Robust inference in time series analysis is concerned with developing statistical methods that remain valid under departures from standard model assumptions, such as the presence of heteroskedasticity ...

Cluster-robust inference and estimation methods have emerged as indispensable tools in empirical research, enabling statisticians and economists to draw valid conclusions from data exhibiting ...

Future applications of national importance, such as healthcare, critical infrastructure, transportation systems, and smart cities, are expected to increasingly rely on machine-learning methods, ...

Sub-headline: BIT researchers introduce CausalBridgeQA to tackle spurious correlations in complex multi-hop reasoning chains.

Faculty develop methods for structured and unstructured biomedical data that advance statistical inference, machine learning, causal inference, and algorithmic modeling. Their work delivers principled ...

Edge data centers are a key factor in supporting the evolution of AI applications, but it can be a challenge to accommodate ...