Convergence Accelerator Portfolio

The Convergence Accelerator program is composed of three cohorts, started in 2019, 2020 and 2021. Each cohort focuses on two research track topics. The 2019 and 2020 cohorts are currently in phase 2. The 2021 cohort is currently in phase 1. 


2019 cohort

Launched in September 2019, 43 phase 1 teams were awarded a total of $39 million to support projects focused on two research track topics: Open Knowledge Networks, Track A, and the Future of Work, Track B. During phase 1, teams worked to build a proof of concept for their solution, developed strong partnerships, and participated in the program’s innovation curriculum. At the end of phase 1, the teams participated in a formal pitch presentation and phase 2 proposal.

In September 2020, the Convergence Accelerator awarded nine teams phase 2 awards, investing more than $28 million to address national-scale societal challenges and to generate knowledge to transition ideas from research into practice. Phase 2 teams are continuing to apply program fundamentals to develop solution prototypes and to build a sustainability model to ensure impact beyond NSF support. During phase 2, teams will engage in a phase 2 innovation curriculum focused on entrepreneurial concepts. By the end of phase 2, teams are expected to provide deliverables that impact societal needs at scale.

Track A: Open Knowledge Networks project image

Vast amounts of data are produced every day, yet many organizations lack the accessibility to use these data to draw insights and make decisions. Knowledge networks, or repositories, that host the world's knowledge will help power the next wave of artificial intelligence exploration, driving innovations from scientific research to the commercial sector.

Knowledge networks and graphs provide a powerful approach for data discovery, integration and reuse. But they also require an investment in their creation and maintenance. Today, only the biggest technology companies have the resources to develop and exploit significant knowledge graphs and networks.

To enable data to be freely accessible, especially to government, academia, small business and nonprofit organizations, the Convergence Accelerator is funding the creation of nonproprietary infrastructure to build open knowledge networks, OKNs.

Using artificial intelligence and machine learning, Convergence Accelerator teams build infrastructure, tools and applications to identify and link data points, describe relationships and gather information at speed and scale — enabling data-driven insights.

Open knowledge networks connect people, events, places, environments, health and more, removing boundaries between these domains. They link data, its attributes, and relationships to other data — making that information accessible to decision-makers, analysts, researchers, and the public so they can answer questions they care about.

The Convergence Accelerator teams currently focus on urban flooding, judicial court records, biomedical health, geospatial information, and technology infrastructure for knowledge network creation and use.

The Open Knowledge Networks phase 2 projects include:

AI and machine learning infrastructure tools and applications

  • OKN Infrastructure Led by the University of Michigan, the team is building infrastructure for constructing novel OKNs and OKN-powered applications. This solution provides tools to make the creation and maintenance of high-quality datasets and apps more cost-effective and more widely accessible.
  • KnowWhereGraph Led by the University of California, Santa Barbara, KnowWhereGraph provides knowledge graph and geo-enrichment services for environmental intelligence applications. The solution enriches data with pre-integrated, custom-tailored knowledge about any locale of interest, reducing the time to find, combine and reuse data. The initial application areas focus on decision support related to food systems, supply chains and humanitarian aid but can easily be expanded to other application areas.

Domain-based open knowledge networks

  • Biomedical Open Knowledge Network Led by the University of California, San Francisco, the network connects millions of pieces of biomedical information, including molecules, pharmacological compounds, organs and diseases, food nutrients and more. Centered around knowledge representation and reasoning, the team develops applications using graph theory, advanced visualizations and real-world clinical evidence to advance drug development and precision medicine.
  • SCALES Led by Northwestern University, the SCALES open knowledge network is designed to be a public resource to help provide insights based on judicial court records. SCALES is creating tools to decode court records and transform the data into actionable information that aids various users, including legal scholars, journalists, policymakers, judiciary and citizens.
  • Urban Flooding Open Knowledge NetworkLed by the University of Cincinnati, the network addresses urban flooding impacts to assist decision-makers and urban planners in real-time response and long-term planning.

Integrating the knowledge networks

  • Data2Knowledge Consortium Knowledge graphs are rapidly emerging as key infrastructure to integrate the diverse information needed to solve complex societal challenges, from climate change and human health to capturing business value from the AI revolution. The Open Knowledge Network phase 2 teams are collaborating on track integration to create the Data2Knowledge Consortium and ensure that the outcome from the Convergence Accelerator Track is "greater than the sum of its parts." Composed initially of the current Open Knowledge Network phase 2 teams, the objective of the Data2Knowledge Consortium is to facilitate a thriving ecosystem for open knowledge graph development and use.

