Grant List
Represents Grant table in the DB
GET /v1/grants?sort=-start_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-start_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-start_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-start_date", "prev": null }, "data": [ { "type": "Grant", "id": "15724", "attributes": { "award_id": "2519776", "title": "The geography of H5N1 avian influenza in the United States: Human-environment ecosystem drivers of transmission and viral evolution", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Human-Envi & Geographical Scis" ], "program_reference_codes": [], "program_officials": [ { "id": 1931, "first_name": "Jeremy", "last_name": "Koster", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-10-01", "end_date": null, "award_amount": 496076, "principal_investigator": { "id": 32774, "first_name": "Michael", "last_name": "Emch", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32773, "first_name": "Xiu-Feng H", "last_name": "Wan", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 166, "ror": "https://ror.org/0130frc33", "name": "University of North Carolina at Chapel Hill", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project investigates how avian influenza (bird flu) spreads and undergoes genetic changes and identifies the key factors driving these genetic changes and spread. It elicits the spatial and genetic patterns of avian influenza in birds, mammals, and humans, aiming to assess the pandemic potential of this virus, which has had a 50% mortality rate in people infected during the past 30 years. Understanding the risk of spillover to humans requires a comprehensive understanding of the influenza ecosystem, an interconnected network of factors involving humans, animals, and the environment. The findings are being organized into a database for public access to support translation of what is learned from the project to practice by informing and optimizing measures to mitigate both the economic impacts on the agricultural sector, a core sector of the bioeconomy, and the public health risks posed by emerging influenza variants. This study aims to understand the genetic evolution of avian influenza, particularly a highly pathogenic H5N1 virus lineage over time and identify the ecological factors that drive human infections and viral change. Central to the study is a systematic analysis and characterization of the spatiotemporal distributions of viral genotypes and their genetic divergence from precursor avian influenza viruses. It leverages advanced geospatial modeling, machine learning, and geospatial artificial intelligence (GeoAI) techniques to identify key viral traits, such as transmission potential and virulence, and to elucidate geographic ecosystem factors that influence the spread and evolution of the virus. The study generates a publicly available database that integrates information on more than 20,000 avian influenza viruses with associated human-animal-environment ecosystem variables. This database subserves translational support for research and private-sector preparedness. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15708", "attributes": { "award_id": "2536495", "title": "2025 USUCGER Early Career Workshop for Geotechnical Faculty; Atlanta, Georgia; October 2025", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "ECI-Engineering for Civil Infr" ], "program_reference_codes": [], "program_officials": [ { "id": 9152, "first_name": "Giovanna", "last_name": "Biscontin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-10-01", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32555, "first_name": "Susan", "last_name": "Burns", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32552, "first_name": "David", "last_name": "Frost", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32553, "first_name": "JC", "last_name": "Santamarina", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32554, "first_name": "Sheng C", "last_name": "Dai", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is to support participants to attend the third United States Universities Council on Geotechnical Education and Research (USUCGER) Early Career Workshop that will be held in October 2025 in collaboration with the Geosystems Engineering faculty at the Georgia Institute of Technology in Atlanta, Georgia. The workshop will be a 1.5-day event focused on issues that are impactful for the initiation of an academic career in today’s rapidly changing research and teaching environment with an academic context significantly impacted by the COVID-19 pandemic and the advent and prevalence of artificial intelligence across university campuses. This workshop will bring together the newest generation of geotechnical engineering faculty to: 1) Facilitate research collaborations through formal presentations and informal discussions; 2) Identify best practices in research and teaching, with special attention to artificial intelligence in the laboratory and in the classroom; 3) Facilitate entrepreneurial thinking through sessions on research commercialization and technology transfer; 4) Facilitate mentoring, work-life balance, and human connection in a post COVID academic world; The format of the workshop is designed to broaden perspective and widen access to new ways of thinking for early-career faculty as they embark on an academic career path. The workshop will focus on practical best methods that researchers and teachers can take back to their home institutions to help build the most solid foundation for a productive and innovative career. Outcomes of the workshop will include a post-workshop report hosted on a dedicated website managed by USUCGER, as well as the creation of a monthly “Office Hour with the Program Director” which will allow junior faculty to sign up for small group mentoring sessions with the CMMI program director. It is anticipated there will be 10 one-hour sessions of 6-8 junior faculty who will be able to ask questions about a range of topics pertaining to researching with NSF and life as a junior faculty member. