The Cognitive Systems Engineering Lab’s 25+ researchers are currently working on a plethora of projects uncovering work in natural habitat for use cases from operating rooms to short-range low-altitude air travel. We are focused on providing the most cutting-edge approach to safety, maximizing the benefits. Listed below are some of the ongoing projects in the lab, click on the project link to explore about the project, researchers and the publications from the project. For collaboration on a specific project or future research related to a project please contact the Phd researchers on the project or the faculty.
Keywords and Topics: Human-Machine Teaming, Proactive and Anticipatory Safety, Visual Analytics, Systems Complexity Science, Envisioned Systems, Computational Modeling and Simulation.
Ongoing Projects
NASA-SBIR: Contingency Planning Toolkit for Advanced Air Mobility (AAM)
CSEL in collaboration with Mosaic ATM is working on building a Contingency Planning Toolkit for Advanced Air Mobility. The project will use WMC (Work Models that Compute) a fast time simulator to evaluate candidate architectures to define the requirement for distributed AAM contingency Planning. We will investigate significant task load drivers like cognitive work, coordination, collaboration, and information exchange. We will use CSE (Cognitive Systems Engineering) methods to evaluate envisioned ConOps using the above factors.
NSF CAREER: Making Robots More Cooperative Agents: Controlling Costs of Coordination Through Graph-Based Models of Joint Activity
This research project develops a generalizable formalization for representing and analyzing joint activity in human-robot systems by combining theories from cognitive and social sciences with techniques from graph theory and agent-based modeling. This framework affords objective and dynamic analysis of the teamwork required to manage interdependencies between humans and robots. Based on the model, the research develops techniques for dynamically adapting and controlling coordination costs to improve collaboration and avoid coordination breakdowns.
99P Labs: Modeling Human-AI Interaction for Driving Scenarios
The project with 99P Labs is a two-year effort to develop innovative methods for designing AI assistants. Any assistive technology’s usefulness is determined not only by its intelligent capabilities but also by how well it interacts with human users. In other words, for any technology to be used, its benefits must outweigh the effort required to use it. Specifically, this project aims to develop formal methods for determining interaction designs to create AI assistants to help drivers replan in situ (i.e., while traveling).
Big Brother
Big Brother is a study investigating the ability of a computer analytic to aid in human understanding of a full-motion view (FMV) exploitation scenario. The purpose is to ‘Evaluate Human-Machine teams for Graceful Extensibility.’ which assesses the types and magnitude of differences between human-human and human-machine team interactions in a joint sensemaking task.
COVID-19
OSU Testing: CSEL lab members are supporting the design and implementation of new procedures, tools, and facilities to help Ohio State maintain control over the spread of COVID-19 on campus.
Columbus Bandits: This project investigates the potential for machine algorithms to support public health initiatives. By designing and testing different means of performing COVID-19 testing, CSEL hopes to use technology to quickly and effectively direct health resources to the people that need them most.
IMPActS Workshop
This research project uses an empirically based framework to assess intervention ideas designed at improving adoption of organizational change. IMPActS stands for Ideas (evidence, mechanisms) behind the interventions being proposed, the degree of Model alignment that stakeholders have around the ideas behind that intervention, the perceived and real Pragmatics of the intervention, the availability of the relevant Actors to implement it, and sufficient resources and effort to Sustain it.
Joint Activity Testing (JAT)
CSEL is exploring a new approach to testing and evaluation (T&E) of human-machine systems called joint activity testing (JAT). This methodology focuses on evaluating the performance of the joint human-machine system (i.e., joint activity) as challenge to the system increases. This methodology was designed to facilitate extrapolating insights outside the limits of discrete testing sets in order to better anticipate how complex human-machine systems will respond to scenarios that were not explicitly tested. CSEL has now begun to operationalize JAT in multiple healthcare and intelligence analysis domains spanning multiple projects and continues to further develop the methodology through these experiences.
Systemic Factors Influencing Risk Aversion: Diagnosing Behaviors and Tailoring Interventions for Lasting Transformation
This research seeks to 1) uncover the underlying, largely invisible system pressures on the acquisition workforce in the DoD that influence innovative behaviors, and 2) design interventions that address systemic contributors in order to incentivize lasting behavior changes leading to the kind of cultural change required to meet the National Defense Strategy to block Russia and China and restore America’s competitive edge.
