Dr. Bouman, who was part of the Event Horizon Telescope team that captured the first photograph of a black hole, will talk about the challenges of the project.

This talk will present the methods and procedures used to produce the first image of a black hole from the Event Horizon Telescope. It has been theorized that a black hole will leave a "shadow" on a background of hot gas. Taking a picture of this black hole shadow could help to address a number of important scientific questions, both on the nature of black holes and the validity of general relativity. Unfortunately, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, Dr. Bouman discusses techniques that were developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Imaging a black hole's structure with this computational telescope requires us to reconstruct images from sparse measurements, heavily corrupted by atmospheric error.

Katie Bouman is a Rosenberg Scholar and an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics where she worked with the Event Horizon Telescope and was part of the team that published the first picture of a black hole in April of 2019. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor's degree in Electrical Engineering from the University of Michigan.

Katie Bouman's research focuses on computational imaging: designing systems that tightly integrate algorithm and sensor design, making it possible to observe phenomena previously difficult or impossible to measure with traditional approaches. Imaging plays a critical role in advancing science. However, as science continues to push boundaries, traditional sensors are reaching the limits of what they can measure. Her group combines ideas from signal processing, computer vision, machine learning, and physics to find and exploit hidden signals for both scientific discovery and technological innovation.

Aronson Family Science and Society Honors Lecture, Purdue Honors College

Researchers should cite this work as follows:

• Katie L. Bouman (2020), "Portrait of a Black Hole & Beyond," https://nanohub.org/resources/34068.

  BibTex | EndNote

1. big data
2. data science
3. image processing
4. image reconstruction