Close

Search

Search Menu
Photonics Media Photonics Buyers' Guide Photonics EDU Photonics Spectra BioPhotonics EuroPhotonics Industrial Photonics Photonics Showcase Photonics ProdSpec Photonics Handbook
More News
share
Email Facebook Twitter Google+ LinkedIn Comments

US University Consortium Receives $10M NSF Grant for Machine Learning Security

Photonics.com
Nov 2018
SAN DIEGO, Nov. 27, 2018 — A team of U.S. computer scientists is receiving a $10 million grant from the National Science Foundation (NSF) to make machine learning more secure.

The grant establishes the Center for Trustworthy Machine Learning, which is a consortium of U.S. universities. Researchers will work together toward two goals: understanding the risks inherent to machine learning, and developing the tools, metrics, and methods to manage and mitigate these risks. The grant will be led by researchers at Pennsylvania State University; Stanford University; the University of California, Berkeley; the University of California, San Diego; the University of Virginia; and the University of Wisconsin-Madison.

The science and defensive techniques emerging within the center will provide the basis for building more trustworthy and secure systems in the future, as well as fostering a long-term research community within this domain of technology.

“This research is important because machine learning is becoming more pervasive in our daily lives, powering technologies we interact with, including services like e-commerce and internet searches, as well as devices such as internet-connected smart speakers,” said Kamalika Chaudhuri, a computer science professor who is leading the UC San Diego portion of the research.

The award is part of NSF’s Secure and Trustworthy Cyberspace (SaTC) program, which includes a $78.2 million portfolio of more than 225 new projects in 32 states, spanning a broad range of research and education topics including artificial intelligence, cryptography, network security, privacy, and usability.

Researchers will explore methods to defend a trained model against adversarial inputs. To do this, they will emphasize developing measurements of how robust defenses are, as well as understanding limits and costs of attacks. They will also develop new training methods that are immune to manipulation while investigating the general security of sophisticated machine learning algorithms, including potential abuses of machine learning models such as fake content generators.

BusinessNational Science Foundationgrantmachine learningCenter for Trustworthy Machine Learningcomputer scienceconsortiumPennsylvania State UniversityStanford UniversityUniversity of California BerkeleyUniversity of California San DiegoUniversity of VirginiaUniversity of Wisconsin-MadisonAmericaseducation

Comments
Terms & Conditions Privacy Policy About Us Contact Us
back to top
Facebook Twitter Instagram LinkedIn YouTube RSS
©2018 Photonics Media, 100 West St., Pittsfield, MA, 01201 USA, info@photonics.com

Photonics Media, Laurin Publishing
x We deliver – right to your inbox. Subscribe FREE to our newsletters.
We use cookies to improve user experience and analyze our website traffic as stated in our Privacy Policy. By using this website, you agree to the use of cookies unless you have disabled them.