Grants

Cambridge Symphony Orchestra

CSO Chamber Players Cambridge Outreach Series

Cambridge Symphony Orchestra

CSO Chamber Players Outreach Series 2013

NSF

Artificial Intelligence (AI) applications, such as ChatGPT, which can create digital content such as images, text, and video, have been considered promising for tasks like creative writing and software development. However, these AI applications’ complexity and demand for costly computational resources limit their access for many people and organizations. This project’s novelties are the creation of software-hardware solutions that enable these AI applications to run efficiently on smaller, less powerful computer systems. The project's broader significance and importance are making AI more available worldwide. This work also supports education by involving students in groundbreaking AI research and creating public AI-related tools to benefit society. This project addresses the critical challenge of running large foundation models, like those powering advanced AI applications, on smaller, resource-limited computer systems. By combining algorithmic and system-level innovations, the project seeks to improve efficiency, scalability, and resource utilization in foundation model inference, making AI more available. This project consists of three research thrusts. First, the project tackles data transfer bottlenecks between graphics processing units (GPUs) and central processing units (CPUs) by designing a sparsity-aware scheduling system. This approach prioritizes important data (tokens) and dynamically manages memory to reduce overhead. Second, the project optimizes how AI tasks are scheduled by introducing speculative execution for real-world workloads and adaptive memory management. Finally, the proposed solutions minimize GPU scheduling delays and cross-layer communication overhead using software-based solutions to improve kernel compilation. These innovations are expected to significantly reduce inference time and cut costs and energy consumption, while broadening access to AI tools for more people and organizations. 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

Using ubiquitous Internet connectivity, it is possible to monitor and control the physical environment – from urban settings to rural communities to uninhabited wild lands – continuously, using digital technologies. Critical unsolved problems in the areas of extreme weather, agriculture, medicine, energy efficiency, education, sustainability, and other high-impact disciplines can be addressed through the creation and implementation of an always-on Internet of Things (IoT) that extends human perception and the ability to act at a distance using pervasively deployed and interconnected digital assets. This project develops a new unifying computer science and engineering approach to building and deploying IoT systems. This project’s novelties are a software environment and ecosystem that are operational ready using current digital technologies. The project’s broader significance is twofold. First, the project makes publicly available a new extensible IoT software infrastructure, enabling new cross-disciplinary discoveries and advances. Second, the project makes IoT more broadly accessible by unifying the technologies that are necessary to implement it as a new software capability. Scientifically, the project postulates a new, multi-scale set of computer science abstractions that are implementable on all devices from small-scale microcontrollers to the largest supercomputers. It does so as a full stack that can be implemented natively or as a guest of existing software infrastructure. It also includes a programming language approach that is general, highly concurrent (for efficiency) at large resource scales, and event-driven for implementation at embedded-system microscale. This combination of advances is integrable with existing development technologies and languages, thereby extending the accessibility of the research results that are required to achieve the project’s goals. 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

The ubiquity of cameras and other sensors in our environment coupled with advances in computer vision and machine learning has enabled several novel situation awareness applications combining sensing, processing, and actuation. Many of these applications are latency sensitive, network bandwidth hungry, and geo-distributed. Edge computing has emerged as a new trend in catering to these computational needs by moving computers physically near the sensors, and is driven in part by low-cost processing resources such as Raspberry Pi and NVIDIA Jetson Nano. In the 5G/6G evolution, cell phone companies are moving network-level packet processing to geo-distributed edge data centers to amortize the computational cost of the proliferation of the wirelessly connected devices. Function-as-a-Service (FaaS) (which allows users to run code without managing server infrastructure) is an appropriate allocation paradigm to increase the utilization of resource-constrained Edge sites that host such applications, but to date its design has targeted Cloud datacenters. The project’s novelty rests in addressing the gap that exists in efficient system support for FaaS for a geo-distributed Edge ecosystem. The project’s broader significance and importance rest on the assertion that Edge computing could well be the next wave of disruption due to the emerging influence of AI and ML pervading all walks of future human endeavor. Thus, the technology nuggets from this project could spur such disruption in the technology landscape. The project is an end-to-end software system architecture for latency-sensitive situation awareness applications using the FaaS paradigm on geo-distributed Edge infrastructure. Specifically, the project makes the following advances to the state-of-the-art: programming idioms and associated runtime machinery that ensure the spatial and temporal correctness of the applications deployed on the Edge infrastructure without requiring distributed systems expertise which is largely unavailable to most developers; software control plane techniques for efficient federated orchestration across the Edge infrastructure while preserving the application’s service level objectives (SLOs) and ensuring spatio-temporal application correctness; data plane techniques for the efficient management of application state at each Edge site with respect to spatial and temporal correctness; and nimble execution environments to quickly launch application components while conserving limited Edge resources. 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

