PI: Dan Schrider


I am an Assistant Professor in the Department of Genetics at UNC-Chapel Hill. I recently completed a postdoc with Andrew Kern at Rutgers University, where we worked on detecting the footprints of natural selection in the genome. Prior to that I did my PhD with Dr. Matthew Hahn at Indiana University, studying genomic copy number variation. I also received my undergraduate training in Computer Science at Indiana University.

Email: drs [at] unc [dot] edu

Office: Genetic Medicine Building, Room 5111

Here is my CV.

Left: me. Right: Andy Kern's awesome sawfish rostrum.

Left: me. Right: Andy Kern's awesome sawfish rostrum.


Anton Suvorov

Anton is working on applying machine learning techniques to problems in phylogenetics and population genetics. Anton joined the lab in 2018 after completing his PhD with Seth Bybee at Brigham Young University. Prior to that he worked with Christian Schlötterer Andreas Futschik at the University of Vienna after receiving his undergraduate and master's training at Moscow State University.

Email: antony [dot] suvorov [at] gmail [dot] com

Ariella Gladstein

Ariella is working on applying machine learning techniques to problems in population genetics. Ariella joined the lab in 2018 after completing her PhD with Michael Hammer at the University of Arizona, where she worked on inferring human demographic history from whole chromosomes and SNP array data with Approximate Bayesian Computation. She received her undergraduate training in Mathematical Biology and Russian at Beloit College. 

Email: aglad [at] med [dot] unc [dot] edu

GitHub: https://github.com/agladstein

Other Member(s)

Louie Schrider (Dog)

The Schrider Lab is still looking to grow. In the mean time we are filling out the website with a picture of my dog. We could count him as a real member—while he doesn't really do anything that directly helps our research, he does boost morale—but we are looking for real human members, so please contact me if interested!

Computer Programmer

We are hiring a computer programmer. The successful candidate will work closely with the PI to develop programs applying statistical and machine learning techniques (including Deep Learning) to genomic data. Other responsibilities include maintaining existing applications, and applying these applications to both simulated and real data sets. For more information, and to apply, see the posting here. But informal inquiries are welcome so don't hesitate to get in touch if you are interested and have any questions.

Current Openings(s)

Left: Cali. Right: Anton

Foreground: Ariella. Background: vegetation

A dog