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15723", "attributes": { "award_id": "2452299", "title": "Integrating Soft Skills with Technical Skills to Produce Next-Generation Cybersecurity Technicians", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [ { "id": 3736, "first_name": "R. Corby", "last_name": "Hovis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-10-01", "end_date": null, "award_amount": 565044, "principal_investigator": { "id": 32772, "first_name": "Alan", "last_name": "Gruver", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32770, "first_name": "Kristopher R", "last_name": "Bradshaw", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32771, "first_name": "David E", "last_name": "Oliver", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2607, "ror": "", "name": "Johnston Community College", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by producing more qualified technicians to meet workforce demands in cybersecurity. Keeping computers and information systems secure is a critical need and a major challenge in business, industry, and government. The growth of cyber-threats has created a need for many more workers who have the knowledge and skills to protect both existing and emerging technologies. Research and feedback from employers indicate that although graduates of cybersecurity programs are generally well-prepared technically, their soft skills remain underdeveloped. (This issue was exacerbated by primarily virtual interactions and remote work during the COVID-19 pandemic.) To address this gap, the investigators intend to integrate the targeted development of soft skills into core cybersecurity courses through structured team-based activities, simulations, and competition-style challenges. The project will focus on five competencies that cybersecurity professionals need in the workplace: communication, critical thinking, problem-solving, continuous learning, and attention to detail. The Business & Industry Leadership Team (BILT) that advises the college's cybersecurity program prioritized these soft skills for attention. The project team will directly embed them into course activities, assignments, and assessments. Specifically, the investigators aim to revise four existing cybersecurity courses -- Introduction to Cyber Crime, Introduction to Protocol Analysis, Security Administration, and Ethical Hacking with Python I -- to include mini-modules and challenge-based team assignments focusing on soft skills. Each course will focus on one or two of the five targeted soft skills, ensuring that each one is addressed in-depth within a technical context. Examples include group-based incident response briefings to strengthen communication and professionalism; packet analysis and network troubleshooting activities completed in teams to promote teamwork and problem-solving; and adaptive policy response scenarios that encourage flexibility and resilience. Each course will include clear learning outcomes, soft skill rubrics, and feedback mechanisms to assess both technical and interpersonal development. In addition, the investigators aim to establish a student cybersecurity competition team as a co-curricular activity and to bring elements of cyber-competition into the classroom for all students. Those activities will include in-class simulations modeled on capture-the-flag or red team/blue team competitions; structured team challenges with rotating roles to develop communication and adaptability; and opportunities for reflection and instructor feedback following simulations or live drills. By embedding a focused set of soft skills into core technical coursework and grounding students' experience in competition-style, gamified team activities, the redesigned cybersecurity program will provide a coherent, high-impact approach to cybersecurity education that aligns with workforce needs and promotes student success. This project is funded by the Advanced Technological Education program, which focuses on the education of technicians for the advanced-technology fields that drive the nation's economy. