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Knowles Lab

The Knowles Lab will be opening in January 2019 at the New York Genome Center, jointly with Columbia University. The lab develops and applies statistical machine learning methods for functional genomics, particularly to understand the role of transcriptomic dysregulation in human genetic disease. This includes characterization of the genetic and environmental factors contributing to mRNA expression and splicing variation. The lab works with diverse research groups at the NYGC and beyond in collecting large-scale genomics datasets in the context of neurological disease and developing novel genomic technologies, including single cell methods, forward genetic screens and long-read transcriptomics.
 

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    David A. Knowles, PhD

    David A. Knowles, PhD, is a Core Faculty Member at the New York Genome Center. He holds a joint appointment as Assistant Professor in the Department of Computer Science at Columbia University.

    Dr. Knowles received a MEng from the University of Cambridge and a MSc in Bioinformatics and Systems Biology from Imperial College London. He obtained a PhD in the Machine Learning Group of the Cambridge Engineering Department under the mentorship of Dr. Zoubin Ghahramani, funded by Microsoft Research. He has developed Bayesian nonparametric approaches to factor analysis, network modeling, and hierarchical clustering. He additionally extended variational inference methods, computationally-attractive alternatives to Markov chain Monte Carlo, to a broader class of probabilistic models.

    Prior to joining NYGC, he was a postdoctoral fellow at Stanford University, under the mentorship of Drs. Sylvia Plevritis (Center for Computational Systems Biology/Radiology), Jonathan Pritchard (Genetics), and Daphne Koller (Computer Science). During his postdoctoral training, he applied his statistical machine learning background to the task of delineating the interacting effects of genetic and environmental factors on gene expression and RNA splicing.

    daknowles@nygenome.org | @david_a_knowles

This work was partially supported by a gift from the Simons Foundation.