This research project focuses on enhancing the performance and programmability of Field Programmable Gate Arrays(FPGAs), electronic devices whose behavior can be modified after manufacturing. Within the FPGA arena, the project further focuses on Compute-In-Memory (CIM) technology, which allows faster computing by merging memory and computer processing. The project’s novelties are the integration of CIM into specific memory blocks of FPGAs, which significantly increases computing power and energy efficiency. The project's broader significance and importance are in addressing the critical challenge of programmability in FPGAs, making them more accessible to a wider range of users, and in demonstrating the principles of open-source hardware design through prototyping CIM-enabled FPGAs. This research aims to accelerate the adoption of advanced FPGA architectures, ultimately leading to more energy-efficient and high-performance computing solutions, for processing modern workloads such as Machine Learning (ML) which require processing vast amounts of data, traditionally involving significant data movement between memory and processing units. Technically, the project employs a holistic approach that includes applications, programmability, architecture, and prototyping. It integrates CIM support into High-Level Synthesis (HLS) design frameworks, allowing for the use of high-level programming languages like C/C++ to program CIM blocks on FPGAs. Additionally, the project involves the physical design and prototyping of a CIM-enabled FPGA chip using open-source tools and technology, providing a proof-of-concept that can persuade industry vendors of its viability. Architectural enhancements in CIM blocks on FPGAs and additional applications for CIM-enabled FPGAs are also to be explored. The expected advances include a transformative impact on FPGA computing, making it a more viable alternative to accelerators like Application Specific Integrated Circuits (ASICs) for parallel workloads such as ML. The results of this research will be disseminated through educational modules, conference proceedings and journal papers, and with the release of open-source code. 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

Digital platforms like cloud computing provide significant opportunities for services such as transaction processing and machine learning but also expose users to privacy risks. Fully Homomorphic Encryption (FHE) offers a promising solution by enabling computations directly on the encrypted data without the need to decrypt it, ensuring privacy and compliance with national regulations. However, while FHE guarantees confidentiality, it does not guarantee computation integrity and verifiability, leaving users vulnerable to errors or tampering. The project plans the development of a system for verifiable FHE that ensures both integrity and verifiability with low-performance overhead, enabling safer and broader applications in sensitive domains. The project's broader significance and importance are: (1) advancing the field of verifiable and private data sharing and analysis, as emphasized in the US national strategy to advance privacy-preserving data sharing and analytics; (2) deepening the understanding of interactions between verifiable algorithms, privacy-supporting computing, and trusted hardware design; and (3) enriching computer engineering curricula while encouraging STEM participation. The project develops system-level supports for efficient, verifiable, privacy-preserving data sharing and analysis with algorithm-hardware-system co-optimized solutions. It focuses on three coherent research thrusts: (1) designing near-zero-cost verifiable algorithms for linear operations in FHE-enabled encrypted computations; (2) integrating an integrity-only Trusted Execution Environment (TEE) with the first thrust to strategically support verification for both linear and non-linear FHE operations; and (3) redesigning hardware with a clean-slate TEE architecture to address side-channel vulnerabilities and minimize encryption overhead. These innovations aim to significantly enhance the scalability and trustworthy of privacy-preserving technologies in real-world applications. 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