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15722", "attributes": { "award_id": "2449985", "title": "Strengthening Rural STEM Education: An Evidence-Based Framework for Increasing Student Success", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [ { "id": 1859, "first_name": "Mike", "last_name": "Ferrara", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-10-01", "end_date": null, "award_amount": 2100000, "principal_investigator": { "id": 32769, "first_name": "Jessica", "last_name": "Black", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32766, "first_name": "Melissa", "last_name": "Haeffner", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32767, "first_name": "Alexander", "last_name": "Alexiades", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32768, "first_name": "SaraBecca", "last_name": "Martin", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2606, "ror": "", "name": "Heritage University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Institutional Transformation project aims to develop and implement a comprehensive model for transforming STEM education at a rural institution serving local communities. The project will address academic achievement gaps in STEM fields by fostering strong STEM identities at both institutional and student levels. This work is especially timely as rural, community-based institutions face unique challenges in retaining STEM students, particularly following the COVID-19 pandemic's disproportionate impact on college-level preparedness. The project will transform institutional culture through structured dialogue between leadership and STEM stakeholders, professional development for administration, and increased leadership engagement in student-centered STEM events. Through this dual approach of institutional transformation and evidence-based teaching practices, the project will contribute new understanding of how community-based institutions can effectively support all students while preparing them for the global STEM workforce. Through innovative programming and teaching practices, this work will create a model for other institutions across the United States to follow. The project will pursue two complementary objectives: i) enhancing STEM identity in institutional leadership to transform student support networks, and ii) developing innovative experiential learning opportunities across STEM disciplines. Two primary research questions will guide this work: how backgrounds and experiences found in community-based institutions influence STEM identity development, and what knowledge transfer approaches effectively help students develop STEM identities in varied educational contexts. The project will employ multiple research methods including Photovoice documentation during international experiences, reflective student journaling, traditional storytelling approaches during research fellowships, and semi-structured interviews with institutional leaders. Specific interventions include Undergraduate Research Fellowships fostering mentored research experiences and the global interconnectedness of STEM, and comprehensive academic support systems with early intervention strategies. The project will advance the field by generating new knowledge about institutional transformation in rural higher education settings, particularly regarding the relationship between institutional STEM identity and student success. Expected broader impacts include developing a model for mutual exchange between institutional leadership and students that can inform STEM policy development at other institutions, creating a guide for building community-based programs that benefit both students and their surrounding communities in both regional and international settings, and increasing the number of STEM graduates prepared for the workforce. Results will be disseminated through publications in undergraduate education journals, conference presentations, and through the NSF HSI Program Network Resource Centers and Hubs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15721", "attributes": { "award_id": "2528179", "title": "STTR Phase I: Room Temperature Stable, Dry Powder Particle-Based Vaccines Against Influenza", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "STTR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 936, "first_name": "Henry", "last_name": "Ahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-10-01", "end_date": null, "award_amount": 305000, "principal_investigator": { "id": 32602, "first_name": "Sean", "last_name": "Kelly", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32601, "first_name": "Kathleen", "last_name": "Ross", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2605, "ror": "", "name": "IMMUNO NANO MED, INC", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to demonstrate room-temperature-stable, dry powder inhalable influenza vaccines represent as an innovative, next generation technology to promote the health and welfare of the American public by eliminating the existing pain points of current influenza vaccines. The global influenza vaccine market size is projected to increase to $17.77 billion by 2032. Therefore, the demand for innovative vaccines against seasonal respiratory viruses remains a high priority. These dry powder vaccines introduce a transformative innovation – induction of durable protective immunity that targets both the upper and lower airways via nasal delivery and removing the cold chain due to room-temperature shelf stability, thereby lowering vaccine costs and wastage. This outcome can result in cost savings of up to 80%. The economic and social benefits of this vaccine technology will lead to achieving and maintaining a significant market share of the flu vaccine market. Additionally, this technology’s plug-and-play capability allows swapping pathogen-specific