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Jacob Schreiber

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 as characterized by the Roadmap consortium using deep tensor factorization. Additionally, I routinely contribute to the Python open source community as the core developer of the pomegranate package for flexible probabilistic modeling and in the past as a developer for the scikit-learn project. Future projects include graduating.

Machine Learning | Deep Learning | Big Data | Computational Biology | Chromatin Architecture | Epigenomics