Bio

I am a post-doctoral researcher at Stanford University. Previously, I was a graduate student and IGERT big data fellow at the University of Washington. My research involves analyzing massive amounts of genomics data to better understand how cells work. Recently, my work has involved predicting the three dimensional structure of the genome at high resolution given simple, cheap, data sources, and investigating a deep tensor factorization approach to impute missing epigenetics experiments for the human genome. As a separate track of research, I've also recently worked on techniques for merging expert knowledge with data to efficiently learn the structure of Bayesian networks, sometimes even making the problem tractable for large problems. When not doing research, I actively contribute to the open source community. I was a core developer of the popular scikit-learn machine learning package for python, where I focused on maintaining the tree based models/ensembles and probabilistic models. I have also written pomegranate, a Python package that extends scikit-learn to flexible probabilistic modeling and some probabilistic graphical models, and apricot, a Python package that implements submodular optimization for machine learning. GitHub: https://github.com/jmschrei Website: https://jmschrei.github.io/