Open Knowledge Networks Track Manager: Lara Campbell, program director

Track B: The Future of Work project image

The world’s technological advancements in AI, machine learning and robotics are irrevocably changing the future of work in unanticipated ways. The Convergence Accelerator is focusing on solutions to train, reskill, upskill and prepare the current and future workforce for the industry needs and jobs of the future. The program also focuses on building a national talent ecosystem to grow the U.S. workforce and ensure its continued global competitiveness.

Teams composed of academia, industry, nonprofits and end-user partners are converging to develop disruptive future-of-work solutions. These solutions envision a positive nationwide impact, where technology is used to create a STEM talent pipeline that is fitted to industry needs, keeps workers safe and helps them perform their jobs better, creates new jobs, and facilitates accessibility and inclusivity.

Solutions include developing the U.S. talent pipeline through competency-based training; intelligent tools that connect academic institutions with industry needs and prepare students for the workforce; improving workforce training and safety for emergency responders through human augmentation; and creating virtual reality and augmented reality tools to identify unique skills of neurodiverse individuals, preparing them to thrive in the workforce.

The Future of Work phase 2 projects include:

  • LEARNER Led by Texas A&M University, LEARNER is an agile and adaptive emergency response training platform integrated with human augmentation technology. LEARNER is accelerating the adoption of human augmentation technology for safer and more efficient emergency response work; supports adaptive learning that is sensitive to emergency response workers’ socio-technical opportunities and budgetary constraints; builds and retrains skilled emergency response personnel; and accelerates next-generation workforce development.
  • NeuroAI@Work Led by Vanderbilt University, NeuroAI@Work is a suite of AI-driven tools to support autistic individuals to successfully enter and contribute to the American workforce. The solution is a safe and effective tool for skill assessment, upskilling, independence, on-the-job support and professional development. The team is composed of engineers, psychologists and business experts.
  • SkillSync Industry 4.0 is changing the skills that workers need and companies require, leaving businesses vulnerable and colleges behind. The team, led by Eduworks Corporation, uses AI and national skills data to help companies identify required skills, connect them with college continuing education departments, and enable colleges to respond with efficient, effective and equitable reskilling programs.

Integrating the future of work ecosystem

  • Skills-Based Talent Ecosystem Platform for Upskilling, STEP UP: The Future of Work phase 2 teams are collaborating on track integration to create STEP UP and ensure that the outcome from this Convergence Accelerator track is "greater than the sum of its parts." STEP UP focuses on connecting the skills and talents of individual workers to the opportunities that most need them. By inclusively engaging the nation's skill and talent base and the technologies that support, augment and develop that talent, the group is ensuring every American can partake in the benefits of a thriving economy and the dignity of meaningful work.

The Future of Work Track Manager: Linda K. Molnar, program director

2019 Cohort Project Videos

Track A: Open Knowledge Networks phase 2 videos

Biomedical Open Knowledge Network

KnowWhereGraph

OKN Infrastructure

SCALES

Urban Flooding Open Knowledge Network

Data2Knowledge Consortium

Track B: The Future of Work phase 2 videos

LEARNER 

NeuroAI@Work

SkillSync

Skills-Based Talent Ecosystem Platform for Upskilling, STEP UP 


2020 cohort

2020 cohort

In September 2020, the Convergence Accelerator launched the 2020 cohort, awarding 29 teams phase 1 awards totaling $27 million. The 2020 cohort addresses two transformative research areas of national importance: Quantum Technology, Track C, and AI-Driven Innovation via Data Sharing and Model Sharing, Track D. During phase 1, teams worked to build a proof of concept for their solution, developed strong partnerships, participated in the program’s innovation curriculum, and completed the formal pitch and phase 2 proposal evaluation.

In September 2021, the Convergence Accelerator awarded 10 teams phase 2 awards totaling $50 million. Phase 2 teams are continuing to apply program fundamentals to develop solution prototypes and to build a sustainability model to ensure impact beyond NSF support. During phase 2, teams will engage in a phase 2 innovation curriculum focused on entrepreneurial concepts. By the end of phase 2, teams are expected to provide deliverables that impact societal needs at scale.

Track C: Quantum Technology

Improving the U.S. industrial base, maintaining an edge in emerging technology areas, creating jobs, and making significant progress to address economic and societal needs are all vital challenges to the nation. Teams within the NSF Convergence Accelerator's Quantum Technology track are developing quantum technologies — sensors, devices, hardware, interconnects, networks and simulations — to deploy in applications such as autonomous vehicles and health care. They are also creating innovative curricula by leveraging strong industry-university partnerships that are diverse and inclusive.

