Experimental and observational studies generate large amounts
of data. However, without well-designed algorithms, computers
do not analyze the data with sufficient efficiency.
Improving algorithms for data analysis requires finding efficient
ways to produce simple intuitive rules for classifying objects,
work on pre-processing data to reduce measurement errors,
epistemic principles for making inferences based on statistical
data and general background knowledge, and work on methods
for combining expert knowledge with new data to provide insight
into the causal processes behind the data.
Improved data processing is critical in situations with limited
computing power, such as space exploration, as well as in
analysis of complex systems, such as climate modeling or gene