Past Projects
FAA: Reliance on Automated or Complex Flight Deck Systems in Commercial Aircraft
This project’s objective is to develop methods for identifying and evaluating design characteristics of automated or complex system. Complex systems such as commercial aircraft, have many high vulnerabilities that can arise. These vulnerabilities may undermine flight crew performance when non-normal and abnormal events occur. The scope of this project is to provide the Federal Aviation Administration (FAA) with recommendations regarding the evaluation process of flight deck systems.
Strategies Analysis: An Exploratory Study of Contextual Control Modes in Human-Human Teaming
This research project explored how pairs of human teammates adapt their decision-making strategies when working under different time constraints. To investigate this, participants worked together on a simulated search and rescue task using Block Worlds for Teams. Each pair experienced variations of the task with different time pressures, allowing us to analyze how their resulting strategy changes.
Contextual Human-Robot Collaboration in Dynamic and Unstructured Environment
This project’s objective is to develop and validate a method for identifying and supporting tactical and opportunistic collaboration strategies in human-robot systems. Complex operations such as disaster response (e.g., search and rescue tasks with robotic assets), emergency response (e.g., a fire department that sends out UAVs to support firefighters), space operations and, to a lesser degree, manufacturing (e.g., a human-robot system performing a relatively unstructured assembly task) are characterized by time pressure, uncertain demands, and conflicting goals. To enable a range of human control behaviors in human-robot collaboration, integration of human and robot capabilities needs to explicitly support tactical and opportunistic control.
OFRN: Support for Coordination Strategies in Envisioned Urban Air Mobility (UAM) Operations
To enable safe and scalable integration of UAM into the NASA there is a need for human-autonomy teaming that supports ad hoc coordination across a variety of operators, including Pilots in Command (PIC), UAS Service Suppliers (USS), Supplemental Data Service Providers (SDSP), and Air Traffic Management (ATM). Effective human-autonomy teaming becomes particularly critical when disturbances challenge nominal operations. The objective of this project is to characterize coordination strategies for UAM contingency management, specifically the effectiveness and appropriateness of strategies for coordination in relation to contextual factors such as workload, urgency, and common ground for various UTM/UAM roles.
Agility Prime: Rapid Mission Planning For Disaster Response
Accidents and disasters – whether natural disasters, mass casualty incidents, industry accidents, or others – draw on our emergency response systems in unexpected ways. Resiliency and strategies to cope with complexity are paramount to managing effects of these disasters. Drawing on our experiences from a variety of domains, CSEL studies disaster response and consults in disaster planning.
Robot Assisted Surgery
OSU’s CSEL performed some of the first research studies on how RAS was changing the operating environment. Most notably, these projects were the first to quantify the skill and cognitive changes required success between RAS and its predecessor technique, laparoscopy. This pioneering research lead to changes in the way clinicians are trained for participation in RAS procedures as well as the development of new cooperation safety protocols for the protection of staff assisting the surgeon while the robot is in use.
NASA Unmanned Aerial Systems
As NASA-funded research, this project entailed the development of a novel process for the evaluation of the expected performance resiliency of small drones in the domestic airspace. We sought to ensure that small drones become increasingly incorporated into daily aviation activities and their system designed all for their safe operation under the widest possible range of conditions. The product technique, the resiliency trade-space analysis, can be used to help system designs and operator understand how to best deploy their limited resources to greatest effect with regard to the resilience of the system.
Contingency Management in UAS Traffic Management
With the reality of drones being used commercially in large numbers almost certain, the state of Ohio began designing an unmanned air traffic management (UTM) system to ensure that it would stay on the cutting edge of technology. The lab was asked to work as experts in the Human-Machine Teaming space to work on mitigating risk in contingency management situations. In collaboration with multiple of the state’s experts in the field, the Lab worked to make sure that this will be done safely and efficiently in the state.
Clinical Alarm Design
We research and design and clinical alarm systems that reflect the events they signal and support physician and clinician sensemaking.
Patient Monitoring and Decompensation
Through visual analytics and human-machine teaming principles, we leverage advancing technology to improve physicians’ and clinicians’ abilities to monitor their patients.
Complexity Science and Principles of Resilience
As founders and key figures in the evolution of Cognitive Systems Engineering, we have abstracted our research into patterns of complexity and resilience that are applicable across domains to improving performance. Several of these contributions are seminal to the field.
Intelligence Analysis
Although all types of analytic processes, from those in scientific research to those used in legal analysis, purport to encourage high degrees of rigor, little legitimate research has been done to explore what the attributes of rigor might be or what processes are most likely to produce high quality analysis. CSEL’s rigor research was the first to do just that.