Applying Human-Centered Design to AI-Enabled Pilot Scheduling

Air Force mission and training scheduling is an immensely complex, time-consuming, and significantly manual process. A scheduling tool known as Puckboard has been developed to help C-17 squadrons transition from moving pucks across large whiteboards to utilizing technology to dynamically plan and deconflict resources in the presence of complex constraints. The overarching goal of incorporating artificial intelligence (AI) into this tool is to empower schedulers to quickly produce more efficient schedules that promote unit readiness, with more pilots completing their training syllabi faster, and with fewer disruptions to missions, training, and aircrew personal life. Our AI efforts focused on refining a neural network approach combining reinforcement learning with linear programming to generate optimal schedules across varying timeframes. The development of this AI-enabled pilot scheduling tool involved applying human-centered design best practices, namely actively involving end-users to inform persona generation, tool functionality, existing and AI-enabled workflows, and wireframe development and iteration

Proceedings of the second biennial Cleveland Neural Engineering Workshop 2013

Abstract The Cleveland Neural Engineering Workshop (NEW) is a biennial meeting started in 2011 as an “unconference” to bring together leaders in the neural engineering and related fields. Since the first iteration of the meeting, NEW has evolved from “just getting together” to a more important purpose of creating, reviewing, and promoting a uniform strategic roadmap for the field. The purpose of this short report, as well as the companion 2015 and 2017 reports, is to provide a historical record of this meeting and the evolution of the roadmap. These reports more importantly establish a baseline for the next meeting to be held in June, 2019. The second Neural Engineering Workshop (NEW) was held in June 2013. The two-day workshop was hosted by the Cleveland Advanced Platform for Technology National Veterans Affairs Center, the Functional Electrical Stimulation National Veterans Affairs Center, and the Case Western Reserve University in Cleveland, Ohio. Participants identified seven areas of future focus in the field of neural engineering: active communications with users, advocacy (regulatory), network building (clinical practice), case studies (clinical and technical), early industrial feedback, value chain resources, engagement, and advocacy (funding). This proceedings document summarizes the meeting outcome

FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research

Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data