Modern Artificial Intelligence (AI)-based applications, such autonomous systems, traffic forecasting, social media, drug discovery, and chip design handle increasingly large and evolving graph-based data. Efficient processing of graph-based problems is challenging because they involve complex mathematical operations that incur performance overhead on hardware processing units. Researchers have recently leveraged methods that reduce this computational complexity via pruning redundant computational tasks, but many challenges related to computer memory and task parallelism persist. This project devises novel mathematical operators that address the bottlenecks of graph-based AI applications to increase their performance. The project also develops computer science curriculum and provides student training through integration of research results and education. Moreover, through semiconductor industry collaborations, the project engages in disseminating its research outcomes, ensuring practical adoption and deployment of emerging AI applications, such as semiconductor chip design and autonomous systems, thus improving the U.S. AI infrastructure, with significant benefits to the economy and society. Efficient processing of graph models is challenging since the underlying computations require graph-proportional matrix operators. The strong input graph dependence has led to performance scaling and sustainability challenges for massively parallel hardware processing units. Although the research community has been attempting to reduce the computational complexity of graph processing operations by introducing sparsity in the model inputs, the resulting graph-proportional operators face underutilization of vector-level parallelism, data locality, and indirect memory access patterns, resulting in diminished hardware parallelism. The aggressively sparsified matrix operators exacerbate the computational structures and patterns in already unstructured and ultra-sparse inputs. This project devises novel matrix operators tailored to efficiently exploit extreme sparsity on highly vectorized and high-core-count processors to unlock sustainable and scalable performance. 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

Scientific research and engineering innovations increasingly depend on complex workflows that require coordination across many types of computing resources, ranging from small edge devices to powerful supercomputers. However, the growing complexity of these workflows and the distributed nature of the underlying computing and data cyberinfrastructure (CI) make it difficult for many researchers and engineers to fully access, manage, and benefit from these advanced technologies. The goal of iWMS is to apply new advancements in Artificial Intelligence (AI) techniques to design and implement a workflow management framework that empowers researchers, engineers, and educators to take advantage of the computing continuum for scientific discovery, innovation, and education. The framework promotes progress in science and engineering, powering applications that advance national priorities. The project designs and implements iWMS, an open, modular workflow management framework that integrates AI across the entire workflow lifecycle. In particular, iWMS incorporates AI models to support automated workflow composition, intelligent resource provisioning, performance prediction, real-time anomaly detection, and workflow adaptation. Techniques such as retrieval-augmented generation facilitate workflow discovery and assembly, while machine learning-based planning and monitoring services optimize execution and enhance system reliability. With the framework foundation built on a modular and integrated design, iWMS incorporates new AI methods as they are being developed in academia and industry. iWMS services feature documented APIs, enabling CI developers to contribute to and enhance iWMS over time. The framework is evaluated with real-world scientific applications and deployed across national testbeds and cyberinfrastructure platforms. In addition to delivering robust software tools and AI models, the project produces training materials, sample workflows, and public datasets to support adoption, foster community engagement, support AI research for enhancing CI, and advance the usability and resilience of the national cyberinfrastructure ecosystem. 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

Arizona State University will investigate best practices for designing and implementing instructional coaching for computer science (CS) in the context of a large, urban school district: Clark County School District (CCSD), NV. Using a reciprocal learning approach, CSTransform seeks to advance progress in computer science education to benefit society by focusing on the overarching question: How does a large district leverage instructional coaching to develop the capacity of their high school CS teachers to teach CS in content-rich, and pedagogically engaging ways through a reciprocal learning approach? By expanding CS coaching resources and contextualizing and adapting the needs of a large school district, CSTransform will explore approaches to implement job-embedded CS coaching. The results of this study related to implementing a reciprocal lens to CS coaching and nurturing a coaching community of practice will be expanded to include professional development and implementation guidance. Moreover, this project team will co-construct a community of practice among CS coaches in CCSD while working in partnership with the Computer Science Teachers Association. Together, we will establish a national CS coaching community where instructional leaders and coaches can share their approaches, ideas, and resources with the larger community. This Small CSforAll High School Strand Research-Practice Partnership (RPP) explores the effectiveness of instructional coaching as a form of teacher professional development. Instructional coaching as a form of teacher professional development is just emerging within CS education. Research underpinning the design and implementation of CS coaching is in its nascent stage. Opportunities for CS teachers to refine their teaching and further develop their CS content, knowledge, and pedagogy beyond initial exposure is lacking. Preservice teacher programs dedicated to developing a future generation of CS teachers are also rare. This proposal will contribute to a critical domain of research to explore how a research-practitioner partnership can support and sustain job-embedded CS coaching for high school teachers and inform further scaling of such efforts. 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.