The Quantum Technology phase 2 projects include:

Quantum sensors

  • Quantum Sensors Led by the University of Arizona, the team is developing an entanglement-enhanced sensing architecture to benefit many domains, including secure inertial navigation, space and planetary terrestrial control, and health care monitoring.
  • PEAQUE Led by the University of Washington, the team is addressing quantum computing scalability by innovating a chip-scale, multi-beam optical control system that empowers cold-atom quantum computing with thousands of qubits.

Quantum networks and simulations

  • QuaNeCQT Led by the University of Maryland, the team is developing hardware to transform the internet into a quantum internet, which will be essential for connecting the anticipated rapid expansion of quantum computers.

Workforce and education

  • QuSTEAM Led by the Ohio State University, QuSTEAM is a transformational undergraduate curriculum aimed at addressing critical workforce needs in quantum information science and engineering.

Quantum Technology Track Manager: Pradeep Fulay, program director

Track D: AI-Driven Innovation via Data and Model Sharing project image

AI research and development require access to high-quality datasets and environments and resources for testing and training. NSF’s Convergence Accelerator is funding the development of tools and platforms to address data and model-sharing challenges, including privacy protection and easy and efficient data matching and sharing.

The AI-Driven Innovation via Data and Model Sharing phase 2 projects include:

Civil/build infrastructure

  • AI-Grid Led by Stony Brook University, AI-Grid is an AI-enabled solution for resilient networked microgrids.

Environment

  • BurnPro3D Led by the University of California, San Diego, BurnPro3D is a platform for public sector collaboration to reduce the risk of devastating megafires. Leveraging the WIFIRE Commons data sharing and AI framework, BurnPro3D uses next-generation fire science to prescribe burns for vegetation management at an unprecedented scale.
  • Computing the Biome Led by Vanderbilt University, the team is creating a data and AI platform for monitoring and predicting biothreats in a major U.S. city, and to drive economic sustainability by empowering businesses and advanced research organizations to deliver valuable consumer apps and breakthroughs.
  • CRIPT Led by the Massachusetts Institute of Technology, CRIPT is an AI-enabled cloud application and database that enables polymer scientists to easily find and interact with complex data.
  • HydroGEN Led by the University of Arizona, HydroGEN is a web-based machine learning platform that generates custom hydrologic scenarios on demand.
  • Precision Epidemiology Led by the University of California, Davis, the team is developing an online platform that converges data, AI models, and expertise across the livestock production and health space for the management of animal health.

AI-Driven Innovation via Data and Model Sharing Track Manager: Michael Pozmantier, program director

Track C: Quantum Technology, phase 2 project videos 

PEAQUE 

QuaNeCQT 

Quantum Sensors 

QuSTEAM

Track D: AI-Driven Innovation via Data and Model Sharing project videos

AI-Grid

BrunPro3D

Computing the Biome 

CRIPT

HydroGEN

Precision Epidemiology


2021 cohort

2021 cohort

In September 2021, the Convergence Accelerator launched the 2021 cohort, awarding 28 teams phase 1 awards totaling $21 million. The program’s third cohort is advancing solutions in two critical areas: the Networked Blue Economy, Track E, and Trust & Authenticity in Communication Systems, Track F. During phase 1, teams will work to build a proof of concept for their solution, develop their team and partnerships, and participate in the program’s innovation curriculum. At the end of phase 1, the teams will participate in a formal pitch presentation and phase 2 proposal.

Track E: Networked Blue Economy project image

Ocean-related industries and resources, known as the blue economy, play a central role in addressing challenges related to climate, sustainability, food, energy, pollution and the economy. This track focuses on interconnecting the blue economy and accelerating convergence across ocean sectors — creating a smart, integrated, connected and open ecosystem for ocean innovation, exploration and sustainable use. Collectively, funded research teams will produce tools, methods and educational resources that improve human engagement with the world's oceans as both an environment and a resource.

The Networked Blue Economy phase 1 projects include:

Networked Blue Economy Track Manager: Aurali Dade, program director

Track F: Trust & Authenticity in Communication Systems project image

Modern life depends on access to communications systems that offer trustworthy and accurate information. Economic growth and opportunity depend on dynamic networks for innovation and transaction that connect American families, communities, and businesses to a range of goods and services. Yet, these systems face a common threat. Communication systems can be manipulated or can have unanticipated negative effects. The overarching goal of this track is to address the urgent need for tools and techniques that help our nation effectively prevent, mitigate and adapt to critical threats to communication systems.

The Trust & Authenticity in Communication Systems phase 1 projects include: 

Trust & Authenticity in Communication Systems Track Manager: Michael Pozmantier, program director