Authors: Matt Campagna (Amazon), Brian LaMacchia (Microsoft Research), and David Ott (VMware Research)

Few people are aware that each advancement we make in quantum computing brings us closer to a complete breakdown of our digital security and privacy. As quantum-driven advancements in cryptographic analysis and computing technology are achieved, the cryptographic algorithms that protect our private online information and data is threatened.  This paper identifies issues that need to be addressed before the quantum transition, particularly in identifying a replacement for current cryptography algorithms and ensuring a safe transition of uses.

Authors: Shuchi Chawla (University of Wisconsin-Madison), Jelani Nelson (UC Berkeley), Chris Umans (California Institute of Technology), and David Woodruff (Carnegie Mellon University)

This paper presents the case for robust support of research and funding of foundational work in Theoretical Computer Science (TCS), highlighting three major areas of current interest in the field. Work in this discipline benefits the entire field of computer science by offering insights to the possibilities and limitations of computation, identifying key issues in new areas and discerning computation and algorithms in settings beyond computer science.

Authors: Elisa Bertino (Purdue University), Daniel Bliss (Arizona State University), Daniel Lopresti (Lehigh University), Larry Peterson (Princeton University), and Henning Schulzrinne (Columbia University)

Wireless networking has seen explosive growth over the past decade and continues to evolve rapidly. Cellular technology is now progressing from 4G and its potential for broadband speeds to mobile devices, to 5G which will further enhance transmission speeds, cell capacity, and latency through the use of different radio technologies. 6G is already envisioned to employ virtualization across all layers and the pervasive deployment of AI. On the hardware side, new classes of highly flexible application specific processors (ASICs) are being developed, driving further progress. In addition, deep programmability will open up enormous opportunities to manage the complexity and harness the power of the new network infrastructure. This paper explores these key technologies and outlines a research agenda to make them a reality.

Authors: Thomas Conte (Georgia Institute of Technology), Ian Foster (University of Chicago), William Gropp (University of Illinois at Urbana–Champaign), and Mark Hill (University of Wisconsin-Madison)

As the 55-year reign of Moore’s Law comes to an end, new computing techniques will be required in order to continue the improvement of computer speed and performance. This paper outlines a number of techniques that will lead to advancements in the computing field and benefits society as a whole.

Authors: Nadya Bliss (Arizona State University), Elizabeth Bradley (University of Colorado, Boulder), Joshua Garland (Santa Fe Institute), Filippo Menczer (Indiana University), Scott Ruston (Arizona State University), Kate Starbird (University of Washington), and Chris Wiggins (Columbia University)

This paper describes a multi-disciplinary research agenda incorporating disinformation detection, education, measurements of impact, and a new common research infrastructure to combat disinformation and its effects upon the US and the world.

Authors: Sujata Banerjee (VMware Research), Yiling Chen (Harvard University), Kobbi Nissim (Georgetown University), David Parkes (Harvard University), Katie Siek (Indiana University Bloomington), and Lauren Wilcox (Georgia Institute of Technology)

Via the Internet and IoT systems, there is an increasing amount of data being collected on individuals every day. This paper dives into the big questions related to this phenomenon, such as who owns the data, the implications for using, controlling, and quantifying the data, and, most importantly, how to best protect citizens’ privacy.

Authors: Raja Kushalnagar (Gallaudet University), Maria Gini (University of Minnesota), and Patty Lopez (Intel Corporation)

Diversity enhances computing creativity. This paper recommends computing workforce diversification support through proven strategies of funding Masters programs at institutions committed to diversity, such as Minority Serving Institutions and Special Institutions, or in providing stipend funding for women, black, indigenous and other people of color, or people with disabilities for Masters or undergraduate research programs.

Authors: Susanne Hambrusch (Purdue University), Lori Pollock (University of Delaware), Ran Libeskind-Hadas (Harvey Mudd College), and Christine Alvarado (University of California, San Diego)

The continuing demand for PhDs in computer science combined with this instability of international student participation requires bold action to increase the number of domestic students completing a PhD in computer science, especially as the  percentage of domestic PhD students has decreased from 69% in 1985 to 37% in 2018.  This report presents bold ideas on how government, industry, and academia can take action to engage domestic students to enter PhD programs and retain them through graduation.  It focuses on increasing opportunities and funding for undergraduate research, creating new pathways into PhD programs, engaging students from admissions through PhD, and strengthening industry’s role in increasing the number of PhDs in CS.

Authors: Jan Cuny (Northeastern University), Andrea Danyluk (Williams College), and Holly Rushmeier (Yale University)

In order to maintain its political and economic position in the world, and for that position to benefit its citizens, the United States must build and retain the strongest and most innovative tech talent at all levels. While the CS4All movement is increasing the preparation of current K-12 students for future tech careers, the U.S. cannot wait for future generations to fill the current tech gap. Today’s post-graduate population represents a valuable untapped resource for the country’s workforce needs. This paper outlines opportunities and requirements to fill the tech gap with individuals who bring to the field a diversity of experience and perspectives to fuel innovation, as well as overcome problems of social justice and equity.

Authors: Sujata Banerjee (VMware Research) and Elisa Bertino (Purdue University)

Could AI better support societal needs if it were deployed at the edge of the network, close to application end-points, as opposed to in a centralized cloud? This white paper examines those potential uses and identifies requirements and areas of research that need to be explored before implementation of AI systems at the edge can be realized.

Authors: Elisa Bertino (Purdue University), Finale Doshi-Velez (Harvard University), Maria Gini (University of Minnesota), Daniel Lopresti (Lehigh University), and David Parkes (Harvard University)

This paper argues for further research in AI and human cooperation in order to understand the ways in which systems of AIs and people, working together, can engender cooperative behavior. Through a set of illustrative examples, a broad research agenda for this goal is laid out incorporating aspects of AI architectures, collaborative human-AI systems, economic viewpoints, and human preferences and control.

Authors: Ian Foster (University of Chicago), David Parkes (Harvard University), and Stephan Zheng (Salesforce AI Research)

Simulations are now pervasive throughout human society and the economy, providing decision makers with a remarkable crystal ball—not just for next week’s weather but also for the spread of a disease through a population. This paper lays out the importance of AI-driven simulators, describing challenges, accomplishments, and a potential research agenda in order to realize the full potential of simulation predictions.

Authors: Odest Chadwicke Jenkins (University of Michigan), Daniel Lopresti (Lehigh University), and Melanie Mitchell (Portland State University and Santa Fe Institute)

This paper describes the history and limitations of today’s AI systems, such as brittleness, weakness against adversarial attacks, and the difficulties of system training. It names a series of recommendations and focus areas for research necessary to catalyze  this new wave of AI.

Authors: Suresh Venkatasubramanian (University of Utah), Nadya Bliss (Arizona State University), Helen Nissenbaum (Cornell University), and Melanie Moses (University of New Mexico)

Among the convenience and opportunities that AI brings to the table, these systems also produce a multitude of problems including seemingly racial or gender-biased algorithms, infringements on citizens’ privacy or freedom or deepening inequalities among different groups. This paper calls for an interdisciplinary approach that incorporates the expertise from a broad set of disciplines and application domains to gain a deeper understanding of how technology and society interact in order to avoid these negative impacts from AI technologies.