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Zikry Lab

 

University of North Carolina at Chapel Hill

Patient tumor sample multiplex imaged for cell cycle markers.

Imaging credit: Samuel C. Wolff

 

Data Science + 
Precision Medicine

 
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Group Members

Our lab works on computational and statistical methods across biomedical sciences, from single-cell cancer biology to neuroscience

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Postdoctoral Research
(Data Science)

Ian Thomas

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PhD Student
(Bioinformatics & Computational Biology)

Alice Zhang

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PhD Student
(Statistics & Operations Research)

Kai Wycik

About Me

 
 
 

Tarek Zikry, Ph.D.

 

Appointments
Assistant Professor (deferred start July 2025),
UNC-Chapel Hill

  • Division of Data Science and Society, School of Data and Information Sciences (Primary)

  • Curriculum in Bioinformatics & Computational Biology (Core Faculty)

  • Lineberger Comprehensive Cancer Center
     

Experience

I completed my Ph.D. in Biostatistics at UNC-Chapel Hill with the joint mentorship of Michael Kosorok in the Department of Biostatistics and Jeremy Purvis in the Department of Genetics. I accepted an assistant professorship at the UNC-Chapel Hill School of Data Science and Society with a deferred start of one year, in which I am a postdoctoral research scientist with Genevera Allen at Columbia University in the Department of Statistics, Center for Theoretical Neurosicence at the Zuckerman Institute, and Irving Institute for Cancer Dynamics.
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Research Areas

I work in the application of these methods to problems in computational biology, cancer, neuroscience, biomedicine, and public health.

See my Google Scholar to check out papers on these topics.

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Dynamic Treatment Regimes

Using reinforcement learning-based models to leverage patient heterogeneity and find optimal sequences of care. 

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Statistical Machine Learning

Developing and applying methods to learn from data and make accurate predictions—while maintaining key principles of stability, predictability, and computability.

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Single-Cell Cancer Omics

Computational medicine methods to utilize high dimensional omics data to find key biomarkers and inform cancer treatments.

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Dimensionality Reduction

Manifold learning methods to understand latent trends and interpret high dimensional biological data in a statistically valid manner. 

Get in touch

I am seeking motivated students in SDSS, STOR, and BCB to join the lab starting in 2025-26. Contact me at tarek@unc.edu!

Location

UNC-Chapel Hill
School of Data Science and Society

Connect

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  • LinkedIn
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