proteins and creating new inhalable room-temperature-stable vaccines for other respiratory pathogens. Altogether, this advance will significantly lower storage costs while improving our nation’s strategic preparedness in stockpiling vaccines against circulating disease, emerging threats, or biowarfare agents. This Small Business Technology Transfer (STTR) Phase I project will demonstrate the feasibility of producing a novel room-temperature-stable, dry-powder inhalable influenza vaccine and using a new scalable process to manufacture the vaccine. Current flu shots do not provide lung-specific immune responses and require refrigerated storage. This project’s value proposition is to replace current needle-in-the-arm, partially effective flu shots with next-generation vaccines and delivery methods. This project enables the risk-reducing R&D needed to advance a dry powder vaccine manufacturing technology called Payload Reduction and Encapsulation Technology (PRET). The goal is to demonstrate feasibility of this manufacturing method by showing dry powder influenza vaccines synthesized by PRET result in reproducible dry powder vaccine characteristics, high vaccine yields, protection against influenza infection, and room-temperature shelf stability. There are three objectives that will be pursued to demonstrate this: 1) feasibility of achieving initial pilot-scale production and characterization of dry powder influenza vaccines using PRET; 2) dry powder influenza vaccine efficacy compared to traditional flu vaccines; and 3) production of influenza particle-based vaccines using scaled-up engineering runs and evaluation of room-temperature shelf-life. The new paradigm represented by room-temperature-stable, dry powder vaccines has the potential to transform the vaccine-delivery landscape and enhance the nation’s pandemic preparedness. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15726", "attributes": { "award_id": "2527134", "title": "Collaborative Research: eMB: The immunological signature of a changing world: mathematical models to infer historical patterns of infectious disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [ { "id": 32775, "first_name": "Vu", "last_name": "Dinh", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-15", "end_date": null, "award_amount": 149949, "principal_investigator": { "id": 32776, "first_name": "Erin", "last_name": "Clancey", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 306, "ror": "https://ror.org/05dk0ce17", "name": "Washington State University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Spillover of infectious diseases from wildlife to humans and livestock is a pervasive risk to the health and welfare of human populations around the world. Effective management of this risk is facilitated by early detection of changes in the frequency of spillover events. This research will develop new mathematical models and statistical methods that allow changes in the rate of spillover to be detected from the fossil record of past infection that remains imprinted on human and animal immune systems. The general methodology developed by this project will be rigorously tested using simulated data and applied to Rift Valley fever virus, a pathogen that poses a high risk of global expansion with potentially devastating consequences for human health and agriculture. Work on this project will train students in cutting edge mathematical and statistical methods and support an international workshop where software developed by the project will be introduced and instruction on its use provided. Predicting how zoonotic infectious diseases change over time is a fundamentally important challenge with few general mathematical solutions. Central to addressing this problem is disentangling historical changes in the rate or “force” of spillover from background biological processes, such as age-specific infection and wanning immunity, which can cloak or mimic the signal of temporal change. Existing statistical methods to infer historical changes in the force of spillover for zoonotic pathogens rely on piecemeal solutions tailored to specific scenarios, ignore interacting background processes, use only single immunological markers, and have failed to rigorously evaluate parameter identifiability. To fill this gap, this project will develop a general mathematical framework describing the probability that an individual is in a specific multivariate immune state as a function of age and time using a coupled system of partial differential equations (PDEs). Approximate and numerical solutions to this system of PDEs will enable a Bayesian statistical framework for inferring recent historical changes in the force of spillover in the presence of alternative biological processes. Testing this statistical framework using extensive, biologically realistic simulated datasets will allow the identifiability of historical change in force of spillover to be evaluated. Application of this methodology to Rift Valley fever virus, a pathogen with significant pandemic potential, will determine whether increasing case counts in East Africa result from fundamental shifts in disease epidemiology or from increased disease surveillance. