Ariella Gladstein

Ariella Gladstein

Postdoctoral Researcher

University of North Carolina, Chapel Hill

About me

I am a postdoctoral researcher in Genetics with Daniel Schrider. I am interested in using genomic data to learn about evolutionary history, and have focused my work on recent demographic history of populations. I enjoy tackling large computational problems in order to answer biological questions.

When I’m not working in the lab, I like to play with my two dogs, and train in circus arts and figure skating.

Interests

  • Population Genomics
  • Computational Biology
  • Machine Learning

Education

  • PhD in Ecology and Evolutionary Biology, 2018

    University of Arizona

  • BS in Mathematical Biology and Russian, 2011

    Beloit College

Skills

Python

Command line

Git

HPC/HTC

Cloud Computing

Containers

Genomics

Data Science

Experience

 
 
 
 
 

Postdoctoral Researcher

University of North Carolina, Chapel Hill

Aug 2018 – Present Chapel Hill, NC
Project: Using Deep Learning to infer demographic history from genomic data
 
 
 
 
 

Graduate Student

University of Arizona

Aug 2011 – Jun 2018 Tucson, AZ
Dissertation: Inference of recent demographic history of population isolates using genome-wide high density SNP arrays and whole genome sequences
 
 
 
 
 

Exchange Student

Moscow State University

Sep 2009 – Dec 2009 Moscow, Russia
Department of Genetics
 
 
 
 
 

Lab Technician Intern

Russian Academy of Medical Sciences

Jan 2009 – Dec 2009 Moscow, Russia
Laboratory of Population Genetics
 
 
 
 
 

Undergraduate Student

Beloit College

Aug 2007 – May 2011 Beloit, WI
Double major in Mathematical Biology and Russian

Recent Publications

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A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on …

Talks & Posters

Demographic Model Selection with Deep Learning

Substructured population growth in the Ashkenazi Jews inferred with Approximate Bayesian Computation

Substructured population growth in the Ashkenazi Jews inferred with Approximate Bayesian Computation

Inference of Evolutionary History with Approximate Bayesian Computation

SimPrily: A Python framework to simplify genome simulation with priors

SimPrily: A Python framework to simplify genome simulation with priors

Projects

Student soars with research computing resources

Supporting UA Researchers with Computing Resources

Pegasus helped Ecology and Evolutionary Biology graduate student at the University of Arizona shed light on human population history

Over 1 Million Jobs Have Run on UA’s Ocelote Supercomputer

Contact