It is clear that high-throughput techniques, such as rapid DNA sequencing, gene chips, and high-throughput genetic screening are rapidly changing the science of genetics. Hypothesis-driven science is now strongly complemented by these newer data-driven approaches. This flood of data brings numerous informatic challenges in both experimental analysis and design. Computer science will play a crucial role in meeting these challenges. My research makes use of machine learning and other computational methods to perform central tasks in high-throughput biology. These tasks include gene-chip design, detection of genomic variation, and the interpretation of gene-expression patterns.