Machine Learning and Genomics Lab 

About us 

We are an interdisciplinary research group affiliated with the Department of Computer Science, the Department of Human Genetics and the Department of Biomathematics at UCLA.

Our lab is broadly interested in questions at the intersection of computer science, statistics and biomedicine. We develop statistical and computational methods to make sense of complex, high-dimensional datasets that are being generated in the fields of genomics and medicine to answer questions ranging from how humans have evolved to what are the biological underpinnings of diseases to how we can improve the diagnosis and treatment of diseases. 

A major focus of our research is in understanding and interpreting our genomes. The  biological questions we are interested in centers around understanding how evolution shapes our genes and how our genes modulate complex traits that include a number of common diseases. To pursue these questions, we develop and extend tools from a diverse set of disciplines including machine learning, algorithms, optimization, high-dimensional statistics, and information theory. We also apply these tools to high-dimensional genomic and medical datasets that are publicly available or being generated by our collaborators.

Some major research themes in our lab are:

  • Population genetic inference:   We have developed methods to learn about mixture among populations and ancestry from genetic variation data. We have used these methods to show that modern humans interbred with archaic humans such as Neanderthals and have shown that the Neanderthal DNA within modern human genomes has impacted human biology. 
  • Understanding how genes affect traits: We aim to understand how our genes map to traits by developing methods that can infer ancestry from genetic data and use this information to localize relevant genes, that can estimate what proportion of variation in a trait is controlled by genetics, that have improved power to detect disease genes, and that can make personalized predictions based on an individual's genome.
  • Machine learning for clinical data:  We are building machine learning algorithms to predict clinically relevant outcomes using electronic medical records from the UCLA Hospitals. A major challenge lies in combining multi-modal datasets that are being collected here at UCLA that include electronic medical records, genomic data, physiological waveforms, and wearable sensors.
  • Machine learning for large-scale genomic data: We are now in a setting where it is feasible to collect genetic data from millions of individuals. How do we effectively harness this information? We are building statistical models that are biologically realistic coupled with inference algorithms that can scale to these massive datasets.
  • Genomic privacy: A major challenge in analyzing personal genomic data is the risk of breaching the individual's privacy. We are interested in understanding how we can analyze genomic data while protecting privacy.

Openings

We are looking for post-docs and Ph.D. students with strong computional and statisical backgrounds. Ph.D. students can join our lab through the Computer ScienceBioinformatics, or Genetics and Genomics graduate programs.  Contact us if you are interested in any of these  opportunities

News

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    Our lab meets at the American Society of Human Genetics in Houston, TX and we got some awards back home! News and More Pictures
     
     
    October 2019
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    Arun passed his qualifying exam! He is now Qualified™.
    August 2019
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    Chris was named a Human Genetics Scholar for 2019 by the American Society of Human Genetics!
    August 2019
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    Saurav Mathur and Tiffany Phan present their work from Bruins in Genomics 2019 under the mentorship of Rob Brown!
     
     
    August 2019
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    Boyang Fu joined the lab! Welcome Boyang!
    August 2019
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    Ariel and Ali both gave talks at RECOMB in Washington DC!
     
     
    May 2019
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    Chris presented a poster and Ruthie gave a talk at Biology of Genomes!
     
     
    May 2019
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    Two papers from our lab have been accepted for talks at RECOMB 2019. Congrats to Ariel, Ali, and our summer students, Mohammed and Anna!
    December 2018
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    Ruthie gives a talk "A scalable Bayesian model for estimating the genetic architecture of complex traits using summary statistics from GWAS"
    at the Probabilistic Modeling in Genomics 2018 conference at CSHL.

    Ariel presents her poster on "Scalable estimation of heritability and genetic correlation for biobank-scale data" at ProbGen 2018.  

     
    November 2018
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    Chris presents his work on the functional impact of human-specific sites  in the Natural Selection and Human Phenotypes session at ASHG 2018!
     
     
    October 2018
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    Alec presents his poster on Binomial probabilistic component analysis for genotype data at ASHG 2018 in San Diego!

     

     
     

     

    October 2018
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    Rob presents his poster on "Enrichment of deleterious aviation on down-regulated haplotypes due to the regulation-dependent penetrance of coding variants" at ASHG 2018 in San Diego!

     

     
     

     

    October 2018
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    Chris gives a talk on "Quantifying the impact of Neanderthal gene flow on human phenotypes at the Fitch symposium at SMBE in Yokohama Japan. He was also a finalist for the SMBE Fitch Prize! 
    July 2018
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    Ariel give a talk: "A scalable estimator of SNP heritability for Biobank-scale data" at ISMB in Chicago!

     
     

     

    July 2018
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    Ruthie gives a talk on the UNITY framework at the Intelligent Systems for Molecular Biology annual conference in Chicago!
     
     
    July 2018
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    Ariel gives a talk:   " A unifying framework for summary statistic imputation " at the Annual Research in Computational Molecular Biology Conference (RECOMB) in Paris!

     

    April 2018
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    Arun receives the NSF GRFP!

    April 2018
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    Congratulations to Ruthie for winning the UCLA Dean’s Prize for Excellence at the undergraduate Research Poster Day 2017!
     
     
    June 2017
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    Chris gives talk at the American Society of Human Genetics meeting in Orlando!

     
     
    June 2017
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    Quanta Magazine had a nice article on archaic introgression that covers some of our work.

     
     
    May 2016
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    Paper from our lab on "The Combined Landscape of Denisovan and Neanderthal Ancestry in Present-Day Humans" published in Current Biology.

    Press coverage:

     

     

    March 2016