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Photo of Dr. France A. Cordova

Dr. France A. Córdova
U.S. National Science Foundation


University at Buffalo Critical Conversations

April 3, 2019

Photo: NSF/Stephen Voss

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Title slide title: NSF Convergence Accelerator

Slide words:
Dr. France A. Córdova
Director, NSF

University of Buffalo
Critical Conversations
April 3, 2019

Slide image: graphic suggesting the convergence of research from concept to deliverables.

Image credit: design by Trinka Kensill, NSF

Thank you, President Tripathi, and thanks to the University at Buffalo for having me today.

I’m always pleased to participate in a program like Critical Conversations. It gives me the chance to talk about some of the high-level concepts and agendas driving our work at the National Science Foundation. And it also allows me to hear directly from the you about the issues that are important to you. I’m looking forward to hearing your thoughts today.

I am going to focus on two key concepts: Convergence and catalysts. Using these ideas, NSF is creating opportunities to make the U.S. research enterprise stronger and stimulate rapid growth in areas with the potential to benefit all of us.

“Convergence” is likely a word you’ve heard before, but perhaps not in this context. NSF and its partners in the scientific community are using convergence to define their vision for a future where researchers from across disciplines collaborate to solve grand challenges in basic research.

Now, some of you may hear that and think that I just defined interdisciplinary research. And that’s a related idea. But convergence is more specific, because it requires starting efforts to solve specific, compelling problems with deep, intentional integration across disciplines.

That word “intentional” is important. We’re not talking about a biologist producing research, then collaborating with an engineer or a physicist to expand on it. We’re talking about purposefully bringing together intellectually diverse researchers – and even stakeholders from outside of the traditional research community – to frame questions and figure out how to answer them.

The questions convergence can answer cover a broad range of topics.

They can include challenges that have vexed the research community for years. For example, how do we create nanoscale materials and devices? Through the convergence of chemistry, physics, engineering and other disciplines.

Or they can include questions about how to address pressing societal needs. How can we develop cyber-physical systems, technology that encompasses everything from smart grids to medical products that interact directly with the physical world? We’re doing it by bringing together life sciences, physical science, engineering and the social and behavioral sciences.

When you talk to a lay audience about convergence, it sounds deceptively simple. NSF just has to issue grants that bring people to the same table, they sit down, and they use their expertise to map out paths to solutions.

But of course, as is so common in research, the answer is more complicated than that.

As many of you know, our scientific ecosystem developed over time, a combination of different traditions, different systems, different ways of operating. As a result, effective convergence requires the development of effective ways of communicating across disciplines. You need common frameworks, a common language. And you need to develop new ways to frame research questions. The goal is to grow a culture of collaboration.

That culture is growing right here in Buffalo, where you have centers studying issues like microbiomes, genomics, and addiction. They’re asking questions that can’t be answered without the kind of deep integration I’m talking about.

In some ways, we’re talking about approaching research based on the questions we’re asking, not the discipline where it fits most comfortably. That’s a big shift, a novel way to understand how the scientific enterprise can understand the vital issues it faces. And that can be uncomfortable.

Keeping science siloed. That limits our potential for discovery. To expand into new frontiers, we need to integrate knowledge and ways of thinking from multiple disciplines, and continually tear down the siloes that we erect.

NSF is usually described as a funding agency – we provide grants and other awards that support researchers and institutions. In fact, we’re currently supporting dozens of awards here in Buffalo, for research ranging from the use of x-ray lasers in biology to high-energy physics. But part of our mission is to help shape the future of U.S. basic research, based on input from the community about what would be the most productive approaches.

Slide Title: NSF's 10 Big Ideas

Slide words: (clockwise from top left)
Research Ideas
-Harnessing the Data Revolution
-The Future of Work at the Human-Technology Frontier
-Windows on the Universe: The Era of Multi-messenger Astrophysics
-The Quantum Leap: Leading the Next Quantum Revolution
-Understanding the Rules of Life: Predicting Phenotype
-Navigating the New Arctic

(clockwise from bottom left) 
Enabling Ideas
-Mid-scale Research Infrastructure
-NSF 2026: Seeding Innovation
-NSF INCLUDES: Enhancing STEM through Diversity and Inclusion
-Growing Convergence Research at NSF

Slide images: (clockwise from top left) word graphic about data science; illustration of creative teams working on giant digital tablets and communicating digitally; aerial photo of LIGO in Livingston, LA; illustration of quantum computation with trapped ions; photo of seedling being watered by hand; photo of radio telescopes at ALMA in Chile; photo of IceCube Neutrino Observatory in Antarctica; aerial photo of melting ice in the Arctic

(bottom clockwise from left) photo of a broken bridge; graphic suggesting future ideas; graphic suggesting inclusion and diversity; illustration suggesting convergence