Csusb Philanthropic Foundation

Csusb Philanthropic Foundation is an active grantmaking foundation based in Sn Bernrdno, CA. Focus: philanthropy. Contact the foundation directly for current funding opportunities.

Clemente Velez Cultural & Education Center

Installation of wheelchair accessible ramps and bathrooms on the first floor and basement. Additional means of egress will be created in and to the basement, sprinklers will be installed throughout the building making the center compliant with the NYC De

Chicago Department of Transportation

TEMPORARY BUS LANES - CHICAGO (LARAMIE TO ASHLAND) AND 79TH (CICERO TO WESTERN)

NIGMS - National Institute of General Medical Sciences

Project Summary Cellular plasticity, including epithelial and mesenchymal transition (EMT), whether partial, complete or reversed, as mesenchymal-epithelial transition-(MET), is critical throughout development and implicated in wound healing, cancer metastasis, and fibrotic disorders. The reversibility or irreversibility of EMT is critical to successful implementation of these cellular programs yet is not well understood. We have shown that EMT results in diminished expression and chromatin interaction of the DNA-binding chromatin looping factor, CTCF. CTCF has been well characterized as a transcription factor and insulator protein capable of regulating formation of topologically associating domains (TADs) when bound to CTCF binding sites (CTCF-BSs). TADs define semi-stable looping structures which can incorporate multiple genes and which impact gene expression. We have shown that CTCF loss accompanies EMT and yet, paradoxically, causes a more epithelial phenotype as measured by E-cadherin and N-cadherin expression. Moreover, cells with low CTCF expression respond differently to TGF-β induced EMT by retaining E-cadherin while still elevating N-cadherin, thus eliciting a distinct partial EMT state. How CTCF and TADs effect these outcomes is unknown. To achieve our long-term goal which is to understand the epigenomic impacts on epithelial-mesenchymal plasticity and elucidate associated mechanistic underpinnings, we propose to test the hypothesis plasticity or irreversibility between epithelial-mesenchymal states, including distinct partial states, is founded upon dynamic chromatin looping patterns, driven by withdrawal and re-engagement of CTCF from CTCF-BSs at key EMT genes. In Aim 1, we will map the EMT-associated redistribution of chromatin loops and TADs, in combination with gene expression changes, during reversible EMT. We will utilize a model of reversible EMT to detect and quantify changes in CTCF binding, chromatin looping, and enhancer-promoter interactions using ChIP-seq and HiC-seq at discrete timepoints. Furthermore, we have uncovered key CTCF engagement sites at CDH1, CDH2, and ZEB1 with potential regulatory roles for EMT. In Aim 2A: we will determine the functional outcome of reduced CTCF expression to the phenotype of partial EMT. We will interrogate this hybrid EMT phenotype by phenotypic analysis including an examination of the relevance of the dual-cadherin state towards collective and individual cell migration. In Aim 2B: we will delineate the contribution of specific CTCF-BSs within these loci to chromatin looping and epithelial-mesenchymal plasticity. We will elucidate the mechanism through a focus on the epigenetic state of these loci by performing 3C-qPCR and ChIP-qPCR at the relevant loci in timecourse and knockdown models. Overall, we will uncover fundamental mechanisms used by cells to that have undergone reversible EMT at the level of chromatin looping and TAD distribution. Furthermore, this work provides opportunities for undergraduate students to perform interdisciplinary research in cellular and computational biology, while addressing fundamental questions in epigenetics and cellular plasticity.

