NSF Convergence Accelerator 2019 Cohort Videos

The Convergence Accelerator's 2019 Cohort phase I included 43 teams focused solutions in two track topics—Open Knowledge Networks (Track A) and The Future of Work (Track B). Both track topics support two of NSF's Big Ideas": Harnessing the Data Revolution and the Future of Work at the Human-Technology Frontier.

During Phase I, a nine-month planning effort, teams leveraged the Convergence Accelerator's fundamentals and innovation curriculum to identify new team members and to further develop the identified solution. The innovation curriculum consists of training in human-centered design, team science activities, inter-team communications, pitch preparation, and presentation coaching—all of which are essential components of the Accelerator's model. At the end of phase I, each team participated in an Expo, pitch competition and a proposal evaluation. Selected teams from phase I were then selected into phase II. These videos feature 2019 Cohort Phase I teams and were crated for the 2020 Convergence Accelerator Expo. 

Open Knowledge Networks | Track A


Knowledge networks pool together many types of information and ideas so that they can be accessed and leveraged to create new understanding. These networks have become important tools for many large organizations that are taking advantage of the current Big Data revolution. However, these vast information networks are often unavailable to many in government, academia, small businesses and nonprofits. The Convergence Accelerator funded 21 phase I teams to develop Open Knowledge Networks infrastructure; tools to identify, harvest, and incorporate datasets into a network; and open knowledge network elements to address specific challenges, such as judicial records, urban infrastructure, geosciences, and biomedicine.

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Building the Federalism Data and Advanced Statistics Hub

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The NSF Convergence Accelerator funded Federalism Data and Advanced Statistics Hub (F-DASH) is a cutting-edge data hub that links open data with dynamic data collection of government institutions, political actors, proposals, rules, laws, and outcomes at the federal, state, and local level. This tool allows citizens, policymakers, journalists, and researchers the ability to compare policy proposals and outcomes across states and over a long period; empowering evidenced-based policy evaluation in a simple to use interface.  
     

The Business Open Knowledge Network

 
Entrepreneurship is imperiled due to information inequity. The NSF Convergence Accelerator funded Business Open Knowledge Network is a resource harvested from terabytes of web data, regulatory filings, patents, social media, and more; capturing competitors, customers, partnerships, and technology for nearly a million companies. This resource empowers entrepreneurs with the market intelligence necessary to develop more successful business plans.
     

Civil Infrastructure Systems Open Knowledge Network

 
The NSF Convergence Accelerator funded Civil Infrastructure Systems Open Knowledge Network (CIS-OKN) enables new modes of data-driven and AI-enabled discovery, innovation, and decision making in the civil infrastructure system domain. This effort improves the safety, accessibility, and economic opportunity for all citizens.
     

Convergence Hub for the Exploration of Space Science: Towards a Space Weather Open Knowledge Network

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We live in a technologically-dependent, space-faring society that makes an understanding of the space environment (i.e., space weather) critical to each of us. Learn how the NSF Convergence Accelerator funded Convergence Hub for the Exploration of Space Science: Towards a Space Weather Open Knowledge Network (CHESS) approach is using a knowledge network to break down silos in this transdisciplinary domain and making a society resilient to space weather. 
     

Credible Open Knowledge Network

 
The NSF Convergence Accelerator funded team is building a framework and tools to assist software developers and domain experts to ensure the credibility of their decision-making software powered by open knowledge networks. Applying technologies and tools to practical domains, the team is constructing knowledge graphs and software to assist healthcare professionals and app developers in mitigating health misinformation and to help cybersecurity professionals in accurately assessing and mitigating software vulnerability.
     

Enabling Lifecycle Collaboration in Chemicals and Materials Manufacturing Systems with Open Authoritative Knowledge

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The NSF Convergence Accelerator funded Chemicals & Materials Open Authoritative Knowledge (OAK) team is creating an ecosystem of data, tools, and people to support the chemical and materials industry and education from material procurement to end-of-life. The information architecture in this ecosystem will support academic and commercial partnerships through interactive cloud services and enable market-to-manufacturing connections for those who manufacture, formulate, and benefit from chemicals and materials.
     

The Global Energy Data Commons for natural hazard risk assessment for energy systems

 
The NSF Convergence Accelerator funded Global Energy Data Commons is a web-based platform and community that informs the assessment of natural hazard risks for energy systems. This solution enables users to easily find, view, download, or otherwise integrate natural hazards data—including wildfires, flooding, droughts, earthquakes, and even solar flares—into their risk analysis through consistent data standards and APIs.
     

Knowledge Network Infrastructure

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Knowledge networks and their applications represent large opportunities but are too technically difficult and expensive to build. This NSF Convergence Accelerator funded project aims to build Knowledge Network programming infrastructure, which  will be validated by constructing concrete networks on COVID-related topics.
     

KnowWhereGraph

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The NSF Convergence Accelerator funded KnowWhereGraph uses open graph technologies to enable geo-enrichment services to add rich contextual information for any study to make data-driven decisions more effective, accessible, and affordable. This effort reduces the resources required for data retrieval, entering, and cleaning.
 

