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   jmschr < at > cs < dot > washington < dot > edu




I am a fifth year Ph.D. student and IGERT big data fellow in the Computer Science and Engineering department at the University of Washington. My primary research focus is on the application of machine larning methods, primarily deep learning ones, to the massive amount of data being generated in the field of genome science. My research projects have involved predicting the three dimensional structure of the genome using convolutional neural networks and learning a latent representation of the human epigenome using deep tensor factorization. These projects typically involve hundreds of millions to billions of samples, making them markedly "big data" problems. Additionally, I routinely contribute to the Python open source community as the core developer of pomegranate, a package for flexible probabilistic modeling, apricot, a package for data summarization for machine learning, and in the past as a core developer for the scikit-learn project. Future projects include graduating.

Machine Learning | Submodular Selection | Big Data | Computational Biology | Chromatin Architecture | Epigenomics

apricot pomegranate Avocado Rambutan scikit-learn PyPore