William T. Freeman

William T. Freeman is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology.[2] He is known for contributions to computer vision.

William Freeman
Born
William T. Freeman

1957 (age 62–63)
CitizenshipUnited States
Alma materStanford University
MIT
AwardsACM Fellow
Scientific career
FieldsComputer Vision
InstitutionsMassachusetts Institute of Technology
Doctoral advisorEdward Adelson
Doctoral studentsKatie Bouman[1]
Websitebillf.mit.edu

Education

Freeman received his undergraduate degree in physics from Stanford University in 1979, and his Ph.D. from MIT in 1992.[3]

Career and research

Freeman worked at Mitsubishi Electric Research Labs before joining the faculty at MIT in 2001, where he is currently Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science. He served as the Associate Department Head from 2011 to 2014.

Freeman's research interests include machine learning applied to computer vision, Bayesian models of visual perception, and computational photography. He has also made research contributions on steerable filters and pyramids, orientation histograms, the generic viewpoint assumption, color constancy, computer vision for computer games, and belief propagation in networks with loops. He received outstanding paper awards at computer vision or machine learning conferences in 1997, 2006, 2009 and 2012, and test-of-time awards for papers from 1990 and 1995.

Awards and honors

Freeman is a fellow of the Association for Computing Machinery (ACM)[4] and the Institute of Electrical and Electronics Engineers (IEEE)[5]

References
  1. Bouman, Katherine Louise (2017). Extreme imaging via physical model inversion : seeing around corners and imaging black holes (PhD thesis). Massachusetts Institute of Technology. hdl:1721.1/113998. OCLC 1027411179.
  2. "William T. Freeman's Homepage". MIT. Dec 2017.
  3. "Resume". MIT. Aug 2014.
  4. "IEEE Fellow Directory". IEEE. Dec 2017.
  5. "ACM Fellow Recipients". ACM. Dec 2017.


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