KONQUER

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The NSF Convergence Accelerator funded KONQUER tool removes barriers for researchers to find, integrate, and analyze cross-disciplinary data to conduct informed research. 
     

Knowledge of Internet Structure: Measurement, Epistemology, and Technology

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The NSF Convergence Accelerator funded Knowledge of Internet Structure: Measurement, Epistemology, and Technology (KISMET) project improves the security and functioning of three critical and vulnerable systems that underpin all activity on the Internet, by changing the landscape from reactive to proactive.
     

Multi-Scale OKN for Biomedicine

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The NSF Convergence Accelerator funded Multi-Scale OKN allows doctors, researchers, the pharmaceutical industry, and citizen scientists to much more effectively understand and explore biomedicine.
     

Network Science for Census and State Geodata

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America's public administration infrastructure requires data modernization. This NSF Convergence Accelerator funded effort is developing an integrated geo-data management tool to leverage the network structure in public geo-electoral data.
     

OceanOKN: Using Ocean Data to Help Fishing Businesses Prepare for a Changing Ocean

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The NSF Convergence Accelerator funded OceanOKN brings together climate, fish, fisheries, and humans data using a novel model-centered knowledge graph. Using advanced AI techniques, the project develops projections to assist fishermen, coastal communities, managers, and businesses make forward-looking decisions.
     

Open Knowledge Network for Collaborative Decision Making

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Imagine a world in which we harness the innovations of information technology and social learning to solve humanity's existential challenges. Current internet search tools are not designed to support local or regional spatial decision-making. Yet it's at these scales that most community decisions are made. This NSF Convergence Accelerator funded project is addressing this gap by integrating three technologies: geographic information systems, knowledge graphs, and collaborative workspaces.

Product Design and Manufacturing Knowledge Graph as a Service

 
The NSF Convergence Accelerator funded Product Design and Manufacturing Knowledge Graph as a Service project connects product design assets and manufacturing services to create a manufacturing knowledge graph.

Putting Web Data in Reach for Everyone

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The NSF Convergence Accelerator funded Open Knowledge Network project empowers non-coders to collect custom datasets. For every stage, including extracting data from webpages to integrating diverse datasets, the solution's infrastructure will put data processing programs in reach for the average computer user.

SCALES

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The NSF Convergence Accelerator funded SCALES team is composed of computer and data scientists, legal scholars, journalists, and policy experts to develop a suite of tools to enable access to court records and analytics.
     

Textbooks Reimagined: Knowledge Graph Enabled Intelligent Textbooks 

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The NSF Convergence Accelerator funded project is developing an intelligent textbook to improve student learning outcomes. This solution leverages a knowledge graph to answer questions asked by the student, allowing for deeper learning.

Transportation Equity Open Knowledge Network

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The NSF Convergence Accelerator funded Transportation Equity Open Knowledge Network enables transportation and economic development officials, private companies, and advocacy groups to make equitable planning and investment decisions by providing data-driven insights.

Urban Flooding OKN 

 
The NSF Convergence Accelerator funded Urban Flooding OKN is delivering flood information to anyone, anytime, and anywhere.

The Future of Work | Track B


The Future of Work focuses on Artificial Intelligence (AI) and future jobs and the national talent ecosystem, including workforce education and training to foster lifelong and universal learning. Using AI, data science, mix reality, and other tools, techniques, and approaches; teams are developing innovative solutions to reskill, upskill, and prepare the future workforce required for 21st century work related to AI, data science, predictive analytics, and other technologies and industries of the future.
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AI-based Multi-Level Skill Analysis and Assessment for Personalized Training of Skilled Workers

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The NSF Convergence Accelerator funded project innovates the training of skilled workers through a smart, personalized, and augmented training platform that coordinates training across organizations. The core technology is AI-based analysis methods for understanding human performances and skills during the training so that we can provide personalized programs to suit individual needs.
     

AI&Work360

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The NSF Convergence Accelerator funded AI&Work360 is an extensible, collaborative artificial intelligence platform and marketplace that continually learns from data to enable personalized, context-adaptive, and frictionless job search, transition, and pathways, internal and external recruitment, and training and workforce development. The solution is for individuals, organizations, training providers, policymakers, and software developers.
     

BHEF's Upskilling Solutions Marketplace

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The NSF Convergence Accelerator funded BHEF Upskilling Solutions Marketplace is an employer-focused technology platform that efficiently connects employers, employees, app developers, labor-market data feeds, and education/training providers. Outcomes include new jobs for workers displaced by COVID-19, upward mobility for current workers, and improved alignment between business and higher education.
     

Compact for a Connected Learn-and-Work Ecosystem

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The NSF Convergence Accelerator funded Compact for a Connected Learn-and-Work Ecosystem solution captures insights from a broad set of local, state, regional, and national stakeholders to establish a critical mass of public credential and competency data using the Credential Transparency Description Language (CTDL) schema.

