With breakthroughs in scientific experimentation, novel sources of high-quality data are now available for tackling modern scientific and engineering problems. However, the generation of such high-fidelity data often requires costly experiments and/or simulations, which greatly limits the amount of data available. This virtual SampSci 2024 workshop will explore sampling methods (supported by theory and algorithms) that tackle such challenges in a cost-efficient and confident manner.

Sessions will feature talks from a broad range of speakers with diverse backgrounds and career stages, tackling a broad spectrum of scientific applications. Topics of interest include (but are not restricted to):

• Bayesian Optimization

• Quasi Monte Carlo Sampling

• Big Data Subsampling

• Experimental Design

• Bayesian Inference with Expensive Forward Simulators

• Uncertainty Quantification and/or Error Bounds for Sampling

• Multi-Level and Multi-Fidelity Sampling

• Software Development for Scientific Use

If you are interested in giving a talk at SampSci 2024, please fill out the Google form under the "Registration" tab, and we will get back to you promptly!