Dr. Horvath is an Assistant Professor in Human Genetics and Biostatistics at UCLA. His expertise lies in bioinformatics and statistical genetics. He directs the Array Data Analysis Group at UCLA. His research interests include: a) allelic association tests for mapping complex disease genes based on genetic markers, b) data mining methods for DNA and tissue microarray data. He has published extensively on allelic association tests, e.g., he has worked on family-based allelic association tests that do not require parental information but instead use sibling controls. He has developed the sibship disequilibrium test (SDT) and is one of the authors of the widely used software package FBAT. Lately, he has extended these methods to haplotype data. Steve Horvath's research in data mining methods focuses on supervised and unsupervised methods for analyzing array data. Lately, he is investigating methods that are at the intersection of genetic marker and gene expression data, e.g., he has studied family-based tests for correlation between gene expression and trait values and is working on adapting random forest predictors to SNP and microarray data. Further he develops methods for constructing and validating gene co-expression networks.