Structural Variant Detection from RNA-seq
Assessing the variability of TADs
Order Min Hash for fast, approximate edit distance
We are interested in designing algorithms to extract insight from biological data. We currently focus on the following classes of problems:
Genomics & genome assembly: RNA-seq expression quantification; genome assembly; large-scale sequence search, etc. This work is currently supported by a Data-Driven Invesgator grant from the Gordon and Betty Moore Foundation and NIH grant R01GM122935. It was previously supported by NIH grant 1R21HG006913, NSF grant CCF-1319998, and an award from The Shurl and Kay Curci foundation.
Chromatin structure and function: Algorithms for determining the spatial organization of eukaryotic genomes from Chromosome Conformation Capture data. Previously supported by NIH grant R01HG007104.
Automatically learning algorithms: Hyperparameter optimization, autoML, and automated algorithm design. Supported by an award from Schmidt Sciences.
Previous research interests include:
Viral evolution: Reassortment in the influenza genome. This work was supported by NIH grant 1R21AI085376.
Protein interactions and networks: Evolution of interactions; protein function prediction; clustering within networks; protein structure prediction. This work was supported by NSF grant EF-0849899 and by NSF grant CCF-1053918/CCF-1256087 (CAREER award).
Ph.D. in Computer Science, 2005