Mountain West Investment Corporation

CTEC Chromium Cleanup Project

CTF Enterprises, Inc.

CTF Enterprises, Inc.

Oregon Business Development Department

CTF Enterprises, Inc.

NCI - National Cancer Institute

Abstract CytoAgents is developing CTO1681 for the prevention and treatment of cytokine release syndrome (CRS) associated with chimeric antigen receptor (CAR) T-cell therapy. CAR T-cell therapy has emerged as a very promising treatment option for patients with relapsed or refractory (R/R) hematologic malignancies. However, CAR T-cell therapy can also result in a high incidence of severe and potentially life-threatening immune-mediated toxicity, including CRS and immune effector cell-associated neurotoxicity syndrome (ICANS). CRS is a systemic inflammatory response that has been reported as one of the most frequent and dangerous adverse events following CD19-directed CAR T-cell therapy. CRS is thought to be mediated by an initial release of proinflammatory cytokines, which activate bystander immune cells and endothelial cells, which in turn activate more immune cells, culminating in a cytokine storm. Because CytoAgents’ novel approach is focused on reducing the transcription of multiple proinflammatory cytokines, its compound is expected to mitigate, and in some cases even prevent, CRS. CTO1681 is an orally available, stable compound identical to beraprost sodium-314d (BPS-314d), the stereoisomer of the racemate beraprost sodium (BPS) that accounts for nearly all of BPS’s pharmacological activity. Because BPS can modulate the release of cytokines from human peripheral blood mononuclear cells, CytoAgents has investigated the use of BPS and its active isomer CTO1681 as a treatment for moderate virus-induced CRS, specifically influenza and COVID-19. All of the results to date indicate that CTO1681 has strong potential to reduce the CRS response, leading to better patient outcomes regardless of CRS etiology. Overall, BPS, BPS-314d, and CTO1681 formulations have been found to be well tolerated and to not completely suppress cytokine levels in healthy volunteers with normal serum levels. CytoAgents is currently conducting a Phase 1b trial of CTO1681, a multicenter, open-label, dose-escalating safety and pharmacokinetic (PK) MAD study in patients with diffuse large B cell lymphoma (DLBCL) receiving CD19- directed CAR T-cell therapy. This Direct to Phase II project will support additional drug manufacturing and initiation of an open-label Phase 2a trial in multiple myeloma (MM) patients. The company will undertake three specific aims: 1) Determine the preliminary efficacy of CTO1681 in preventing or reducing CRS or ICANS compared to historical data; 2) Determine the expanded safety profile of CTO1681 in patients with R/R MM receiving CAR T-cell therapy; 3) Investigate the potential impact of CTO1681 on antitumor activity of CAR T-cell therapy compared to historical data. This project will help support clinical assessment of CTO1681 in CAR T-cell therapy recipients, advancing a novel treatment with the potential to reduce hospitalization, intensity of supportive care, and mortality and to improve patients’ quality of life. Moreover, this therapeutic may allow more patients with R/R hematologic malignancies to have access to potentially life-saving CAR T-cell treatment.

