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