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Mission

We study and advance the principled foundations of data-driven science.

We accelerate scientific discovery driven by AI and statistical science.

Sampling

statistical sampling, data gathering, active learning, design of experiments, sequential design, reinforcement learning

Modeling

surrogate modeling, multi-fidelity modeling, deep learning, time series modeling

Optimization

Bayesian optimization, non-convex optimization, surrogate-based single and multi-objective optimization

Inverse Problems

likelihood-free parameter estimation, approximate Bayesian computation, learning summary statistics