About Me

(Big Sur, Pacific Coast Highway, California)

I am moving to Detroit, MI, to start as an assistant professor at the Center for Molecular Medicine and Genetics. My research focuses on developing novel computational methods for understanding gene regulation and the impact genetic variation can have on gene regulatory networks. These models integrate new experimental assays (ChIP-seq, DNase-seq, FAIRE, and others) together with genome annotations and prior information by using the latest advances in machine learning and statistical signal processing. 

I did my postdoctoral training with Jonathan Pritchard at the Department of Human Genetics at the University of Chicago. In collaboration with colleagues at the University of Chicago, I developed CENTIPEDE a novel probabilistic framework to predict tissue-specific regulatory sites for DNA-binding proteins using DNase-seq footprinting (Pique-Regi et al. 2011 Genome Res). Subsequently, we used DNase-seq to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines (LCLs), for which genome-wide genotypes and estimates of gene expression levels based on RNA-seq are also available. This work presents direct results supporting a molecular mechanism for a large fraction of genetic determinants of gene expression (Degner, Pai, Pique-Regi, et al., 2012 Nature). 

Previously, I obtained my Telecommunications Engineering degree from the Universitat Politecnica de Catalunya, Barcelona, Spain in 2002. I obtained my Ph.D. in Electrical Engineering at the University of Southern California, Los Angeles, CA in 2009 (advisor Antonio Ortega). My Ph.D. work focused on developing new Bioinformatics methods to analyze microarray data using Signal Processing techniques. I collaborated with  Children's Hospital of Los Angeles (co-advisor: Shahab Asgharzadeh) to develop new methods for: i) building a tumor prognosis classifier for neuroblastoma cancer using gene expression microarrays (DLDA and BDLDA), ii) detecting copy number alterations in cancer samples and variation in human populations (GADA).