solved the 2.9 Å resolution crystal structure of a T-shaped domain of saguaro cactus virus 3ꞌCITE in a complex with Fab BL3-6 and characterized its binding with human eIF4E. Many viruses within the tombusviridae family contain 3ꞌCITEs with similar domains, suggesting that these RNAs adopt a shared topology to mimic mRNA 5ꞌ-cap for binding eIF4E. We are thus poised to determine the crystal structures of different kinds of viral IRESs, 3'CITEs, and some cellular IRESs. The Fab approach has successfully solved the crystal structures of several RNAs, but it has not been demonstrated for RNP complexes; the second goal is to integrate this Fab-assisted technology to crystallize and determine the structures of RNP complexes. The third goal is to create anti-RNA single-chain variable fragments (scFvs) as unique probes for RNA visualization to facilitate the localization, tracking, and quantification of viral RNAs in host cells. We have obtained promising preliminary data in this direction, including the development of three anti-RNA scFvs and their scFv-GFP fusions based on the existing anti-RNA Fabs. The scFvs with and without GFP tags bind the cognate RNA targets with affinities similar to their parent Fabs. When taken together, the proposed studies will provide deeper insights into the mechanisms of cap-independent translation initiation in (+)-sense RNA viruses and unlock opportunities for developing RNA-targeted therapeutics against these pathogens that cause human, animal, and plant diseases.

NIH Research Projects · FY 2025 · 2022-08

ABSTRACT My laboratory has been dedicated to developing physiologically relevant yet easy-to-apply tissue modeling technologies, and exploring the interactions between extracellular matrix (ECM) microstructures and cell metabolisms. Building on recent successes and discoveries, we propose to develop a more advanced technology for liver modeling, and to profoundly study how fibrosis-relevant ECM microstructures can impair hepatic metabolism—the former aims to reduce the monetary/time costs and human subject risks in new drug development, and the latter will provide new understanding and metabolic targets for fibrosis treatments. Although various microfluidic liver models have been reported, they lack the critical compositions of the physiological liver, namely, all the necessary cell types, the relevant architecture, and physiological 3D ECMs. Both literature and our preliminary results suggest the crucial roles of these components in maintaining hepatic functions and homeostasis, which may explain why current liver models could only mimic part of the functions. Integrating the various cell types, the cellular architecture, and the 3D ECMs represents challenging hurdles by the available technologies. Therefore, we propose an innovative technology to model the liver with a new fabrication logic, workflow, and set of technical means. This technology will recapitulate the most liver niche properties heretofore, but with relatively simple and straightforward operations (setting up, maintenance, analyses, etc.). We recently reported for the first time that ECM microstructures could modulate metabolic activities in various cell types. Based on this, we propose to set up ECM controls that mimic healthy and fibrotic conditions, and thoroughly investigate how the aberrantly remodeled ECMs can impair hepatic metabolisms. Mechanistic studies involving integrins and AMP-activated protein kinase are also planned. To summarize, there has not been a tissue modeling technology like the proposed one; and others have not reported the interactions between ECM microstructures and cell metabolism. The proposed work, therefore, represents high novelty and my laboratory’s unique space in the field. Completing the proposed studies will be significant for pharmaceutical developments because a new testing/screening platform and metabolic (metabolites and the controlling proteins) targets will be provided for future fibrosis therapies.

