arXiv subjects

Daniel Lopresti

Publications and source records attributed to Daniel Lopresti.

16 recordsLinked to original sources

arXiv2026

The Imperative for Grand Challenges in Computing

Computing is an indispensable component of nearly all technologies and is ubiquitous for vast segments of society. It is also essential to discoveries and innovations in most disciplines. However, while past grand challenges in science have involved computing as one of the tools to address the challenge, these challenges have not been principally about computing. Why has the computing community not yet produced challenges at the scale of grandeur that we see in disciplines such as physics, astronomy, or engineering? How might we go about identifying similarly grand challenges? What are the grand challenges of computing that transcend our discipline's traditional boundaries and have the potential to dramatically improve our understanding of the world and positively shape the future of our society? There is a significant benefit in us, as a field, taking a more intentional approach to "grand challenges." We are seeking challenge problems that are sufficiently compelling as to both ignite the imagination of computer scientists and draw researchers from other disciplines to computational challenges. This paper emphasizes the importance, now more than ever, of defining and pursuing grand challenges in computing as a field, and being intentional about translation and realizing its impacts on science and society. Building on lessons from prior grand challenges, the paper explores the nature of a grand challenge today emphasizing both scale and impact, and how the community may tackle such a grand challenge, given a rapidly changing innovation ecosystem in computing. The paper concludes with a call to action for our community to come together to define grand challenges in computing for the next decade and beyond.

cs.CY↗

arXiv2025

Concerning the Responsible Use of AI in the US Criminal Justice System

Artificial intelligence (AI) is increasingly being adopted in most industries, and for applications such as note taking and checking grammar, there is typically not a cause for concern. However, when constitutional rights are involved, as in the justice system, transparency is paramount. While AI can assist in areas such as risk assessment and forensic evidence generation, its "black box" nature raises significant questions about how decisions are made and whether they can be contested. This paper explores the implications of AI in the justice system, emphasizing the need for transparency in AI decision-making processes to uphold constitutional rights and ensure procedural fairness. The piece advocates for clear explanations of AI's data, logic, and limitations, and calls for periodic audits to address bias and maintain accountability in AI systems.

cs.CY↗

arXiv2025

Enabling the AI Revolution in Healthcare

The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical infrastructure for health data repositories for data sharing, along with mechanisms to enable access to this data for appropriately trained computing researchers.

cs.CY↗

arXiv2025

Lessons for Cybersecurity from the American Public Health System

The United States needs national institutions and frameworks to systematically collect cybersecurity data, measure outcomes, and coordinate responses across government and private sectors, similar to how public health systems track and address disease outbreaks.

cs.CR↗

arXiv2024

Conference Submission and Review Policies to Foster Responsible Computing Research

This report by the CRA Working Group on Socially Responsible Computing outlines guidelines for ethical and responsible research practices in computing conferences. Key areas include avoiding harm, responsible vulnerability disclosure, ethics board review, obtaining consent, accurate reporting, managing financial conflicts of interest, and the use of generative AI. The report emphasizes the need for conference organizers to adopt clear policies to ensure responsible computing research and publication, highlighting the evolving nature of these guidelines as understanding and practices in the field advance.

cs.CY↗

arXiv2021

Pandemic Informatics: Preparation, Robustness, and Resilience; Vaccine Distribution, Logistics, and Prioritization; and Variants of Concern

Infectious diseases cause more than 13 million deaths a year, worldwide. Globalization, urbanization, climate change, and ecological pressures have significantly increased the risk of a global pandemic. The ongoing COVID-19 pandemic-the first since the H1N1 outbreak more than a decade ago and the worst since the 1918 influenza pandemic-illustrates these matters vividly. More than 47M confirmed infections and 1M deaths have been reported worldwide as of November 4, 2020 and the global markets have lost trillions of dollars. The pandemic will continue to have significant disruptive impacts upon the United States and the world for years; its secondary and tertiary impacts might be felt for more than a decade. An effective strategy to reduce the national and global burden of pandemics must: 1) detect timing and location of occurrence, taking into account the many interdependent driving factors; 2) anticipate public reaction to an outbreak, including panic behaviors that obstruct responders and spread contagion; 3) and develop actionable policies that enable targeted and effective responses.

