David Arthur Knowles

Computer Science

David Knowles uses machine learning approaches—probabilistic graphical models, deep learning and convex optimization—to address challenges in understanding large genomic datasets.

  • Core Faculty Member, New York Genome Center, 2019 -
  • Assistant Professor, Computer Science, Columbia University, 2019 -
  • Interdisciplinary Appointee, Systems Biology, Columbia University, 2019 -
  • Affiliate Member, Data Science Institute, Columbia University, 2019 -
  • Brielin C. Brown and David A. Knowles. “Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization”. bioRxiv (2020)
  • Andrew Stirn, Tony Jebara, and David A. Knowles. “A New Distribution on the Simplex with Auto-Encoding Applications”. Advances in Neural Information Processing Systems. 2019.
  • David A Knowles, Joe R Davis, Hilary Edgington, Anil Raj, Marie-Julie Favé, Xiaowei Zhu, James B Potash, Myrna M Weissman, Jianxin Shi, Doug Levinson, Philip Awadalla, Sara Mostafavi, Stephen B Montgomery, and Alexis Battle. “Allele-specific expression reveals interactions between genetic variation and environment”. Nature Methods (2017)
  • David A Knowles*, Courtney K Burrows*, John D Blischak, Kristen M Patterson, Daniel J Serie, Nadine Norton, Carole Ober, Jonathan K Pritchard, and Yoav Gilad. “Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes”. eLife (2018)
  • Yang I. Li*, David A. Knowles*, Jack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, and Jonathan K. Pritchard. “Annotation-free quantification of RNA splicing using LeafCutter”. Nature Genetics (2017)
  • Tim Salimans and David A. Knowles. “Fixed-form variational posterior approximation throughstochastic linear regression”. Bayesian Analysis (2013).