NIH Research Projects · FY 2025 · 2022-08

PROJECT SUMMARY Fungal infections significantly impact human health, both in terms of mortality and treatment cost. While anti- fungal drugs have been the leading therapy for fungal infections, there is an increasing incidence of resistant fungal infections that are difficult to treat. An alternative approach to disrupting fungal cell wall synthesis with drugs is the active degradation of fungal cell wall polysaccharides. However, there is a substantial knowledge gap in regards to the requirements for effective fungal cell wall degradation. This shortfall prevents the development of new anti-fungal therapies that could be used alone or in combination with current drug treatments. The long-term goal of this project is to develop mechanistic understanding of polysaccharide deconstruction to produce medically relevant enzymes. The objective of this particular proposal is focused on identifying and characterizing the mechanisms for the degradation of fungal cell wall polysaccharides by the bacterium Cellvibrio japonicus. Our central hypothesis is that a coordinated suite of enzymes is required to effectively degrade the glucan and chitin components of fungal cell walls. We will test this hypothesis with three Specific Aims: (1) Multiomic analyses during degradation of fungal cell wall polysaccharides, (2) Functional analysis of genes that encode enzymes essential for fungal cell wall deconstruction, and (3) Quantitative enzymology of fungal cell wall degrading enzymes. For the first Aim, we will use established transcriptomic and proteomic methods to decipher the complex gene and protein expression patterns of C. japonicus when actively degrading the fungal cell walls of Aspergillus nidulans and Saccharomyces cerevisiae. Novel targets will be placed in a functional context by subsequent genetic analysis. The second Aim will determine the contribution of individual gene products for the deconstruction of fungal cell walls. We have established both transposon and high-throughput targeted mutational approaches to identify and analyze genes that are essential for polysaccharide degradation in C. japonicus. We will test the fitness of mutant strains lacking these genes with growth assays using insoluble fungal cell wall polysaccharides and intact fungal biomass. For the third Aim, we will purify and characterize enzymes capable of degrading fungal cell wall polysaccharides to determine their substrate specificity, kinetic parameters, and to assess enzyme synergy. The use of fungal biomass as a substrate will allow us to determine what enzyme combinations are maximally effective at deconstructing intact fungal cell walls. These approaches are innovative because we use a bacterium that has a robust polysaccharide degrading capability coupled with a novel screen that uses intact fungal biomass, which includes filamentous fungi and yeasts. This project is significant because it will characterize enzymes with medically-relevant properties, give mechanistic insight into the requirements for the effective disruption of fungal cell walls, and generate a powerful system for the discovery of enzymes that have anti-fungal potential.

NIH Research Projects · FY 2025 · 2022-05

PROJECT SUMMARY The purpose of this application is to obtain funding to support 36 doctoral trainees per year in participating departments/programs at UMBC (Biology, Biochemistry, Chemistry, Chemical, Biochemical and Environmental Engineering, Mechanical Engineering, Computer Science and Electrical Engineering, Physics and Psychology). The G-RISE trainee pool will include students who are underrepresented in STEM fields; specifically, racial and ethnic minorities, and students with disabilities. The specific measurable outcomes are to (1) maintain high enrollment and retention rates in the program, (2) increase research productivity, (3) improve performance in core competencies, (4) enhance career readiness, (5) promote an inclusive research environment, and (6) increase faculty participation in G-RISE activities. G-RISE at UMBC will provide comprehensive financial support in years 1 and 2. Competitive funding can be obtained in year 3 of the program based on the assessment of mentor/mentee proposals to advance research while enabling the trainee to acquire skills and experience relevant to career interests. Students will be recruited regionally and nationally through outreach activities, participation in recruitment events, social media presence, and presentations at scientific conferences. Recruitment will be carried out in a holistic manner, as a joint effort between the participating departments and the G-RISE team. Retention, which is already very high, will be maintained at current levels. The G-RISE program will enhance career development through individualized mentoring. Trainees will enroll in the required Summer Bridge to enhance writing skills and facilitate transition into graduate school. Trainees will also complete a course on the Responsible Conduct of Research and attend several workshops: career exposure, grant-writing, quantitative skills, and transition into the workplace. They will also participate in community-building activities, monthly meetings, and annual retreats. In addition, trainees will have the opportunity to carry out elective, hands-on training and take on additional coursework to prepare them for careers of interest. Faculty mentors will be expected to participate in mentor training and G-RISE- sponsored activities that seek to promote an inclusive environment. The efficacy of the program will be monitored regularly by analysis of performance and trainee and alumni surveys.

