The Convergence Accelerator is composed of a variety of convergence research track topics designed to accelerate technologies and solutions toward real-world impact. Since 2019, the program has completed six convergence research tracks.
2019 Track: AI & the Future of Work
The world’s technological advancements in AI, machine learning, and robotics are shifting the future of work in unanticipated ways. The AI & the Future of Work Track focused on solutions to train, reskill, upskill, and prepare the current and future workforce with industry needs and jobs of the future, as well as build a national talent ecosystem to stimulate the U.S. workforce and ensure continuing global competitiveness.
Solutions included developing the U.S. talent pipeline through competency-based training intelligent tools to connect academic institutions with industry needs to prepare students for the workforce, improving workforce training and safety for emergency responders through human augmentation, and creating virtual reality and augmented reality (VR/AR) tools to identify unique skills to prepare people to thrive in the workforce.
The completed AI & the Future of Work funded Phase 2 projects include:
- LEARNER — Led by Texas A&M, is an agile and adaptive Human Augmentation Technologies (HAT) integrated Emergency Response (ER) training platform that accelerates HAT adoption for safer and more efficient ER work, supports adaptive learning sensitive to ER workers’ socio-technical opportunities and budgetary constraints, builds and retains skilled ER personnel, and accelerates next-gen workforce development.
- SkillSync — Industry 4.0 is changing the skills that workers need and companies require, leaving businesses vulnerable and colleges behind. SkillSync, 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.
The completed AI & the Future of Work Phase 1 projects include:
- A Framework for Diagnosis, Recommendation, and Training in Continuous Workforce Development, led by University of Massachusetts Amherst.
- A Universal Framework of Micro-credentials for Nation-wide Employment, led by the State University of New York, Buffalo.
- AI-Based Decision Support for Linking Workers with Future Jobs and for Planning Work Transition and Career Pathway, led by Michigan State University.
- AI-Enabled Personalized Training for Displaced Workers in Materials Supply Chain, led by Colorado School of Mines.
- Analytics-Driven Accessible Pathways To Impacts-Validated Education (ADAPTIVE), led by Indiana University.
- Competency Catalyst, led by Georgia Tech Research Corp.
- Connecting Indiana's Learn-And-Work Ecosystem, led by Credential Engine.
- Developing Intelligent Technologies for Workforce Empowerment: Credential Gap Diagnostics and Personalized Recommenders for Jobs and Retraining, led by North Carolina State University.
- Empowering a digital technology workforce through alignment and coordination of upskilling and reskilling opportunities, led by Business-Higher Education Forum.
- Empowering Neurodiverse Populations for Employment through Inclusion AI and Innovation Science, led by Vanderbilt University.
- Fostering a Diverse AI Workforce, led by Columbia University.
- Learning Environments with Advanced Robotics for Next-generation Emergency Responders (LEARNER), led by Virginia Tech.
- Prepare the US labor force for future jobs in the hotel and restaurant industry: A hybrid framework and multi-stakeholder approach, led by University of Central Florida.
- Preparing the Future Workforce of Architecture, Engineering, and Construction for Robotic Automation Processes, led by Florida International University.
- Rapid Dissemination of AI Microcredentials through Hands-on Industrial Robotics Education (RD-AIM-HIRE), led by Carnegie Mellon University.
- Safe Skill-Aligned On-The-Job Training with Autonomous Systems, led by Arizona State University.
- Skill-LeARn: Affordable Augmented Reality Platform for Scaling Up Manufacturing Workforce, Skilling, and Education, led by Purdue University.
- Smart Platform of Personalized Learning, Assessment and Prediction for Future Career Training of Skilled Workers, led by University of North Carolina, Charlotte.
- Toward Fair, Ethical, Efficient, and Trustworthy Crowdsourcing Platforms to Support Crowdworkers in Jobs of the Future, led by Colorado School of Mines.
- Unlocking the Power of Data and Science to Empower American Workers, led by National Bureau of Economic Research.
- Unpacking the Technology Career Path, led by University of Virginia.
- Upskilling for Future Jobs through NLx Talent Demand Data, led by The Center for Employment Security Education & Research Inc.
2019 Track: Open Knowledge Networks
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. To enable data to be freely accessible, especially to government, academia, small business and nonprofit organizations, the Convergence Accelerator funded the creation of nonproprietary infrastructure to build open knowledge networks, OKNs.
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 available to decision-makers, analysts, researchers, and Americans so they can answer questions they care about.
The completed 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 and supply chains 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 created tools to transform the data into actionable information that aids various users, including legal scholars, policymakers, judiciary and citizens.
- Urban Flooding Open Knowledge Network — Led 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.
The completed Open Knowledge Networks Phase 1 projects include:
- A multi-scale open knowledge network for precision medicine, led by University of California, San Francisco.
- Building the Federalism Data and Advanced Statistics Hub (FDASH), led by University of North Carolina, Charlotte.
- Civil Infrastructure Systems Open Knowledge Network (CIS-OKN), led by University of Illinois Urbana-Champaign.
- Convergence Hub for the Exploration of Space Science (CHESS), led by the Atmospheric and Space Technology Research Associates.