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15725", "attributes": { "award_id": "2527135", "title": "Collaborative Research: eMB: The immunological signature of a changing world: mathematical models to infer historical patterns of infectious disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [ { "id": 32775, "first_name": "Vu", "last_name": "Dinh", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-15", "end_date": null, "award_amount": 224888, "principal_investigator": { "id": 27856, "first_name": "Scott", "last_name": "Nuismer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4470, "first_name": "Christopher H", "last_name": "Remien", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 627, "ror": "", "name": "Regents of the University of Idaho", "address": "", "city": "", "state": "ID", "zip": "", "country": "United States", "approved": true }, "abstract": "Spillover of infectious diseases from wildlife to humans and livestock is a pervasive risk to the health and welfare of human populations around the world. Effective management of this risk is facilitated by early detection of changes in the frequency of spillover events. This research will develop new mathematical models and statistical methods that allow changes in the rate of spillover to be detected from the fossil record of past infection that remains imprinted on human and animal immune systems. The general methodology developed by this project will be rigorously tested using simulated data and applied to Rift Valley fever virus, a pathogen that poses a high risk of global expansion with potentially devastating consequences for human health and agriculture. Work on this project will train students in cutting edge mathematical and statistical methods and support an international workshop where software developed by the project will be introduced and instruction on its use provided. Predicting how zoonotic infectious diseases change over time is a fundamentally important challenge with few general mathematical solutions. Central to addressing this problem is disentangling historical changes in the rate or “force” of spillover from background biological processes, such as age-specific infection and wanning immunity, which can cloak or mimic the signal of temporal change. Existing statistical methods to infer historical changes in the force of spillover for zoonotic pathogens rely on piecemeal solutions tailored to specific scenarios, ignore interacting background processes, use only single immunological markers, and have failed to rigorously evaluate parameter identifiability. To fill this gap, this project will develop a general mathematical framework describing the probability that an individual is in a specific multivariate immune state as a function of age and time using a coupled system of partial differential equations (PDEs). Approximate and numerical solutions to this system of PDEs will enable a Bayesian statistical framework for inferring recent historical changes in the force of spillover in the presence of alternative biological processes. Testing this statistical framework using extensive, biologically realistic simulated datasets will allow the identifiability of historical change in force of spillover to be evaluated. Application of this methodology to Rift Valley fever virus, a pathogen with significant pandemic potential, will determine whether increasing case counts in East Africa result from fundamental shifts in disease epidemiology or from increased disease surveillance. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15643", "attributes": { "award_id": "2442970", "title": "CAREER: Mechanism-Informed AI for Biological Systems-of-Systems to Accelerate Biomanufacturing Systems Integration and Innovations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "MSI-Manufacturing Systms Integ" ], "program_reference_codes": [], "program_officials": [ { "id": 31283, "first_name": "Janis", "last_name": "Terpenny", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-01", "end_date": null, "award_amount": 596920, "principal_investigator": { "id": 32147, "first_name": "Wei", "last_name": "Xie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Unlike traditional pharmaceuticals, biopharmaceuticals use living organisms, e.g., cells, as factories to provide essential life-saving treatments for severe and chronic diseases (including cancers, metabolic diseases, and infectious diseases such as COVID-19) often with advantages such as increased efficacy and reduced side effects. However, current manufacturing systems lack the flexibility to produce existing and new biopharmaceuticals on demand. This is mainly because biomanufacturing processes are highly complex and variable, with hundreds of biological, physical, and chemical factors dynamically interacting at molecular, cellular, and macroscopic scales. Further, bioprocessing mechanisms are not systematically understood, and data are often very limited, sparse, and heterogeneous. To address these challenges, this Faculty Early Career Development (CAREER) project aims to optimize biomanufacturing processes via a bioprocess-specific AI that integrates uncertainty, intelligence, and science (i.e., systems and synthetic biology). Leveraging emerging sensing technologies that can monitor bioprocesses at molecular and cellular scales, this AI can also efficiently decode fundamental mechanisms. Moreover, by transferring this AI to industry practice, it