Computational imaging involves the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. Computers can replace bulky and expensive optics by solving computational inverse problems. This talk will describe new microscopes that use computational imaging to enable 3D, super-resolution and phase imaging with simple and inexpensive hardware. Our reconstruction algorithms are based on large-scale nonlinear non-convex optimization with sparsity-based regularizers similar to compressed sensing.
One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk, Dr. Schramm will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective. The slides from this presentation can be viewed here.
Presented by GraphXD and BIDS at the University of California, Berkeley, on Thursday, September 28, 2017.
The Splash Brothers, Steph Curry, and Klay Thompson, are great shooters but they are not streak shooters. Only rarely do they show signs of a hot hand. This counter-intuitive result is based on an empirical analysis of field goal and free throw data from the 82 regular season games and 17 post season games played by the Golden State Warriors in 2016–2017.
Building on the success of its “Collections as Data” symposium last year, the Library of Congress National Digital Initiatives (NDI) again hosted a daylong symposium featuring a cadre of experts to explore the value of using digital collections and their impact on the public. The symposium featured case studies and impact stories about the application of digital methods in analyzing and sharing collections.
BIDS Fellow Nick Adams presented this talk at the Library of Congress National Digital Initiatives (NDI) on July 25, 2017.
In this talk at the Library of Congress' Collections as Data Symposium on July 25, 2017, BIDS' Nick Adams gave this talk about creating tutorials to teach archivists, librarians, and computational researchers how to make public digital archives accessible for computational text analysis techniques, allowing us to ask and answer more interesting and complex questions of those textual archives. This project also won a grant from the Social Science Research Council.