NIH Research Projects · FY 2025 · 2021-03

PROJECT SUMMARY There is a lack of fundamental understanding on how microbial breakdown of chlorinated organic compounds is influenced by the presence of sorptive surfaces. Several laboratory and field studies have demonstrated a synergy between sorptive materials and microorganisms leading to the development of material aided delivery of bioamendments in both groundwater and sediment applications. However, a mechanistic understanding of the relationship between sorptive surfaces and microbial dechlorination is lacking. To fill this critical knowledge gap, this research team of chemical/environmental engineers and microbiologists will investigate the fundamental mechanism of microbial dechlorination of chlorinated organics on sorptive surfaces and develop quantitative models that allow optimization and engineering scaleup of enhanced bioremediation aided by materials engineering. Improved understanding will allow better prediction of the degradation of sorbed chemicals in the environment and enable optimization of material science aided technologies for the delivery of biodegradation technologies. The project will target chlorinated organics ranging from less hydrophobic compounds like chloroethenes typically associated with groundwater and strongly hydrophobic compounds such as PCBs typically associated with sediments. These pollutants will be investigated individually as well as in mixtures that are commonly encountered at Superfund sites. A set of carbon-based sorbent materials will be produced in the laboratory to provide a range of physical and chemical properties. In addition to the lab synthesized materials, two most commonly used activated carbons (bituminous coal based, and coconut shell based) and graphite will be tested in parallel for comparison. Through systematic laboratory experiments, the physical and chemical properties (such as specific surface area, pore size distribution, electron accepting capacity, and carbon content) will be evaluated for influence on the sorption characteristics and synergy with biodegradation of chloroethenes and PCBs. Final material selection will also be guided by environmental sustainability considerations. Sorption and biokinetics data from the experimental studies with optimized materials will be synthesized into advanced site models to predict material behavior for field-scale remedial applications. Results from the modeling simulations will allow for optimization of the engineering design for pilot and full-scale applications at contaminated groundwater and sediment Superfund sites. This platform of combining tailored materials with biodegradation will be adaptable for targeting other pollutant mixtures.

NIH Research Projects · FY 2026 · 2020-09

Project Summary The regulation of biological growth, size, and whole-body shape during development and regeneration requires complex interactions between molecular signaling mechanisms, biophysical forces, and cellular behaviors. However, the subjacent feedback loops and nonlinear interactions between these different levels of regulation prevent us from obtaining a comprehensive mechanistic understanding of how organisms develop and maintain their shape and form. There is a pressing need for integrative approaches to aid in our ability to understand and control the mechanisms of large-scale tissue growth and shape formation towards novel therapies for birth defects, injuries, and cancer. The overall objective of this research program is to achieve a mechanistic understanding of the genetic regulation and coordination of large-scale tissue growth and shape formation by developing and implementing a novel integrated systems biology approach. We will leverage the outstanding regenerative and plasticity capabilities of the planarian worm, which can restore all its organs and whole body after almost any surgical amputation, in addition to maintaining their correct proportions as they grow when fed, shrink when starved, or regenerate when injured. We will develop a comprehensive systems biology methodology combining (1) experimental assays of surgical and genetic perturbations and the formalization of their phenotypic outcomes in terms of body and organ shapes and gene expression patterns; (2) dynamic biophysical mathematical modeling of the genetic signaling mechanisms regulating cell differentiation and spatial tissue growth; and (3) novel machine learning methodologies able to infer de novo such spatial mathematical models directly from the dynamic, heterogeneous experimental data. This tight integration of machine learning, biophysical mathematical modeling, spatial data formalization, and in vivo surgical and genetic perturbation assays represents a comprehensive systems biology approach aimed at understanding the regulation of biological shape, size, and form. This work will focus on elucidating the mechanisms of the various genetic pathways that control cell differentiation and tissue growth dynamics to infer their precise interactions and the biophysical properties they regulate to form and maintain specific shapes and sizes at the whole-body level. Ultimately, this research will pave the way for applications and innovative treatments in the fields of human developmental, regenerative, and cancer medicine.

NIH Research Projects · FY 2026 · 2020-06

SUMMARY/ABSTRACT The purpose of this application is to obtain funding for five years to support research training of 27 students (sophomores through seniors) in the EDUCATE Scholars Program at UMBC. The program aims to annually recruit, train and retain undergraduate sophomores from groups underrepresented in nine STEM majors (Biology, Biochemistry, Chemistry, Bioinformatics, Chemical Engineering, Computer Engineering, Computer Science, Mathematics, and Statistics). Participants are expected to pursue post-baccalaureate training experiences, with the ultimate goal of earning a PhD or MD/PhD. Through sustained research experiences with seasoned and experienced faculty, a vast array of mentoring and educational activities, and sequential skills development courses, EDUCATE is designed to provide an engaging and transformative experience that prepares selected students to thrive in STEM and successfully navigate the often treacherous transition to the next career stage in pursuit of a career in biomedical science. Participants will be selected through a holistic application process, specifically identifying students from diverse backgrounds who have demonstrated interest for careers in biomedical research and interest in research relating to drug/substance abuse and addiction. The program will utilize a cohort model, promoting close-knit, supportive community among scholars. The PI/PD, a tenured full professor, is experienced leading successful undergraduate diversity/education/research/training initiatives, maintains an active research group, and possess a strong record training and mentoring undergraduates. A multi-method evaluation study assessing both short-term and long-term outcomes will be conducted by a full-time research professor at UMBC.