By Jessica Kent
February 01, 2021 – The University of Southern California and Amazon have established the Center for Secure and Trusted Machine Learning, a joint research effort to develop new approaches to machine learning privacy and security.
The new center will be housed within the USC Viterbi School of Engineering, and will help support USC and Amazon researchers in the design of novel, privacy-preserving machine learning solutions.
The organizations expect that the center will lead to a new line of critical research on privacy and security aspects of machine learning, a timely endeavor given the technology’s growing presence in healthcare and other industries.
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“Developing solutions to protect our data requires cutting edge advances. I am pleased the USC-Amazon center will build a platform based on a shared commitment to advancing understanding and developing solutions,” said USC Provost Charles F. Zukoski.
“The center reflects the important role that our university has in society, as a leading research institution, in promoting the advancement of scholarship and technology.”
Each year, the center will provide support for research projects focused on the development of new methodologies for secure and privacy-preserving machine learning solutions that can scale to support billions of users.
The center will also provide annual fellowships to doctoral students working in this research area, allowing them to advance research frontiers. Fellowship recipients will be named as Amazon ML Fellows in recognition of their promise and achievements. These fellowships will give students greater understanding of industry and solution-driven research.
“At Amazon, our mission is to be Earth’s most customer-centric company. For us, customer trust is of paramount importance. And that means maintaining the highest possible standards of security and privacy when handling customer data,” said Prem Natarajan, Alexa AI vice president of Natural Understanding.
“We are delighted to bring together top talent at Amazon and USC in a joint mission to drive ground-breaking advances in privacy and security preserving machine learning – advances that enable us to continue to safely and securely deliver experiences that enrich and delight our customers worldwide.”
In addition to funded research projects and annual fellowships for doctoral students, the collaborators will host an annual joint public research symposium to share their knowledge with the machine learning and AI communities.
Amazon and USC will also host annual workshops, as well as training and recruiting events for university and high school students.
“We are delighted to partner with Amazon in establishing the Center for Secure and Trusted Machine Learning at the USC Viterbi School of Engineering. Creating such mutually beneficial partnerships between academia and industry will help guide the development of a field as important and as rapidly changing as machine learning and artificial intelligence,” said Yannis C. Yortsos, dean of the USC Viterbi School of Engineering
“Enhancing the security and trustworthiness of these powerful technologies is a necessity and a grand challenge-type problem. We eagerly anticipate the results of this new collaboration.”
The new center is expected to leverage talent and scholarship across Amazon and USC Viterbi, particularly the Ming Hsieh Department of Electrical and Computer Engineering, the Department of Computer Science, and the school’s leading research institutes.
“The USC-Amazon center provides an exciting opportunity, through close university-industry collaboration, to study trust and security,” said Salman Avestimehr, professor and director of the Information Theory and Machine Learning research lab at USC Viterbi and an Amazon Scholar, who will serve as the inaugural director of the center.
“Through the center’s Amazon ML Fellows program, we can also recognize our exceptional PhD students and further the impact of their research through collaborations with Amazon.”
Source: https://healthitanalytics.com/news/usc-amazon-launch-center-for-secure-machine-learning-development
