Data Driven Inference and Control

As infrastructural systems rapidly grow in scale, complexity and interconnectedness, legacy operations driving the systems have shown to be limited in their ability to handle large-scale disasters that can occur either through natural extreme events, or through carefully orchestrated attacks, and in particular cyber-attacks. Advances in sensing and communication technologies allow diverse data collection at massive scale, granting unprecedented visibility into system operations and provide enormous potential to overhaul the operational paradigms towards increased safety, security and sustainability. It is towards this end that we are conducting  data-intensive data-driven research  for inference and control in large scale cyber physical infrastructural networks.

LaRosa, N., Farber, J., Venkitasubramaniam, P., Blum, R., & Al Rashdan, A. (2022). Separating Sensor Anomalies From Process Anomalies in Data-Driven Anomaly Detection. IEEE Signal Processing Letters29, 1704-1708.

Yao, Ruigen, et al. “Iterative spatial compressive sensing strategy for structural damage diagnosis as a BIG DATA problem.” Dynamics of Civil Structures, Volume 2: Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015. Springer International Publishing, 2015.

Yao, Ruigen, Shamim N. Pakzad, and Parvathinathan Venkitasubramaniam. “Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics.” Structural Control and Health Monitoring 24, no. 4 (2017): e1881.

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