Battlefield Health Care

Using cutting edge research and technologies, CMU develops techniques and systems intended to rapidly triage and stabilize injuries sustained in high-risk areas, particularly the battlefield or during military operations. Automated diagnosis and detection of injuries can quickly identify problems, with the eventual goal of automatic medical kits to stabilize soldiers and victims of disasters until qualified help can arrive.

Projects

  • TRACIR
    - Robotic assessment and stabilization of soldiers in critical condition.
Battlefield Health Care

Health Care

Health Care

We use machine learning tools to build various types of practical models of data. This can range from predictive models that aim to identify some interesting aspect of patient data (e.g. is a monitor alert real or artifact, are there signs of disease or not), explanatory models (e.g. what differentiates one cohort from another, or one state from another), forecasting and trending models (e.g. what is going to happen in the future, will a patient become unstable), and grouping (or clustering) entities (e.g. these patients are similar to those ones).

Projects

Outbreak Detection

Hospital acquired infections are a significant yet preventable detractor of patient care. The Auton lab develops statistical models for joining disparate sources of information such as genetic tests, patient histories, geography, and other epidemiological information for detecting systematic outbreaks and identifying root cause. Leveraging multiple data sources, our algorithms establish corroborating evidence to support or dismiss hypothetical outbreak scenarios, both increasing detectability and speed of analysis while maintaining low false alert rates.

Projects

  • EDS-HAT
    - Tracing the source of outbreaks in hospitalized patients.
Outbreak Detection

Predictive Maintenance

Predictive Maintenance

The Auton Lab has over 15 years of experience applying machine learning to maintenance of complex aerospace and automotive assets. Our work focuses on reducing risks of unforeseen issues, reducing false positives in fault detection systems, and forecasting future failures.

Projects

Radiation Safety

We develop algorithms for both detection and decision support in nuclear threat identification. Using our flagship Bayesian Aggregation method for source detection and characterization we are developing fast and efficient tools for situational awareness and safety applications. Our work focuses on robust methods, multi-sensor and multi-modal data fusion, and decision support infrastructure for rapidly processing alerts.

Projects

  • ERNIE
    - Detection and classification of radiation from vehicles entering the US.
Radiation Safety

Usability

Usability

The Auton Lab is dedicated to helping organizations and individuals make use of machine learning and automation principles to better ourselves and the lives of everyone. We create software and guidelines that makes it easier for anyone to perform complex data analysis, and to refine techniques to better use the data available to them.

Projects

  • AutonML
    - Automated machine learning system.