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Maxwell Libbrecht

Associate Professor
Computing Science

Areas of interest

Research Interests:
machine learning
probabilistic modeling
unsupervised learning
submodular optimization
genomics
gene regulation

Teaching Interests:
machine learning
probability and statistics
data structures and algorithms
discrete math

Education

  • PhD, Computer Science, University of Washington, 2016
  • BSc, Computer Science, Stanford University, 2011

Courses

This instructor is currently not teaching any courses.

Selected Recent Publications

  • Maxwell W. Libbrecht, William S. Noble. Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16: 321-332, 2015. http://dx.doi.org/10.1038/nrg3920
  •  Maxwell W. Libbrecht, Ferhat Ay, Michael M. Hoffman, David M. Gilbert, Jeffrey A. Bilmes, and William S. Noble. Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression. Genome Research, 25: 544-557, 2015. http://doi.org/10.1101/gr.184341.114
  • Maxwell W. Libbrecht, Michael M. Hoffman, Jeffrey A. Bilmes, William S. Noble. Entropic graph-based posterior regularization. Proceedings of the International Conference on Machine Learning (ICML) 2015. http://jmlr.org/proceedings/papers/v37/libbrecht15.html
  •  Maxwell W. Libbrecht, Jeffrey A. Bilmes, William S. Noble. Eliminating redundancy among protein sequences using submodular optimization. http://dx.doi.org/10.1101/051201