His research applies statistical models to natural language problems in ways that interact with humans, learn from humans, or help researchers understand humans. Jordan is an expert in the application of topic models, completely automatic tools that can discover structure and meaning in large, multilingual datasets. He is a contributor to the Natural Language Toolkit (NLTK), a popular tool used in natural language education research. Jordan received his PhD from Princeton University in 2010, advised by Dave Blei, and has bachelors degrees in history and computer science from the California Institute of Technology. He received a best student paper honorable mention at NIPS 2009 and a Computing Innovation Fellowship (declined). His current work is supported by NSF, IARPA, and ARL.