Professor Beck focuses on the development of theory and algorithms for stochastic system modeling, uncertainty propagation and Bayesian updating of dynamic systems and networks based on sensor data, treating both modeling and excitation uncertainty. The primary computational tools are advanced stochastic simulation algorithms based on Markov chain Monte Carlo concepts. Some applications of current interest are stochastic predictions of the performance of structural systems under earthquakes, reliability assessment of technological networks, fast automated decision making for mitigation actions based on earthquake early warning systems, earthquake source inversions from seismic sensor networks, damage detection and assessment from structural sensor monitoring networks, Bayesian compressive sensing, and a stochastic mechanics approach to quantum mechanics.