Johann Schumann

About

Johann Schumann is a leading researcher at the intersection of artificial intelligence, high-assurance systems, and aerospace engineering. His work focuses on ensuring the safety and reliability of autonomous and cyber-physical systems, particularly in mission-critical aerospace applications. Schumann’s major contributions include pioneering runtime system health management (SHM) with the R2U2 framework—a realizable, responsive, and unobtrusive unit that monitors hardware and software in real time, deployable on FPGAs or in software. He also advanced the use of Bayesian approaches for performance monitoring of neuro-adaptive controllers in aircraft and UAVs, addressing the challenge of controlling nonlinear systems safely. His survey on neural networks in high-assurance systems (26 citations) remains a foundational reference, while his recent work explores requirements for learning-enabled software. Schumann’s research has direct impact on autonomous robotics missions and collision avoidance systems, with his R2U2 tool overview (19 citations) and statistical failure prediction studies demonstrating his influence. His achievements highlight a career dedicated to bridging AI innovation with rigorous safety engineering for aerospace and robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Application of Neural Networks in High Assurance Systems: A Survey

   26 citations · 2010
2. 2
   R2U2: Tool Overview

   19 citations · 2018
3. 3
   Performance Monitoring and Assessment of Neuro-Adaptive Controllers for Aerospace Applications Using a Bayesian Approach

   12 citations · 2005
4. 4
   Exploring Requirements for Software that Learns: A Research Preview

   6 citations · 2023
5. 5
   Software and System Health Management for Autonomous Robotics Missions

   4 citations · 2018
6. 6
   Statistical Prediction of Failures in Aircraft Collision Avoidance Systems

   3 citations · 2019

Key Collaborators

Contact & Links