Safe Machine Learning

Monday, November 11, 2019
11:40 AM - 12:40 PM
Department of Computer Science, 500 W. 120th St., New York, New York 10027
Room/Area: 451
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Computer Science Department
Distinguished Lecture Series
Shafi Goldwasser

ABSTRACT:
Cryptography and Computational Learning have shared a curious history: a scientific success for one has often provided an example of an impossible task for the other. Today, the goals of the two fields are aligned. Cryptographic models and tools can and should play a role in ensuring the safe use of machine learning. We will discuss this development with its challenges and opportunities.

BIO:
Shafi Goldwasser is the Director of the Simons Institute for the Theory of Computing, and a professor of computer science at UC Berkeley. She is also the RSA Professor of Electrical Engineering and Computer Science at MIT, and a professor of computer science and applied mathematics at the Weizmann Institute of Science in Israel. Goldwasser received a BS in applied mathematics from Carnegie Mellon University in 1979, and MS and PhD in computer science from UC Berkeley in 1984.

Goldwasser was the recipient of ACM Turing Award for 2012. She was also the recipient of the Gödel Prize in 1993 and another in 2001 for her work on interactive proofs and connections to approximation, and was awarded the ACM Grace Murray Hopper Award (1996), the RSA award in mathematics (1998), the ACM Athena award for women in computer science (2008), the Benjamin Franklin Medal in Computer and Cognitive Science (2010), the IEEE Emanuel R. Piore Award (2011), the Barnard College Medal of Distinction (2016), and the Suffrage Science Award (2016). She is a member of the AAAS, ACM, NAE, NAS, Israeli Academy of Science, London Mathematical Society, and Russian Academy of Science.

Host: Jeannette Wing
Event Contact Information:
Daniel Hsu
[email protected]
LOCATION:
  • Morningside
TYPE:
  • Lecture
CATEGORY:
  • Computer Science
EVENTS OPEN TO:
  • Alumni
  • Faculty
  • Family-friendly
  • Postdocs
  • Graduate Students
  • Public
  • Prospective Students
  • Staff
  • Students
  • Trainees
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