News

Katie Bouman Named Recipient of the 2020 Breakthrough Prize for Fundamental Physics

09-09-19

Katie Bouman, Assistant Professor of Computing and Mathematical Sciences and Rosenberg Scholar, has been named a recipient of the 2020 Breakthrough Prize for Fundamental Physics as part of the Event Horizon Telescope (EHT) team that generated the first-ever image of a black hole. [Caltech story] [Breakthrough Prize Announcement]

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Seeing Farther and Deeper

07-02-19

Katie Bouman, Assistant Professor of Computing and Mathematical Sciences, creates images from nonideal sensor data and mines for information from images using techniques that can be applied to everything from medical imaging to studying the universe. She likes to search for information hidden in images, imperceptible to humans, that she can use to learn about the environment around us. [Profile of new EAS faculty member Professor Bouman]

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Katie Bouman Joins EAS and CMS

04-11-19

Congratulations to the entire Event Horizon Telescope team, and especially to Dr. Katie Bouman who is joining the Engineering and Applied Science (EAS) Division in June as assistant professor of computing and mathematical sciences (CMS). Currently, Caltech and CO Architects are working with her to design and construct a unique laboratory that will facilitate her work in computational imaging. The laboratory is the first of its kind and is designed for her to conduct experimental work in conjunction with her computational approaches – making it possible, for instance, to observe phenomena previously difficult or impossible to measure. The black hole imaging is one spectacular example of how Professor Bouman’s algorithms are advancing our knowledge of the world; she has also developed algorithms that let us “see around corners” and detect material properties (such as stiffness and dampness) via imaging. In her work, Bouman has also developed methods to combine information from both imaging as well as acoustic systems to analyze sub-pixel scale vibrations of otherwise seemingly still objects. As a result, relatively inexpensive cameras, combined with powerful algorithms, are an increasingly attractive alternative to complex and expensive laser-based systems to sense “invisible” attributes of a material. [Caltech story - How to Take a Picture of a Black Hole]

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