cs.CY↗

arXiv2021

A National Discovery Cloud: Preparing the US for Global Competitiveness in the New Era of 21st Century Digital Transformation

The nature of computation and its role in our lives have been transformed in the past two decades by three remarkable developments: the emergence of public cloud utilities as a new computing platform; the ability to extract information from enormous quantities of data via machine learning; and the emergence of computational simulation as a research method on par with experimental science. Each development has major implications for how societies function and compete; together, they represent a change in technological foundations of society as profound as the telegraph or electrification. Societies that embrace these changes will lead in the 21st Century; those that do not, will decline in prosperity and influence. Nowhere is this stark choice more evident than in research and education, the two sectors that produce the innovations that power the future and prepare a workforce able to exploit those innovations, respectively. In this article, we introduce these developments and suggest steps that the US government might take to prepare the research and education system for its implications.

cs.CY↗

arXiv2021

A National Research Agenda for Intelligent Infrastructure: 2021 Update

Strategic, sustained Federal investments in intelligent infrastructure will increase safety and resilience, improve efficiencies and civic services, and broaden employment opportunities and job growth nationwide. The technologies that comprise intelligent infrastructure can also provide keys to solving some of the most vexing challenges we face today, including confronting future pandemics and natural disasters, achieving sustainability and energy efficiency goals, and advancing social justice. Enabling those technologies effectively will require investment in the associated computing research as well, beyond and in concert with the basic building projects. In 2017, the Computing Community Consortium (CCC) produced a series of intelligent infrastructure whitepapers, and in 2020 CCC issued a set of companion whitepapers on closely related topics. Here we briefly survey those earlier works, and then highlight four themes of rising national prominence where intelligent infrastructure can also play an enabling role, driven by experiences with the COVID-19 pandemic and the social justice movement. We conclude with recommendations for the necessary research investments.

cs.CY↗

arXiv2021

A Research Ecosystem for Secure Computing

Computing devices are vital to all areas of modern life and permeate every aspect of our society. The ubiquity of computing and our reliance on it has been accelerated and amplified by the COVID-19 pandemic. From education to work environments to healthcare to defense to entertainment - it is hard to imagine a segment of modern life that is not touched by computing. The security of computers, systems, and applications has been an active area of research in computer science for decades. However, with the confluence of both the scale of interconnected systems and increased adoption of artificial intelligence, there are many research challenges the community must face so that our society can continue to benefit and risks are minimized, not multiplied. Those challenges range from security and trust of the information ecosystem to adversarial artificial intelligence and machine learning. Along with basic research challenges, more often than not, securing a system happens after the design or even deployment, meaning the security community is routinely playing catch-up and attempting to patch vulnerabilities that could be exploited any minute. While security measures such as encryption and authentication have been widely adopted, questions of security tend to be secondary to application capability. There needs to be a sea-change in the way we approach this critically important aspect of the problem: new incentives and education are at the core of this change. Now is the time to refocus research community efforts on developing interconnected technologies with security "baked in by design" and creating an ecosystem that ensures adoption of promising research developments. To realize this vision, two additional elements of the ecosystem are necessary - proper incentive structures for adoption and an educated citizenry that is well versed in vulnerabilities and risks.

