Dan Brown

Professor Dan Brown's primary research area is the understanding of sequential data, joining ideas from evolutionary theory with probabilistic modeling and discrete mathematical ideas. His established expertise is in biological sequence analysis, where he has worked on a host of areas, ranging from homology search to algorithms for hidden Markov models to haplotype inference and kinship discovery.

Dan is also interested in why algorithms in bioinformatics tend to work faster in practice than might be predicted in theory, and has solved this question for problems in motif finding, homology search, haplotype inference and kinship detection.

Dan's other major research area is music information retrieval. He has adapted algorithms from bioinformatics to the study of lyric and audio analysis, with notable successes in rhyme detection in hip hop and musicological applications of this technique, misheard lyric disambiguation, and cover song detection. He is also working with a current student on ways to add lyric-related features to a variety of problems in music information retrieval.