Machine Learning
Core Faculty
ee.washington.eduMachine learning, speech/language/bioinformatics/music, submodularity & discrete optimization
Large scale computational methods for statistics, machine learning, signal processing
Computational biology - network biology, genetics of complex traits, precision medicine, clinical sequencing
Machine learning - feature selection, structure learning, interpretable machine learning
Development of machine learning techniques for molecular biology
Artificial intelligence, internet systems, human computer interaction
Affiliate Faculty and Research Scientists
Natural Language Processing, Artificial Intelligence, Machine Learning
Machine learning, mathematical optimization, statistical hypothesis testing, computer vision, and signal processing.
Speech recognition, machine translations
Computer vision, multimedia retrieval, biomedical informatics
Computational and graphical methods in multivariate analysis
Postdoctoral Researchers
Graduate Students
human-robot interaction, machine learning, brain-computer interface
Discrete optimization. Specifically, submodularity and machine learning
Machine learning, artificial intelligence, natural language processing, information extraction
computational social science, nlp for politics
I'm interested in natural language understanding. My research is aimed at learning to automatically map natural language sentences to graph representations of their meaning, ideally in a way that works well for a broad variety of domains and languages.
ML Alumni
Corin Anderson (Google)
Michele Banko (Microsoft)
Jesse Davis (KU Leuven)
Doug Downey (Northwestern)
Krzysztof Z Gajos (Harvard)
Vibhav Gogate (UT Dallas)
Stanley Kok (UMaryland - CSE)
Tessa Lau (IBM)
Thomas Lin (Microsoft)
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Daniel Lowd (UOregon - CIS)
Xu Miao (Microsoft)
Ana-Maria Popescu (Yahoo!)
Stefan Schoenmackers (Decide)
Richard Segal (IBM)
Parag Singla (IIT Delhi - CSE)
Laurie Spencer (CSE)
Fei Wu (Google)
Danny Wyatt (Google)
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