ATD: This project develops “novel causality-guided approaches for reliable threat detection and forecasting in complex event streams. This project opens new lines of research, expanding the domain and scope of algorithmic threat detection.

Specifically, it focuses on three key research topics:
(1) Causal inference for observed event streams with latent confounders and nonstationarity,
(2) Causal representation learning for latent event streams, and
(3) Causal anomaly detection and long-term forecasting.

Leveraging the Hawkes process model, the investigators will establish a formal framework to determine when and how causal links can be inferred from partially observed and potentially non-stationary event sequences. The identified causal relationships will enable comprehensive situational awareness while pinpointing anomalies and providing long-term forecasts. The mathematical theory, algorithms, and software produced through this research will be transformational. This project aims to establish a foundational understanding of causality for algorithmic threat detection, provide principled algorithms for analyzing complex event streams, and broaden the application of these methods to diverse social and scientific domains.”