Gregory Darnell

PhD Student at Princeton


I study machine learning and statistical genetics in the Quantitative and Computational Biology Ph.D program at Princeton University, where I am advised by Barbara Engelhardt (Princeton) and co-advised by Sayan Mukherjee (Duke).

I have worked in research labs at Microsoft Research New England (with Jennifer Listgarten and Nicolo Fusi), Rockefeller University (with Robert Darnell and Chaolin Zhang), and Harvard School of Public Health (with Alkes Price). At UCLA, my research mentor was Eleazar Eskin.



Princeton University 2014-

PhD Candidate - Quantitative and Computational Biology

UCLA 2008-2013

B.S. Computer Science, minor Bioinformatics

Curriculum vitae



Adaptive Randomized Dimension Reduction on Massive Data

Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt. "Adaptive Randomized Dimension Reduction on Massive Data." Journal of Machine Learning Research (JMLR). 18(140):1−30, 2017.

Bayesian Test for Heteroskedasticity

A Bayesian test to identify variance effects

Bianca Dumitrascu, Gregory Darnell, Julien Aroyles, Barbara E Engelhardt. "A Bayesian test to identify variance effects." arXiv preprint arXiv:1512.01616 (2015).

Power Association

Incorporating Prior Information into Association Studies

Gregory Darnell, Dat Duong, Buhm Han, Eleazar Eskin. "Incorporating Prior Information into Association Studies." Bioinformatics. 28(12):i47-53, Special Issue of the Proceedings of the Nineteenth International Conference on Intelligent Systems in Molecular Biology (ISMB-2012) Long Beach, CA: July 15-27, 2012.


gdarnell [at] princeton [dot] edu