cs.CY↗

arXiv2021

Computing Research Challenges in Next Generation Wireless Networking

By all measures, wireless networking has seen explosive growth over the past decade. Fourth Generation Long Term Evolution (4G LTE) cellular technology has increased the bandwidth available for smartphones, in essence, delivering broadband speeds to mobile devices. The most recent 5G technology is further enhancing the transmission speeds and cell capacity, as well as, reducing latency through the use of different radio technologies and is expected to provide Internet connections that are an order of magnitude faster than 4G LTE. Technology continues to advance rapidly, however, and the next generation, 6G, is already being envisioned. 6G will make possible a wide range of powerful, new applications including holographic telepresence, telehealth, remote education, ubiquitous robotics and autonomous vehicles, smart cities and communities (IoT), and advanced manufacturing (Industry 4.0, sometimes referred to as the Fourth Industrial Revolution), to name but a few. The advances we will see begin at the hardware level and extend all the way to the top of the software "stack." Artificial Intelligence (AI) will also start playing a greater role in the development and management of wireless networking infrastructure by becoming embedded in applications throughout all levels of the network. The resulting benefits to society will be enormous. At the same time these exciting new wireless capabilities are appearing rapidly on the horizon, a broad range of research challenges loom ahead. These stem from the ever-increasing complexity of the hardware and software systems, along with the need to provide infrastructure that is robust and secure while simultaneously protecting the privacy of users. Here we outline some of those challenges and provide recommendations for the research that needs to be done to address them.

cs.CY↗

arXiv2020

Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable

The history of AI has included several "waves" of ideas. The first wave, from the mid-1950s to the 1980s, focused on logic and symbolic hand-encoded representations of knowledge, the foundations of so-called "expert systems". The second wave, starting in the 1990s, focused on statistics and machine learning, in which, instead of hand-programming rules for behavior, programmers constructed "statistical learning algorithms" that could be trained on large datasets. In the most recent wave research in AI has largely focused on deep (i.e., many-layered) neural networks, which are loosely inspired by the brain and trained by "deep learning" methods. However, while deep neural networks have led to many successes and new capabilities in computer vision, speech recognition, language processing, game-playing, and robotics, their potential for broad application remains limited by several factors. A concerning limitation is that even the most successful of today's AI systems suffer from brittleness-they can fail in unexpected ways when faced with situations that differ sufficiently from ones they have been trained on. This lack of robustness also appears in the vulnerability of AI systems to adversarial attacks, in which an adversary can subtly manipulate data in a way to guarantee a specific wrong answer or action from an AI system. AI systems also can absorb biases-based on gender, race, or other factors-from their training data and further magnify these biases in their subsequent decision-making. Taken together, these various limitations have prevented AI systems such as automatic medical diagnosis or autonomous vehicles from being sufficiently trustworthy for wide deployment. The massive proliferation of AI across society will require radically new ideas to yield technology that will not sacrifice our productivity, our quality of life, or our values.

cs.CY↗

arXiv2020

Artificial Intelligence & Cooperation

The rise of Artificial Intelligence (AI) will bring with it an ever-increasing willingness to cede decision-making to machines. But rather than just giving machines the power to make decisions that affect us, we need ways to work cooperatively with AI systems. There is a vital need for research in "AI and Cooperation" that seeks to understand the ways in which systems of AIs and systems of AIs with people can engender cooperative behavior. Trust in AI is also key: trust that is intrinsic and trust that can only be earned over time. Here we use the term "AI" in its broadest sense, as employed by the recent 20-Year Community Roadmap for AI Research (Gil and Selman, 2019), including but certainly not limited to, recent advances in deep learning. With success, cooperation between humans and AIs can build society just as human-human cooperation has. Whether coming from an intrinsic willingness to be helpful, or driven through self-interest, human societies have grown strong and the human species has found success through cooperation. We cooperate "in the small" -- as family units, with neighbors, with co-workers, with strangers -- and "in the large" as a global community that seeks cooperative outcomes around questions of commerce, climate change, and disarmament. Cooperation has evolved in nature also, in cells and among animals. While many cases involving cooperation between humans and AIs will be asymmetric, with the human ultimately in control, AI systems are growing so complex that, even today, it is impossible for the human to fully comprehend their reasoning, recommendations, and actions when functioning simply as passive observers.

cs.CY↗

arXiv2020

Safety, Security, and Privacy Threats Posed by Accelerating Trends in the Internet of Things

