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 Investigator 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).
Disclosure: I am co-founder of Ocean Genomics, Inc.
Ph.D. in Computer Science, 2005