NCATS - National Center for Advancing Translational Sciences

PROJECT SUMMARY The overarching aim of the K12 program at the New York University (NYU) Clinical Translational Science Institute (CTSI) is to train the next generation of clinical and translational researchers to improve individual and public health through rigorous, reproducible, and solution-oriented biomedical research aligned with national research priorities. To help realize NCATS’ strategic goal to “enable all people to contribute to and benefit from translational science”, we will build on the previous three cycles of funding for our K12. The training record for previous scholars is excellent, with all 28 remaining in research or academic leadership careers and 23 out of 28 obtaining federal funding within 2 years of the program. In this proposal, the program will be led by two new, experienced program directors with a long-standing commitment to fostering the careers of early-stage investigators, Drs. Melamed and Shaukat. Both have active clinical and translational research programs, have undergone extensive mentor training, have won mentorship awards, and have led multiple institutional career development programs. They will collaborate closely to maintain the tradition of excellence of the program. The new career development program and environment will focus on fostering the seven attributes of a translational scientist and leadership development, while emphasizing rigorous study design, research transparency, and reproducibility consistent with gold-standard biomedical science. The program is composed of didactic courses, experiential learning, and individualized leadership coaching. Each scholar will have at least two science mentors, a career mentor and a mentorship advisory committee that will meet regularly to ensure adequate progress along the scholar’s independent development plan and Scholar-Mentor Compact. Our scholars will present to members of our Community Advisory Board. NYU has shown a substantial commitment to the program by increasing the number of institutionally funded positions from two to seven. We will be guided by our long-standing External Advisory Committee which meets annually and provides supervision, direction and approval of scholar appointments to the program. Our 50 faculty mentors offer a wide range of CTS expertise, from T1 to T4 funded science. Over half of the mentors have served as program directors of NIH funded training programs (T32, K12, R25, TL1, etc.) and 5 have current mid-career mentoring awards (K24 or equivalent). We will employ rigorous evaluation methods and continuous quality improvement to refine the program and equip scholars with the tools to guide their careers, accelerating the pace of CTS and preparing them to become future mentors and leaders, including training in reproducible research practices, data science approaches, and responsible use of emerging technologies such as artificial intelligence in biomedical research. By advancing their knowledge and expanding their skills, our K12 scholars will emerge as the next generation of leaders in CTS who will translate biomedical discoveries into measurable health improvements for the American public.

NCATS - National Center for Advancing Translational Sciences

The University at Buffalo (UB) CTSA Mentored Research Career Development Program will provide research training and professional development to a new cadre of translational researchers to address health challenges that impact the populations of Buffalo, Western New York and beyond. The current KL2 scholar program has been very successful in expanding the clinical and translational science workforce. The proposed K12 Program will build on these accomplishments and employ innovative organizational, pedagogic and research strategies to successfully achieve the K12 Program’s Specific Aims: (1) to change the face of translational science by recruiting a competitive cohort of early career scholars from a wide range of backgrounds and scientific and clinical disciplines; (2) to ensure career success of this highly skilled cadre of scholars by offering comprehensive, tailored and efficient didactic training, supportive evidence-base mentoring and experiential learning opportunities; and (3) to translate their research findings into efficient, high-quality healthcare. The innovations of the UB K12 Program include (i) increasing the pool of translational research scholars by selectively recruiting both trainees and mentors from highly qualified candidates in all UB health sciences schools including Jacobs School of Medicine and Biomedical Sciences, Nursing, Dental Medicine, Social Work, Pharmacy and Pharmaceutical Sciences, and Public Health and Health Professions, as well as a very engaged UB School of Engineering and Applied Sciences and the newly funded UB Institute for Artificial Intelligence and Data Science; (ii) attracting and retaining a highly competitive trainee pool by aligning CTSI K12 scholar recruitment with the UB-wide recruitment pipeline for junior faculty and postdocs supported by competitive compensation packages approved by UB leadership; (iii) using innovative tailored interactive and hybrid teaching strategies, to shorten the time scholars spend on didactic training and in turn, increase opportunities for mentored research and experiential learning; (iv) facilitating process innovation, systems thinking, dissemination and technology transfer by encouraging scholars to develop a long-term dissemination and sustainability plan for their research program including evaluation of commercial potential, understanding of regulatory requirements and financial implications; (v) pairing each trainee with a mentoring team that includes externally funded investigators from several domains of the translational science spectrum to maximize potential for multidisciplinary teamwork and boundary crossing and (vi) engaging more junior faculty on cross-disciplinary mentoring teams to increase the pool of qualified mentors with matching expertise. Together with established community and industry partners, these constitute a broadly engaged team-based environment that is ideally poised to advance biomedical and translational research and promote population health.