The Internet of Things (IoT) is already transforming industries, cities, and homes. The economic value of this transformation across all industries is estimated to be trillions of dollars and the societal impact on energy efficiency, health, and productivity are enormous. Alongside potential benefits of interconnected smart devices comes increased risk and potential for abuse when embedding sensing and intelligence into every device. One of the core problems with the increasing number of IoT devices is the increased complexity that is required to operate them safely and securely. This increased complexity creates new safety, security, privacy, and usability challenges far beyond the difficult challenges individuals face just securing a single device. We highlight some of the negative trends that smart devices and collections of devices cause and we argue that issues related to security, physical safety, privacy, and usability are tightly interconnected and solutions that address all four simultaneously are needed. Tight safety and security standards for individual devices based on existing technology are needed. Likewise research that determines the best way for individuals to confidently manage collections of devices must guide the future deployments of such systems.

cs.CY↗

arXiv2017

Research Agenda in Intelligent Infrastructure to Enhance Disaster Management, Community Resilience and Public Safety

Modern societies can be understood as the intersection of four interdependent systems: (1) the natural environment of geography, climate and weather; (2) the built environment of cities, engineered systems, and physical infrastructure; (3) the social environment of human populations, communities and socio-economic activities; and (4) an information ecosystem that overlays the other three domains and provides the means for understanding, interacting with, and managing the relationships between the natural, built, and human environments. As the nation and its communities become more connected, networked and technologically sophisticated, new challenges and opportunities arise that demand a rethinking of current approaches to public safety and emergency management. Addressing the current and future challenges requires an equally sophisticated program of research, technology development, and strategic planning. The design and integration of intelligent infrastructure-including embedded sensors, the Internet of Things (IoT), advanced wireless information technologies, real-time data capture and analysis, and machine-learning-based decision support-holds the potential to greatly enhance public safety, emergency management, disaster recovery, and overall community resilience, while addressing new and emerging threats to public safety and security. Ultimately, the objective of this program of research and development is to save lives, reduce risk and disaster impacts, permit efficient use of material and social resources, and protect quality of life and economic stability across entire regions.

cs.CY↗

arXiv2016

Systems Computing Challenges in the Internet of Things

A recent McKinsey report estimates the economic impact of the Internet of Things (IoT) to be between $3.9 to $11 trillion dollars by 20251 . IoT has the potential to have a profound impact on our daily lives, including technologies for the home, for health, for transportation, and for managing our natural resources. The Internet was largely driven by information and ideas generated by people, but advances in sensing and hardware have enabled computers to more easily observe the physical world. Coupling this additional layer of information with advances in machine learning brings dramatic new capabilities including the ability to capture and process tremendous amounts of data; to predict behaviors, activities, and the future in uncanny ways; and to manipulate the physical world in response. This trend will fundamentally change how people interact with physical objects and the environment. Success in developing value-added capabilities around IoT requires a broad approach that includes expertise in sensing and hardware, machine learning, networked systems, human-computer interaction, security, and privacy. Strategies for making IoT practical and spurring its ultimate adoption also require a multifaceted approach that often transcends technology, such as with concerns over data security, privacy, public policy, and regulatory issues. In this paper we argue that existing best practices in building robust and secure systems are insufficient to address the new challenges that IoT systems will present. We provide recommendations regarding investments in research areas that will help address inadequacies in existing systems, practices, tools, and policies.

cs.CY↗

arXiv2016

Smart Communities Internet of Things

Today's cities face many challenges due to population growth, aging population, pedestrian and vehicular traffic congestion, water usage increase, increased electricity demands, crumbling physical infrastructure of buildings, roads, water sewage, power grid, and declining health care services. Moreover, major trends indicate the global urbanization of society, and the associated pressures it brings, will continue to accelerate. One of the approaches to assist in solving some of the challenges is to deploy extensive IT technology. It has been recognized that cyber-technology plays a key role in improving quality of people's lives, strengthening business and helping government agencies serve citizens better. In this white paper, we discuss the benefits and challenges of cyber-technologies within "Smart Cities", especially the IoT (Internet of Things) for smart communities, which means considering the benefits and challenges of IoT cyber-technologies on smart cities physical infrastructures and their human stakeholders. To point out the IoT challenges, we will first present the framework within which IoT lives, and then proceed with the challenges, conclusions and recommendations.

cs.CY↗