Image credits: (clockwise from top left) James Kurose, NSF; Jesus Sanz/; LIGO Scientific Collaboration; Joint Quantum Institute, University of Maryland; ©; F. Fleming Crim, NSF (2); Roger Wakimoto, NSF
(clockwise from bottom left) ©; © and design by Adrian Apodaca, NSF; design by Trinka Kensill, NSF; National Research Council of the National Academies Press

Convergence is one such approach. So, if you are pursuing convergence research, we’re looking to provide new and expanding opportunities for you. Three years ago, NSF unveiled a set of what we call our “Big Ideas,” areas of science and engineering where targeted investment has the potential to result in big gains. Growing Convergence Research is one of those ideas.

Today, you can find NSF support for convergence research in many of our longstanding programs and portfolios. But we’re stepping up our investment. We’re also providing funding for convergence research that falls outside of NSF’s current programs and initiatives. We want to see what happens when multidisciplinary teams apply creativity and ingenuity to come up with research questions that we haven’t even thought of.

I mentioned the NSF 10 Big Ideas, and I want to discuss two more of them with you: Harnessing the Data Revolution and The Future of Work at the Human-Technology Frontier. What these two ideas have in common is that they’re both focused on challenges that we, as a nation and a scientific enterprise, need to address right now.

They’re emerging issues, and getting in front of them means new advances that will drive our position as a global innovation leader and it will define a new economy. Getting behind means lost opportunities.

Harnessing the Data Revolution recognizes that we are in an era where data-driven research is redefining our capabilities for discovery. We want to make sure that we harness that potential to the fullest extent possible. This will be essential not only for expanding the horizons of science, but for keeping the U.S. competitive. We’re not the only ones who have realized that we’re in the midst of a data revolution.

The Future of Work at the Human-Technology Frontier considers where the country and the research community is headed because of AI, robotics, and other computer-based technologies changing how we live, communicate, move from place to place and do our jobs.

The employment landscape is changing at a breathtaking speed, and as a society, we’re reconceiving work and the workplace. If we focus on enhancing the lives of workers, technology can augment human performance. The hope is to develop a future employment pool with a lifelong drive to learn and apply new skills on the job. Technology can create new careers and occupations. If we understand how we interact with it, we can shape technology to augment our workforce.

To reach that point, we need to find out how technology is already changing our offices and factories and schools, and how it might continue to do so. Enhanced technology often comes with a risk for workers – jobs can become automated or skills can become obsolete.

But today’s technology can carry additional concerns, including privacy and fairness. There is a need for policies and practices that guide implementation. NSF is looking to respond to these challenges.

Striking while the iron is hot is important for both of these Big Ideas, and Harnessing the Data Revolution and the Future of Work at the Human-Technology Frontier both promise to provide dramatic returns on investment. That is why NSF is looking to accelerate their development. We want a catalyst that will create the momentum needed to propel these ideas forward. That catalyst? It’s convergence.

Slide title: What is a Convergence Accelerator?
A new organizational structure to accelerate the transition of convergence research into practice, in areas of national importance

Slide words:
(left) Characteristics

  • Use-inspired research (Pasteur’s Quadrant)
  • Testbeds, tools, living labs...
  • Partnerships with industry
  • Clear goals, milestones, directed deliverables

  • (right) Management
  • Time-limited “tracks”
  • Cohorts
  • More directed management
  • Mission-driven evaluation
  • Just 10 days ago, NSF formally announced a new vehicle, called a Convergence Accelerator. Here is what a Convergence Accelerator is.

    I said earlier that convergence involves intentional, deep collaboration to solve specific, compelling problems. But that collaboration doesn’t have to be limited to the academic research community.

    We want our convergence accelerator to build bridges among industry, academia, non-for-profits, government entities, and others, because these types of partnerships are effective in producing breakthroughs.

    From AI to quantum computing, and from supercomputing to data science, NSF has found time and again that when we combine resources and ideas from these potential partners, we stimulate innovation and efficiency. And these partnerships often produce research results that can quickly move from the lab to applications that can benefit Americans.

    The NSF Convergence Accelerator will channel that kind of energy. To do that, it will focus on bringing together teams of multidisciplinary researchers. All of these teams will be focused on common, nationally important research goals. But they will be pursuing different approaches to achieve it. This approach will help us stimulate innovation by encouraging as many ideas as possible in pursuit of a common goal.

    The Convergence Accelerator will also feature a new experiment for NSF in the way it funds proposals.

    When people submit ideas for the Convergence Accelerator, their first step will be to gather their teams, make their plans and develop a proof of concept.

    They will then take all of that information and make pitches for support to a blue-ribbon panel of experts.