NCATS - National Center for Advancing Translational Sciences

The mission of the Johns Hopkins University (JHU) Institute for Clinical and Translational Research (ICTR) Postdoctoral Program (T32) is to provide an integrated set of training opportunities focused on the core principles of translational science augmented by instruction in biodesign principles and methodology. We will focus the training experience on (1) specific skills for competency in research including research applications of biodesign methodology, (2) individual mentored project development, and (3) developing skills in community engagement to advance trust in science. A core program goal is to advance cross-disciplinary training among researchers in translational science. Our history of successful recruitment and selection of highly qualified trainees from various schools, specialties, and participating institutions allows us to build on our efforts to enroll a wider range of trainees who bring multiple disciplines to address health problems. We will use existing strategies to recruit trainees in medicine, nursing, dentistry, and public health with varied scientific interests and perspectives in translational science. Our partnership with UMB will provide additional access to varied health professionals who approach problems in unique ways. We (1) Teach core principles in clinical and translational research, ensuring trainees have the knowledge and skills to succeed in their scientific pursuits and build an identity as a translational researcher; (2) Provide a supportive and collaborative culture for trainees; (3) Create a community of clinical and translational science trainees, building networks and collaborations among trainees (and mentors) to drive innovation; and (4) Encourage the development of team science skills. Trainees will recognize the importance of working collaboratively, an increasingly critical research skill. Our integrated program offers a multidisciplinary mentoring team, didactic training, competency-based interactive workshops, core ICTR resources, peer-support and resource-sharing designed to stimulate lifelong engagement in a clinical and translational research career. The program offers trainees intensive and hands-on learning experiences that prepare them to be exemplary clinical and translational scientists in any discipline, specialty or subspecialty. Our heterogeneous pool of trainees will emerge equipped with the knowledge, skills and abilities to advance diagnostics, therapeutics, clinical interventions and behavioral modifications to improve the health of all Americans as they develop into independent researchers. We will perform continuous program evaluation with multiple metrics. We evaluate the program itself (faculty, courses, seminars) and track enrollment, retention and satisfaction of trainees; and conduct course and mentoring evaluations. Like the consortium of CTSA academic medical institutions across the country, we will report on trainee performance and satisfaction as well as mentor satisfaction. We will also measure, evaluate and disseminate the impact of translational research from our program. The JHU ICTR T32 will support 5 postdoctoral trainees per year for 24 months.

NCATS - National Center for Advancing Translational Sciences

The University of Rochester (UR) Clinical and Translational Science Institute (CTSI) T32 Pre-Doctoral Training Program aims to cultivate the next generation of leaders in translational science research (TSR). It encompasses a comprehensive mentored research experience, transdisciplinary educational opportunities, career development planning, and a supportive environment conducive to growth and achievement. Our training encompasses competencies across eight fundamental domains of a translational scientist (e.g. rigorous researcher, team player, boundary crosser, and population health champion), equipping trainees with the knowledge and skills needed to improve the health of patients and populations, and supporting the overall UR CTSI goals of advancing translational science by fostering Research Without Walls. Our specific aims are: Aim 1: Contribute to a well-trained clinical and translational science workforce by recruiting, mentoring, and immersing students into focused training opportunities across the translational science spectrum. We will form a coordinated learning community through structured collaborative coursework, research experience, translational science and regulatory science competitions, innovative academic challenges, and participation in professional development opportunities. Formal T32 training addresses TSR, population health, and bioinformatics and health analytics. Aim 2: Develop a heterogeneous Trainee pathway and stimulate interest in clinical research training and career paths. Integral components are targeted recruitment efforts, including summer research programs for undergraduates, tailored mentorship programs, and integration with UR CTSI undergraduate and Master’s programs. Aim 3: Ensure a safe and supportive research training environment through evidence-based mentor training and evaluation. T32 trainees will be mentored by a tailored structure consisting of multi-disciplinary faculty teams. We prioritize formal mentor training through our Ever Better Mentoring course, and we will implement a responsive evaluation approach that includes data-driven assessments and regular review of Trainee outcomes. Aim 4: Promote exploration of translational training and careers by leveraging institutional and CTSA Consortium resources and engaging with alumni. We will intentionally engage University faculty, former faculty, alumni, current and previous CTSI TL1/T32 and KL2/K12 Trainees, student organizations, and students and researchers across the CTSA Consortium to facilitate scientific and professional networking. The UR CTSI T32 Pre-Doctoral Training Program is the foundation of a research education ecosystem that reaches beyond the UR CTSI to leverage resources and faculty expertise across the institution, facilitating Trainee transformation from student to translational scientist. It is closely integrated with the K12 program and UM1 Workforce Development Element D through shared and integrated governance structures, CTSI services and a shared mentor base.

