We are a computational biology lab within the Department of Biological Chemistry in the David Geffen School of Medicine and also part of the Computer Science Department, the Interdepartmental Bioinformatics Program, the Institute for Quantitative and Computational Biosciences, the Broad Stem Cell Research Center, the Jonsson Comprehensive Cancer Center, and the Molecular Biology Institute at UCLA.

The focus of the lab is on developing and applying machine learning methods for the analysis of high-throughput experimental data to address problems in epigenomics and gene regulation. Our research is often conducted in close collaboration with experimental groups. Please see the publications page to learn more about the lab's research.

We are recruiting new members, if interested please see the positions page.

Recent News

  • November 2017: Ernst et al, Nature Biotech 2016 is selected to the annual regulatory and systems genomics top 10 papers reading list.
  • November 2017: Nature Protocols publishes a protocols paper on ChromHMM.
  • October 2017: Shan is selected to give an oral presentation at CSHL Single Cell Analyses meeting and Artur at the NIPS Machine Learning in Computational Biology Workshop.
  • October 2017: Jason will be co-teaching COM SCI M225/BIOINFO M225/HUM GEN M265 'Computational Methods in Genomics' with Bogdan Pasaniuc in Winter 2018.
  • October 2017: A new set of enhancer-gene link predictions extending the expression correlation approach in Ernst et al, 2011 to the Roadmap Epigenomics data is published as a part of this paper.

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    Upcoming Presentations

  • December 9, 2017: NIPS Workshop on Machine Learning in Computational Biology - Artur Presents