is hoped this research will help make life-saving biopharmaceuticals rapidly available by accelerating biomanufacturing systems integration and automation with dramatically improved capabilities. The project will in parallel create a world-leading workforce pipeline from training the current workforce to educating (under)graduate and K-12 students. This project will create a mechanism-informed AI platform on Biological Systems-of-Systems to enable the quick assembly of flexible and robust biomanufacturing systems. To support biomanufacturing systems integration and accelerate the development of flexible optimal robust manufacturing systems, this research will answer two fundamental questions: (1) how to create a unified knowledge representation that enables integration of heterogeneous data collected at molecular, cellular, and macroscopic scales in different production processes; and (2) how to enable sample-efficient and interpretable learning for fundamental mechanisms and optimal control strategies within and across different scales. These questions will be addressed through three integrated research efforts: (i) creating a multi-scale probabilistic knowledge graph (pKG) hybrid (mechanistic + statistical) model with a modular design capable of representing spatial-temporal causal interdependencies from molecular- to cellular- to macroscopic scales for different biomanufacturing processes; (ii) developing interpretable federated learning to quickly fuse sparse and heterogeneous data collected from different production processes to advance scientific understanding and track critical latent states through sequential Bayesian inference on the pKG; and (iii) constructing new provably efficient model-based reinforcement learning schemes on Bayesian pKG, accounting for model uncertainty, informing design of experiments for digital twin calibration, and streamlining the policy search on optimal robust biomanufacturing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15727", "attributes": { "award_id": "2537759", "title": "Collaborative Research: SaTC: EDU: A Socially-Distant Cloud-Based Hardware Security Educational Platform", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 32777, "first_name": "Gursel", "last_name": "Serpen", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-01", "end_date": null, "award_amount": 186000, "principal_investigator": { "id": 31736, "first_name": "Kanad", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The main goal of this project is to develop and deliver remote experiments utilizing cloud-based resources aimed at educating a broad audience of students and practitioners in hardware security. In the post-COVID era, it is imperative to develop online education platforms for remote training of both students and the workforce in the field of Hardware Security. Recent advances in this field and FPGA-based cloud servers have enabled an opportunity to move related experiments to an online format that only requires a standard computer and internet connection by the students. Teaching “hardware” security in a socially distanced format poses significant challenges. Essential experiments for teaching key concepts in hardware security necessitate multiple evaluation boards and physical equipment such as voltage supplies, oscilloscopes, multimeters, and function generators. To adapt these experiments for an online platform, the project will explore innovative methods to execute or emulate them using the cloud ecosystem. This project addresses a critical gap by developing a fully online hardware security training module accessible to students and professionals worldwide. This project proposes various comprehensive experiments testing different notions in hardware security. The framework will be designed for both undergraduate and graduate students in the electrical engineering, computer engineering, and computer science departments, leveraging courses developed by the PIs in their respective institutions. The proposed infrastructure includes preparing detailed experiments for instructors with walkthrough documents and organizing student assignments for independent completion. This setup supports not only teaching but also facilitates independent research upon assignment completion. Supplemented with video instructions, these experiments will constitute a comprehensive training module, equipping participants with the necessary skills and knowledge to address complex challenges in this emerging domain, thereby instilling preparedness and confidence. This award is co-funded by the NSF Improving Undergraduate STEM Education (IUSE: EDU) Program. