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Katie Bouman
American computer scientist known for processing the first direct image of a black hole

Katie Bouman

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American computer scientist known for processing the first direct image of a black hole
Known for
Continuous High-resolution Image Reconstruction using Patch priors
A.K.A.
Katherine L. Bouman K. L. Bouman Katherine Bouman Katherine Louise Bouman
Gender
Female
Birth
Place of birth
West Lafayette, USA
Age
36 years
The details (from wikipedia)

Biography

Katherine Louise Bouman (/ˈbmən/; born 1989/1990) is an American computer scientist working in the field of computer imagery.

She led the development of an algorithm for imaging black holes, known as Continuous High-resolution Image Reconstruction using Patch priors (CHIRP), and was a member of the Event Horizon Telescope team that captured the first image of a black hole.

As of June 2019, she is an assistant professor of computing and mathematical sciences at the California Institute of Technology.

Early life and education

Bouman grew up in West Lafayette, Indiana, and graduated from West Lafayette Junior-Senior High School in 2007. Her father, Charles Bouman, is a professor of electrical and computer engineering and biomedical engineering at Purdue University.As a high school student, she conducted imaging research at Purdue University. She first learned about the Event Horizon Telescope in school in 2007.

Bouman studied electrical engineering at the University of Michigan and graduated summa cum laude in 2011. She earned her master's degree (2013) and doctoral degree (2017) in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT).

At MIT, she was a member of the Haystack Observatory. She was supported by a National Science Foundation Graduate Fellowship. Her master's thesis, Estimating Material Properties of Fabric through the Observation of Motion, was awarded the Ernst Guillemin Award for best Master's Thesis in electrical engineering. Her Ph.D. dissertation, Extreme imaging via physical model inversion: seeing around corners and imaging black holes, was supervised by William T. Freeman. Prior to receiving her doctoral degree, Bouman delivered a TEDx talk, How to Take a Picture of a Black Hole, which explained algorithms that could be used to capture the first image of a black hole.

Research and career

After earning her doctorate, Bouman joined Harvard University as a postdoctoral fellow on the Event Horizon Telescope Imaging team.

Bouman joined Event Horizon Telescope project in 2013. She led the development of an algorithm for imaging black holes, known as Continuous High-resolution Image Reconstruction using Patch priors (CHIRP). CHIRP inspired image validation procedures used in acquiring the first image of a black hole in April 2019, and Bouman played a significant role in the project by verifying images, selecting parameters for filtering images taken by the Event Horizon Telescope, and participating in the development of a robust imaging framework that compared the results of different image reconstruction techniques. Her group is analyzing the Event Horizon Telescope's images to learn more about general relativity in a strong gravitational field.

Bouman received significant media attention after a photo, showing her reaction to the detection of the black hole shadow in the EHT images, went viral. Some people in the media and on the Internet misleadingly implied that Bouman was a "lone genius" behind the image. However, Bouman herself repeatedly noted that the result came from the work of a large collaboration, showing the importance of teamwork in science. Bouman also became the target of online harassment, to the extent that her colleague Andrew Chael made a statement on Twitter criticizing "awful and sexist attacks on my colleague and friend", including attempts to undermine her contributions by crediting him solely with work accomplished by the team.

She joined the California Institute of Technology as an assistant professor in June 2019, where she plans to work on new systems for computational imaging using computer vision and machine learning.

The contents of this page are sourced from Wikipedia article. The contents are available under the CC BY-SA 4.0 license.
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