Current Students


Antoine Barthelet (Yu)

Application of machine learning algorithms to identify the important components of protein-protein interactions and their networks.

Juan Beltran 1st Year - currently rotating
Ian Caldas 1st Year - currently rotating
Nicholas Cheney (Lipson)

Evolution of artificial agents and analysis of their brains in order to answer questions about cognition.

Tinyi Chu (Danko) Developing Bioinformatics tools and addressing biological questions using state of-art machine learning and statistical learning algorithms.

Melissa Hubisz

Using ancestral recombination graphs for inference in population genetics, especially demographic reconstruction of human populations.
Charles Liang
Discovering trends and patterns in biological datasets, such as protein network disruption datasets and drug-drug interaction datasets, with statistical methods and machine learning tools.
Manisha Munasinghe (Clark)  

Paul Munn

Interested in both developing machine learning techniques to aid in the identification of transcription unit boundaries, and in improving our understanding of chromatin structure and how it affects gene regulation.
Nathan Oakes (Messer)

Characterizing the dynamics of rapid evolution in complex demographics.

Ying Qiao (TBD) Developing statistical and computational methods to understand the relationship of DNA replication timing and mutational landscape of the genome.

Afrah Shafquat (Mezey)

Developing statistical and computational methods to identify biomarkers associated with complex phenotypes.
Shayne Wierbowski 1st Year - currently rotating
Lenore Pipes (Siepel/
The Non-Human Primate Reference Transcriptome Resource Project.
Nathaniel Tippens (Yu/Lis)

Developing an experimental technique to map all protein-DNA interactions in accessible regions of the genome. I am interested in applying this and other genomic techniques to study the transcriptional mechanisms that specify cellular identity.

Yiping Wang (Gu) Developing a new constraint-based computational method called FALCON, to predict metabolic flux distributions using gene expression.