 

     

The Data for Opportunity in Occupation Reskilling Solution

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The NSF Convergence Accelerator funded Data for Opportunity in Occupation Reskilling Solution (DOORS) uses machine learning, artificial intelligence, and modern cloud computing to create and deliver valuable metrics to displaced workers seeking reskilling. The solution enables workers to choose programs to prepare them for and place them in new careers. In partnership with state governments across the country, the solution unlocks the power of administrative data and science to kick-start a virtuous cycle of informed decisions, improved reskilling, and improved economic outcomes for workers, families, and communities per dollar spent on training.

DIRECT

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The NSF Convergence Accelerator funded DIRECT is a framework for job matching, continuous training, and career path planning in the future of work. With a multidisciplinary team to include academia and industry partners, DIRECT is a complete solution for continuous workforce development in the manufacturing industry and beyond.
     

Factory Forward

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The NSF Convergence Accelerator funded Factory Forward solution supports small and medium-sized manufacturers in the US. It consists of two parts: BootUP, which connects underserved populations to entry-level manufacturing positions, and SkillUP—which trains incumbent workers to spot meaningful technology advancement opportunities that benefit them and their companies.
     

LEARNER

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The NSF Convergence Accelerator funded LEARNER is an agile and adaptive human augmentation technology-integrated emergency response training platform. The solution accelerates skills and expertise development for safer and efficient emergency response work, supports adaptive learning across different augmentation technologies, and ultimately accelerates next-gen workforce development across other industries (e.g., manufacturing and construction).
     

National Labor Exchange Research Hub

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The NSF Convergence Accelerator funded National Labor Exchange Research Hub is the first national, open ecosystem of real-time and historical labor market information to improve connections between jobseekers and employers.
 

NeuroAI@Work

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Upwards of 85% of Americans with Autism Spectrum Disorder (ASD) are unemployed or underemployed relative to their skill levels. The NSF Convergence Accelerator funded NeuroAI@Work solution uses artificial intelligence to build an employment pipeline for neurodiverse individuals.
     

Personalized AI Guidance Ecosystem (PAIGE)

 
The NSF Convergence Accelerator funded Powered by human-centered AI techniques, the Personalized AI Guidance Ecosystem (PAIGE) accelerates future workforce transition with a personalized, adaptable, and scalable design. PAIGE will guide job seekers through a fast and fair job transition, help employers select best-fit candidates in an accelerated, cost-effective manner, and provide scalable programs with higher impact for governments and education providers.
     

Robotics Academy

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The NSF Convergence Accelerator funded Robotics Academy is a cloud-based, AI-powered software platform providing training, innovation, and investment resources for the future building industry cluster.
     

Safe Skill-Aligned On-The-Job Training for Work with Autonomous Systems

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The NSF Convergence Accelerator funded project enables the creation of a new job category in manufacturing: robot taskers. The solution provides agile production and an upskilled workforce by adapting the skills of robot taskers and training. 
     

Shaping future careers for America's Most Vulnerable Workers

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The economic restructuring necessitated by the COVID-19 pandemic is a harbinger of a more fundamental transformation of our economy and workforce. This NSF Convergence Accelerator funded project focuses on improving worker outcomes in an increasingly dynamic and digital labor market. This includes connecting workers' potential to rising career development opportunities, recommending viable career paths, and developing an efficient and secure system for job matching and skill training.
     

Situational Awareness for Employment Resilience

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The NSF Convergence Accelerator funded Situational Awareness for Employment on millions of jobs and associated skills Resilience (SAFER) project is transforming millions of job and associated skills data into powerful tools for workers and career coaches to make data-driven decisions. The solution is developing and deploying will personalized apps and comprehensive data visualizations of near-real-time labor market data to enhance situational awareness of emerging job risks emanating from advances in automation or fast-moving threats, like the COVID-19 pandemic. For additional information, visit https://cns-iu.github.io/safer/

SkillSync

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The NSF Convergence Accelerator funded SkillSync project consists of a Competency Catalyst open data platform and the Skillsync web application. The solution converts and aligns job and course data into underlying knowledge, skills, and abilities enabling employers to define their reskilling needs, connecting them to colleges, and helps colleges rapidly created targeted reskilling programs.

SkillXR

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The NSF Convergence Accelerator funded SkillXR platform makes augmenting human skills a reality. The solution focuses on skills training and analytics in manufacturing workforce education.

Toward Fair, Ethical, Efficient, and Trustworthy Crowdsourcing to Support Jobs of the Future

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The NSF Convergence Accelerator funded team is developing a suite of novel client-side and server-side techniques for fair, ethical, efficient, and trustworthy crowdsourcing. These techniques will support millions of American workers for jobs of the future, connecting them in both traditional and gig economies.

Unpacking the career path with AI

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The NSF Convergence Accelerator funded artificial intelligence-driven career planning tool models STEM career paths for under-represented groups and their plans. The solution is currently at the beginning stage.

Workforce Empowerment and Retraining: Internet of Solutions for Employment

 
The NSF Convergence Accelerator funded Workforce Empowerment and Retraining: Internet of Solutions for Employment (WE-RISE) solution removes the interoperation barriers among workers, employers, trainers, and support providers in workforce development. A free browser plugin, the solution brings one-stop-shop solutions to include credential gap analysis and recommendations of job, training, and wrap-around services to millions of job seekers as they search for jobs using their favorite recruiting websites.