Twin-Win Model: A human-centered approach to research success

Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research

Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.

Where is your field going? A machine learning approach to study the relative motion of the domains of physics

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multi-dimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields.

Vision for a systems architecture to integrate and transform population health

Entities involved in population health often share a common mission while acting independently of one another and perhaps redundantly. Population health is in everybody's interest, but nobody is really in charge of promoting it. Across governments, corporations, and frontline operations, lack of coordination, lack of resources, and lack of reliable, current information have often impeded the development of situation-awareness models and thus a broad operational integration for population health. These deficiencies may also affect the technical, organizational, policy, and legal arrangements for information sharing, a desired practice of high potential value in population health. In this article, we articulate a vision for a next-generation modeling effort to create a systems architecture for broadly integrating and visualizing strategies for advancing population health. This multipurpose systems architecture would enable different views, alerts, and scenarios to better prepare for and respond to potential degradations in population health. We draw inspiration from systems engineering and visualization tools currently in other uses, including monitoring the state of the economy (market performance), security (classified intelligence), energy (power generation), transportation (global air traffic control), environment (weather monitoring), jobs (labor market dynamics), manufacturing and supply chain (tracking of components, parts, subassemblies, and products), and democratic processes (election analytics). We envision the basic ingredients for a population health systems architecture and its visualization dashboards to eventually support proactive planning and joint action among constituents. We intend our ambitious vision to encourage the work needed for progress that the population deserves.

Modeling research universities: Predicting probable futures of public vs. private and large vs. small research universities

The future of the American academic research enterprise is considered. Data are presented that characterize the resources available for the 160 best-resourced research universities, a small subset of the 2,285 4-year, nonprofit, higher education institutions. A computational model of research universities was extended and used to simulate three strategic scenarios: status quo, steady decline in foreign graduate student enrollments, and downward tuition pressures from high-quality, online professional master's programs. Four specific universities are modeled: large public and private, and small public and private. The former are at the top of the 160 in terms of resources, while the latter are at the bottom of the 160. The model's projections suggest how universities might address these competitive forces. In some situations, it would be in the economic interests of these universities to restrict research activities to avoid the inherent subsidies these activities require. The computational projections portend the need for fundamental change of approaches to business for universities without large institutional resources.