Andrew McCallumis this you? claim profile
Andrew McCallum is a Professor and Researcher at the University of Massachusetts Amherst Computing Department. Its main specialities are in machine learning, natural language processing, extraction of information, integration of information and analysis of social networks.
McCallum graduated from Dartmouth College with the summa cum laude in 1989. He completed his Ph.D. in 1995 under the supervision of Dana H. Ballard at the University of Rochester. He was a postdoctoral fellow, working at Carnegie Mellon University with Sebastian Thrun and Tom M. Mitchell.
He was a Research Scientist and Research Coordinator at the Justsystem Pittsburgh Research Center from 1998 to 2000. Between 2000 and 2002, WhizBang Labs Vice President Research and Development, and Director of its office in Pittsburgh. He has been a computer science professor at the University of Massachusetts Amherst since 2012.
In 2009, he was elected Fellow of the Artificial Intelligence Association and in 2017 Fellow of the Computing Machinery Association. He has been the President of the International Machine Learning Society from 2014 to 2017, which organizes the International Machine Learning Conference. He is also director of the Data Science Center in UMass and leads a new partnership with the initiative Chan and Zuckerberg. In 2018, the initiative provided an initial 5.5 million grant to the Centre, which supports research to provide scientists with new ways of exploring and finding research articles.
McCallum has developed conditional random fields, in collaboration with John Lafferty and Fernando Pereira, first described in a paper submitted at the International Machine Learning Conference. McCallum wrote a number of widely used open-source software toolkits for maker learning, language processing, and other text handling, including Rainbow, Mallet, and FACTORIE. In 2011 this paper won ICML “Test of Time.” He was also instrumental in the publication of the Enron Corpus, a large collection of e-mails which was used as the basis for a number of social networking and language academic studies.