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is further supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15753", "attributes": { "award_id": "1R21ES038077-01", "title": "Project Firestorm: Assessing Respiratory and Mental Health Impacts of Wildland-Urban Interface Fires and Long-Term Toxic Exposures", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Environmental Health Sciences (NIEHS)" ], "program_reference_codes": [], "program_officials": [ { "id": 32812, "first_name": "ASHLINN KO", "last_name": "QUINN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-25", "end_date": "2027-08-24", "award_amount": 456500, "principal_investigator": { "id": 32813, "first_name": "FRANK D.", "last_name": "GILLILAND", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32814, "first_name": "Daniel", "last_name": "Soto", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32815, "first_name": "Jennifer Beth", "last_name": "Unger", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2622, "ror": "", "name": "UNIVERSITY OF SOUTHERN CALIFORNIA", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "/ ABSTRACT The Los Angeles firestorms in January 2025 burned over 50,000 acres, destroyed over 16,000 homes and other structures, and displaced over 150,000 Los Angeles County residents. Most importantly, the fires have significantly impacted air quality across the Los Angeles Basin. The fires released high levels of fine particulate matter, VOCs, CO, NOx, and ozone precursors, exacerbating respiratory and cardiovascular conditions for those throughout Los Angeles. These findings stress the need to study long-term health impacts of wildland- urban interface (WUI) wildfire smoke. The acute and longer-term health effects of exposures from these catastrophic wildfires have yet to be defined. A better understanding of WUI) fire-related exposures and the health impacts is an urgent public health priority for Los Angeles. We will collect biological samples from affected individuals and analyze home dust, surface contaminants and outdoor soil and ash over time. After a wildfire it is critical to assess exposure levels and characterize the composition of toxins before home clean-up and environmental factors, such as wind and rain, alter its distribution or concentrations. We propose to conduct Project Firestorm, a rapid study to quantify the health effects of the wildfires. We will leverage an existing cohort of over 9,000 USC faculty, staff, and students who participated in a longitudinal COVID-19 study in 2021-2022. These participants, most of whom live in or around Los Angeles, have completed surveys about their physical and mental health and sociodemographics, providing an essential baseline assessment. The participants have signed consent forms giving their permission to be recontacted for future studies, enabling us to launch the study quickly without extensive recruitment time. We will recontact these participants and invite them to complete a survey about the effects of the fires on physical, mental, and financial health over the next year. From those who complete the survey (N=approximately 3000), we will recruit and collect more detailed data from a sample of 200 participants--100 who lived near the fires (fire-adjacent) and 100 who live over 15 miles away from the burn site (fire-distant). These participants will provide health outcome data on respiratory and other key outcomes, hair samples, wear silicone bracelets for VOC measurements, and samples of their house dust, surface wipes and yard soil for analysis, in February-March 2025 and again in February-March 2026. We will analyze (1) differences between fire-adjacent and fire-distant participants at baseline, and (2) change over a one-year period among fire-affected households and more distant households. Findings will guide public health interventions, long-term remediation efforts, and strategies to mitigate the WUI fires’ health impacts.", "keywords": [ "Acute", "Adult", "Affect", "Air", "Anxiety", "Area", "Aromatic Polycyclic Hydrocarbons", "Arsenic", "Authorization documentation", "Biological", "Burn injury", "COVID-19", "Cadmium", "Carbon Black", "Carbon Monoxide", "Cardiovascular system", "Characteristics", "Chemical Burns", "Communities", "Consent", "Consent Forms", "Consumption", "County", "Data", "Dioxins", "Disasters", "Distant", "District of Columbia", "Dose", "Environmental Risk Factor", "Environmental Wind", "Evaluation", "Event", "Exposure to", "Faculty", "Financial Hardship", "Fire - disasters", "Flame Retardants", "Funding", "Future", "Hair", "Health", "High Prevalence", "Home", "House Dust", "Household", "Individual", "Intervention", "Investigation", "Lead", "Lead levels", "Longitudinal Studies", "Los Angeles", "Measurement", "Measures", "Mental Depression", "Mental Health", "Mercury", "Metals", "National Institute of Environmental Health Sciences", "Nitrogen Oxides", "Outcome", "Ozone", "Participant", "Particulate Matter", "Pattern", "PhenX Toolkit", "Physical assessment", "Policies", "Polychlorinated Biphenyls", "Positioning Attribute", "Post-Traumatic Stress Disorders", "Protocols documentation", "Psyche structure", "Public Health", "Rain", "Readiness", "Recontacts", "Reporting", "Research", "Respiratory Signs and Symptoms", "Risk", "Sampling", "Silicones", "Site", "Smoke", "Soil", "Standardization", "Stress", "Structure", "Students", "Surface", "Surveys", "Testing", "Time", "Toxicant exposure", "Toxin", "Wildfire", "Wrist", "air monitoring", "coarse particles", "cohort", "design", "exposed human population", "fine particles", "health data", "improved", "persistent organic pollutants", "phthalates", "physical conditioning", "physical symptom", "pollutant", "psychological symptom", "public health intervention", "public health priorities", "recruit", "remediation", "respiratory", "respiratory health", "sociodemographics", "tool", "toxic metal", "volatile organic compound", "wristband" ], "approved": true } } ], "meta": { "pagination": { "page": 1, "pages": 1405, "count": 14046 } } }