- Convergence Research to meet Ocean Decision Challenges, led by Gulf of Maine Research.
- Credible Open Knowledge Network, led by University of Texas at Arlington.
- Exploiting Financial and Economic Data - Business OKN, led by University of Southern California.
- Knowledge of Internet Structure: Measurement, Epistemology, and Technology (KISMET), led by University of California, San Diego.
- Knowledge Open Network Queries for Research (KONQUER), led by University of California, San Diego.
- Linking the Open Knowledge Network to the Web with End-User Programming, led by University of Washington.
- MPrint-OKN, led by Virginia Commonwealth University.
- Network for Equity in the Era of Driverless Vehicles, led by Regents of the University of Michigan.
- Network Science of Census Data, led by Tufts University.
- Northwestern Open Access to Court Records Initiative, led by Northwestern University.
- Open Knowledge Network for Spatial Decision Support, led by Portland State University.
- Open Knowledge Network for the Global Energy Data Commons, led by Duke University.
- Product Design and Manufacturing Graph-as-a-Service, led by North Carolina State University.
- Scalable Knowledge Network to Enable Intelligent Textbooks, led by Rice University.
- Simultaneous Knowledge Network Programming and Extraction, led by Regents of Michigan University.
- Spatially-Explicit Models, Methods, and Services for Open Knowledge Networks, led by University of California, Santa Barbara.
- The Urban Flooding Open Knowledge Network, led by University of Cincinnati.
2020 Track: AI-Driven Innovation via Data and Model Sharing
AI research and development require access to high-quality datasets and environments and resources for testing and training. The AI-Driven Innovation via Data and Model Sharing Track funded the development of tools and platforms to address data and model-sharing challenges, including easy and efficient data matching and sharing.
The completed AI-Driven Innovation via Data and Model Sharing Phase 2 projects include:
- AI-Grid — Led by Stony Brook University, AI-Grid is an AI-enabled solution for resilient networked microgrids.
- 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 created 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 developed an online platform that converges data, AI models, and expertise across the livestock production and health space for the management of animal health.
The completed AI-Driven Innovation via Data and Model Sharing Phase 1 projects include:
- A Community Resource for Innovation in Polymer Materials, led by Massachusetts Institute of Technology.
- A Standardized Model Description Format for Accelerating Convergence in Neuroscience, Cognitive Science, Machine Learning and Beyond, led by Princeton University.
- A Trusted Integrative Model and Data Sharing Platform for Accelerating AI-Driven Health Innovation, led by Duke University.
- AI-Enabled Provably Resilient Networked Microgrids, led by State University of New York, Stony Brook.
- AI-Enabled, Privacy-Preserving Information Sharing for Securing Network Infrastructure, led by Carnegie Mellon University.
- America's Water Risk: Water System Data Pooling for climate vulnerability assessment and warning system, led by Columbia University.
- Application of sequential inductive transfer learning for experimental metadata normalization to enable rapid integrative analysis, led by Research Triangle Institute.
- Artificial Intelligence and Community Driven Wildland Fire Innovation via a WIFIRE Commons Infrastructure for Data and Model Sharing, led by University of California, San Diego.
- Data & AI Methods for Modeling Facial Expressions in Language with Applications to Privacy for the Deaf, ASL Education & Linguistic Research, led by Rutgers University, New Brunswick.
- Data-driven disease control and prevention in veterinary health, led by University of California, Davis.
- Deep Monitoring of the Biome Will Converge Life Sciences, Policy, and Engineering, led by Vanderbilt University.
- Hidden water and hydrologic extremes: a groundwater data platform for machine learning and water management, led by University of Arizona.
- ImagiQ: Asynchronous and Decentralized Federated Learning for Medical Imaging, led by University of Iowa.
- Intelligent surveillance platform for damage detection and localization of civil infrastructure, led by Howard University.
- Rapid Development of Intelligent, Built Environment Geo-Databases Using AI and Data-Driven Models, led by Oregon State University.
- Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for accelerated Impact (STRAIT I3), led by Vanderbilt University.
- The Data Hypervisor: Orchestrating Data and Models, led by University of Chicago.
- Towards Intelligent Sharing and Search for AI Models and Datasets, led by University of California, San Diego.
2020 Track: 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 Quantum Technology Track developed quantum technologies — sensors, devices, hardware, interconnects, networks and simulations — to deploy in applications such as autonomous vehicles and health care. They also created innovative curricula by leveraging strong industry-university partnerships that are diverse and inclusive.
The completed Quantum Technology Phase 2 projects include:
- PEAQUE — Led by the University of Washington, the team supported quantum computing scalability by innovating a chip-scale, multi-beam optical control system that empowers cold-atom quantum computing with thousands of qubits.
- Quantum Sensors — Led by the University of Arizona, the team developed an entanglement-enhanced sensing architecture to benefit many domains, including secure inertial navigation, space and planetary terrestrial control, and health care monitoring.
- QuaNeCQT — Led by the University of Maryland, the team developed hardware to transform the internet into a quantum internet, which will be essential for connecting the anticipated rapid expansion of quantum computers.
- 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.
