Professor Claire Monteleoni's Machine Learning Group is concerned with developing principled methods (known as algorithms) to automatically detect patterns in data. In this era of "Big Data," the various forms of complexity inherent in real data sources increasingly pose challenges for machine learning algorithm design. The GW Machine Learning Group works on the design, analysis, and application of machine learning algorithms, motivated by problems in real data sources, including learning from data streams, learning from raw (unlabeled) data, learning from private data, and climate informatics: accelerating discovery in climate science with machine learning.
- Ph.D., Massachusetts Institute of Technology, 2006
- M.S., Massachusetts Institute of Technology, 2003
- B.A., Harvard University
- Machine Learning
- Big Data Analytics
- Climate Informatics