John Erickson

Abraham Sanders, Debjani Ray-Majumder, John S. Erickson et al.

The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a predictive method which can be used to explore the potential reception of such messages. Specifically, we harness a generative model (GPT-2) to directly predict probable future responses and demonstrate how it can be used to optimize expected reception of important health guidance. Finally, we introduce a novel evaluation scheme with extensive statistical testing which allows us to conclude that our models capture the semantics and sentiment found in actual public health responses.

Andrés Vargas, MD Ridwan Al Iqbal, John S. Erickson et al.

We present a new "grey-box" approach to anomaly detection in smart manufacturing. The approach is designed for tools run by control systems which execute recipe steps to produce semiconductor wafers. Multiple streaming sensors capture trace data to guide the control systems and for quality control. These control systems are typically PI controllers which can be modeled as an ordinary differential equation (ODE) coupled with a control equation, capturing the physics of the process. The ODE "white-box" models capture physical causal relationships that can be used in simulations to determine how the process will react to changes in control parameters, but they have limited utility for anomaly detection. Many "black-box" approaches exist for anomaly detection in manufacturing, but they typically do not exploit the underlying process control. The proposed "grey-box" approach uses the process-control ODE model to derive a parametric function of sensor data. Bayesian regression is used to fit the parameters of these functions to form characteristic "shape signatures". The probabilistic model provides a natural anomaly score for each wafer, which captures poor control and strange shape signatures. The anomaly score can be deconstructed into its constituent parts in order to identify which parameters are contributing to anomalies. We demonstrate how the anomaly score can be used to monitor complex multi-step manufacturing processes to detect anomalies and changes and show how the shape signatures can provide insight into the underlying sources of process variation that are not readily apparent in the sensor data.

John Erickson, Abhinav Aggarwal, G. Matthew Fricke et al.

Measurement of volcanic CO2 flux by a drone swarm poses special challenges. Drones must be able to follow gas concentration gradients while tolerating frequent drone loss. We present the LoCUS algorithm as a solution to this problem and prove its robustness. LoCUS relies on swarm coordination and self-healing to solve the task. As a point of contrast we also implement the MoBS algorithm, derived from previously published work, which allows drones to solve the task independently. We compare the effectiveness of these algorithms using drone simulations, and find that LoCUS provides a reliable and efficient solution to the volcano survey problem. Further, the novel data-structures and algorithms underpinning LoCUS have application in other areas of fault-tolerant algorithm research.

Alexander New, Sabbir M. Rashid, John S. Erickson et al.

One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision health. We develop a combined semantic and machine learning system that uses a health risk ontology and knowledge graph (KG) to dynamically discover risk factors and their associated subpopulations. Semantics and the novel supervised cadre model make our system explainable. Future population health studies are easily performed and documented with provenance by specifying additional input and output KG cartridges.