NCATS - National Center for Advancing Translational Sciences

PROJECT SUMMARY/ABSTRACT The UMCCTS was funded in 2010 with the vision of building healthier communities together through translational innovation. Our mission is to advance learning and discovery to solve Translational Science challenges and improve well-being by: 1) catalyzing, rigorously testing, and disseminating evidence-driven approaches that remove Translational Science (TS) roadblocks to efficient, high quality, and impactful translational research (TR); and 2) building a workforce of skilled professional staff and investigators capable of changing paradigms in TS and TR. As Massachusetts’ only public university system (UMass) partnered with 3 large clinical systems (UMass Memorial Health; Baystate Health; Lahey Health), we share an enduring focus on public engagement and societal benefit. The UMCCTS engages a broad range of interest holders (communities, patient groups, foundations, industry, NCATS, and CTSA hubs) to ensure that the research we support and workforce we train address problems important to the communities we serve. With our partners, we identify important problems and needs, develop and validate enabling platforms, and provide resources that facilitate transdisciplinary team science. We use data and analytics to generate knowledge, apply that knowledge to improve performance, then use lessons learned to inform and refine the next improvement cycle. UMCCTS workforce development programs ensure the future sustainability of the TS enterprise. Our four Specific Aims correspond to NCATS strategic goals stated in the NCATS NOFO and build on our prior successes: Aim 1: Promote individual and community health by building community-centered systems and approaches that expand and sustain the engagement of participants, communities, and research teams; Aim 2: Develop a robust set of digital tools and informatics systems that engage a broad range of study participants, promote data sharing, enable actionable insights, and that extend our Learning Health System across partners and into home and community settings; Aim 3: Provide resources that overcome TS and operational barriers to continuously improve the quality, efficiency, and impact of TR across the spectrum; Aim 4: Advance the development of a skilled TS workforce through innovative educational curricula, transdisciplinary team-based training, and career development programs. By working with our partners on each of these aims we will accomplish our overarching goal of speeding the development of evidence-based, real-world approaches that promote health, treat disease, and respond to urgent public health needs locally, regionally, and nationally.

U.S. National Science Foundation

Lack of essential observations from space is currently a major limiting factor in many areas of geospace and atmospheric research. Recent advances in sensor and spacecraft technologies make it feasible to obtain key measurements from low-cost, small satellite missions. A particularly promising aspect of this development is the prospect for obtaining multi-point observations in space that are critical for addressing many outstanding problems in space and atmospheric sciences. Space-based measurements from small satellites also have great potential to advance discovery and understanding in geospace and atmospheric sciences in many other ways. To take full advantage of these developments, NSF is soliciting research proposals centered on small satellite missions.The overarching goal of the program is to support the development, construction, launch, operation, and data analysis of small satellite science missions to advance geospace and atmospheric research. Equally important, it will provide essential opportunities to train the next generation of experimental space scientists and aerospace engineers.To facilitate launch of the satellites as secondary payloads on existing missions, the focus of the program is on CubeSat-based satellites. Launch of the satellites will mainly be through the standardized CubeSat deployment system, the Poly Picosatellite Orbital Deployer (P-POD). Launch of the P-PODS will be as auxiliary payloads on DOD, NASA, or commercial launches. This will be arranged after selection and is not part of this solicitation. This solicitation covers proposals for science missions to include satellite development, construction, testing and operation as well as data distribution and scientific analysis.