    If you have ever applied for NSF funding, you know that this is very different from how we usually operate. Why are we doing this? Because working this way will help applicants focus on the value proposition and intended outcomes of their proposals. In today’s competitive environment, those skills are crucial. And we’re also embarking on this experiment because we, too, are always looking to innovate. Experiments like the Convergence Accelerator allow us to incorporate new approaches, then look back and see what works.

    Slide title: NSF Convergence Accelerator
    Initial Tracks

    Slide words:
    (left) Open Knowledge Network
    Enhancing scientific data discovery and use

    (middle) AI & Future Jobs
    Connecting, retraining and reskilling for jobs using AI

    (right) National Talent Ecosystem
    Building talent in a changing workplace

    Slide images: (left to right) logos of NASA, LIGO, NSF, LSST, DOE, IceCube and icons representing other institutions and partners (middle) photo of woman using virtual reality glasses
    (left) photo of a group of diverse professionals

    Image credits: (left to right) design by Trinka Kensill, NSF; franz12/; Syda Productions/

    So, what do we want to accomplish through the Convergence Accelerator. Some of you may be familiar with the idea of an “Open Knowledge Network.” Knowledge networks are important tools for today’s companies. They pool information and ideas into a place where they can be accessed across an organization.

    Of course, if you’re a private company, this network is proprietary – after all, you don’t want to share that information with competitors. These networks are also expensive to construct and operate, so a private company wouldn’t be inclined to share that resource.

    This means we have vast information networks popping up around the country and the world, inaccessible to government, academia, small businesses and nonprofits. All of those groups are important members of the research community, and all of them could benefit greatly from that kind of linked data.

    NSF and government agencies have worked to address that gap for years, supporting the creation of knowledge networks in specialized areas ranging from astronomy to geosciences. But scientists and engineers who work with Big Data have for some time been discussing the idea of an Open Knowledge Network, a much more comprehensive effort – one that could help drive the next wave of AI development and help research translate into commercial development. In fact, in 2017 a White House working group called for the creation of an Open Knowledge Network.

    That’s why one of our Convergence Accelerator targets is to turn that idea into a reality, with the creation of a true Open Knowledge Network, one that allows us to harness the data and apply the power of data and AI to advance science and grow the economy. We want to spark the development of infrastructure that allows nonproprietary knowledge to be shared, stored and accessed easily.

    Right now, we’re looking to support the convergence foundation needed for this complex task. We’re bringing together multidisciplinary teams that can identify the pathways for development an Open Knowledge Network. In the future, we’ll be looking at how to take the findings of these teams and support the creation of this kind of network.

    This is a case where a broad spectrum of partners would be extremely helpful. Again, the proprietary knowledge networks of today are largely industry-built. That means the private sector can give us insight on how to build and maintain a successful network – and, since an Open Knowledge Network can be used by anyone, they would benefit from its creation.

    Someday soon, I hope that network will be available for the benefit of everyone in this room.

    So that’s how our Convergence Accelerator will catalyze Harnessing the Data Revolution. For the Future of Work at the Human Technology Frontier, the Convergence Accelerator will work to help today’s workers transition into tomorrow’s workplaces.

    Many groups have already helped define our national challenges when it comes to our workforce. Most recently, a 2018 report from the White House Council of Economic Advisors found that even in a booming job market, the skills possessed by our labor force are often mismatched with the needs of employers – and that is largely due to the fact that technology keeps changing those skill requirements.

    U.S. education and employment has traditionally relied up a “frontloaded” model for skills – you pick them up in your first 25 years, while you’re in school or recently added to the workforce, and they carry you through your career. Thanks to rapid technological advances, that means we have a situation where current workers lack the skills to perform 21st Century work – and, even more troubling, so do our recent graduates moving into the marketplace.

    To address those needs, the Convergence Accelerator will support the development of new tools to connect workers with jobs, keeping in mind that reskilling and retraining is particularly important for modern workers. And the Convergence Accelerator will fund research and development that finds ways that employers can support those workers who seek the skills to operate in a world where data science, AI and other technologies are parts of everyday life.

    These goals require traversing some difficult terrain. Disabilities, family responsibilities, location and many other factors can affect someone’s ability to acquire skills and reskill to adapt to a changing workplace. That’s why convergence is especially important in this area. Bringing together the broadest group of people possible to address this challenge increases the odds of finding solutions.

    The Convergence Accelerator, The Big Ideas, convergence research – these are all ambitious attempts to produce research that changes the face of the country and the world. NSF can’t do it alone. We need to invest in the approaches of members of the research community like you. And moving forward, we will also need an increasing amount of assistance from our partners in industry, the non-profits, government agencies and others. These are the first pilots for our Convergence Accelerator debut. Perhaps you can imagine the next focuses! What research do you think we need to speed up?

    Thank you.