The completed Quantum Technology Phase 1 projects include:
- A toolkit for solving practical materials science problems on near-term, led by University of Texas at Austin.
- Chip-scale Integrated Multibeam Steering System for Cold-Atom Quantum Computing, led by University of Washington.
- Chiral-based Quantum Interconnect Technologies (CirquiTs), led by University of California, Los Angeles.
- Cloud-Accessible Integrated Quantum Simulator Based on Programmable Atom Arrays, led by Columbia University.
- High-throughput Proteomics Technology Based on Quantum Sensing, led by University of Chicago.
- Interconnecting Quantum Computers for the Next-generation Internet, led by University of Maryland, College Park.
- National Quantum Literacy Workforce Curriculum and Training Network, led by Morgan State University.
- Quantum-interconnected optomechanical transducers for entanglement-enhanced force and inertial sensing, led by University of Arizona.
- QuSTEAM: Convergent undergraduate education in Quantum Science, Technology, Engineering, Arts, and Mathematics, led by Ohio State University.
- SQAI: Scalable Quantum Artificial Intelligence for Discovery, led by The Pennsylvania State University.
- Synergistic thrusts towards practical topological quantum computing, led by Massachusetts Institute of Technology.
2021 Track: Networked Blue Economy
Ocean-related industries and resources, known as the blue economy, play a central role in addressing ocean-related challenges such as sustainability, food, energy, pollution and the economy. The Networked Blue Economy Track focused on accelerating convergence across ocean sectors — to create a smart, integrated, connected and open ecosystem for ocean innovation, exploration and sustainable use.
The completed Networked Blue Economy Phase 2 projects include:
- Backyard Buoys — Led by the University of Washington, Backyard Buoys empowers coastal communities to collect and use ocean data to support maritime activities and food security.
- BlueGAP — Led by the University of South Florida, the BlueGAP project connects communities across watersheds to address economic and health challenges caused by nitrogen pollution.
- Digital Reefs — Led by the Woods Hole Oceanographic Institute, Digital Reefs delivers interactive 4D visualizations of reef environments into the hands of local communities, helping to ensure a future for all coral reefs.
- Nereid Biomaterials — Led by the University of California, Santa Barbara, Nereid Biomaterials is enabling a healthier ocean through safe and rapid ocean degradation of plastic components of equipment. The team is developing "ocean degradable" polymers with embedded additives to accelerate and control degradation.
- FathomNet — Led by Monterey Bay Aquarium Research Institute, FathomNet, formally Ocean Vision AI, accelerates the processing of underwater visual data with a globally integrated network of services, tools, and a wide range of community users. FathomNet streamlines access and analysis of visual data to enable effective marine stewardship.
- ReCoast — Led by Tulane University, ReCoast is ensuring mitigation of land loss through coastal community recycling programs to keep glass out of landfills by creating glass sand products that support coastal restoration and preservation.
The completed Networked Blue Economy Phase 1 projects include:
- A Globally Coordinated, Universally Accessible Digital Twin Network for the Coral Reef Blue Economy, led by Woods Hole Oceanographic Institution.
- Advancing Innovative Convergence Between Fisheries and Offshore Energy to Drive Adaptive Stewardship of Fisheries Habitat in a Dynamic Blue Economy, led by Blue Latitudes, LLC.
- Combining High-resolution Climate Simulations with Ocean Biogeochemistry, Fisheries and Decision-making Models to Improve Sustainable Fisheries, led by Texas A&M University.
- Convergence Towards Nationwide Smart Precision Aquaculture Networks for Sustainable Shellfish Farming, led by the University of Maryland, College Park.
- Data Governance to Support an Equitable and Sustainable Blue Economy, led by Multiplier.
- Data Interfacing of Human Use, Culture, Economics, and Environment within the Blue Economy for Underserved Populations, led by West Virginia University.
- Developing Blue Economy from Micro to Macro-scale in Kelp Aquaculture, led by San Diego State University.
- Empowering Stakeholders from Ship to Store, Solving Fishery Management Challenges with Use-Inspired Genomic and Artificial Intelligence Tools, led by Michigan State University.
- Equipping Underserved Communities with Ocean Intelligence Platforms, led by the University of Washington.
- Innovative Seafood Traceability Network for Sustainable Use, Improved Market Access, and Enhanced Blue Economy, led by Loyola Marymount University.
- Linking the Green Economy to the Blue Economy at the Coast, led by the University of South Florida.
- Next Generation Biomaterials with Engineered Biodegradability to Enable Networked Swarm Sensing in the Ocean, led by the University of California, Santa Barbara.
- Ocean Vision Artificial Intelligence: Scaling Up Visual Observations of Life in the Ocean Using Artificial Intelligence, led by Monterey Bay Aquarium Research Institute.
- Reconfiguring Urban Shorelines for Resilience: Convergence Research Meshing Ecology, Engineering, and Architecture, led by Stony Brook University.
- Regional Climate Change Projections to Enable Equitable Ocean Planning for the Blue Economy, led by Rutgers University.
- Using Recycled Glass Sand to Promote Resilience and the Blue Economy in Coastal Communities, led by Tulane University.
