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Monday, January 22, 2018 — 9:30 AM EST

Title: Are cryptosystems based on ideal lattices quantum-safe ?

Speaker Jean-Francois Biasse
Affiliation: University of South Florida
Room:  QNC 1501

Abstract:

Shor's algorithm factors RSA integers and solves the Discrete Logarithm Problem (DLP) in quantum polynomial time. Therefore, alternatives to these cryptosystems must be developed to replace the current cryptographic schemes. One of the most interesting family of schemes that have been proposed for the replacement of RSA-based and DLP-based primitives relies on the hardness of finding short vectors in Euclidean lattices.

Thursday, January 18, 2018 — 1:30 PM EST

Title: Quantum Latin Squares and Magic Unitaries

Speaker: Chris Godsil
Affiliation: University of Waterloo
Room: MC 6486

Abstract:

A quantum Latin square is an n x n array of vectors such that vectors in any row or any column form an orthonormal basis for Cn. A magic unitary matrix is an n x n array of projections such that each row and column sums to I.

Wednesday, January 17, 2018 — 9:30 AM EST

Title: Stochastic algorithms for distributed optimization and machine learning

Speaker: Yi Zhou
Affiliation: Georgia Institute of Technology
Room: MC 5501

Abstract:

In the big data era, machine learning acts as a powerful tool to transform data into knowledge that helps us make predictions and decisions. It has strong ties to the field of optimization, in the way the latter provides methods and theory.

Monday, January 15, 2018 — 9:30 AM EST

Title: Next-generation authentication and key exchange protocols

Speaker: Douglas Stebila
Affiliation: McMaster University
Room: (change of room!) MC 5417

Abstract:

Key exchange and authentication are at the heart of protocols for establishing secure communication on the Internet and other communication channels.  In this talk, I'll discuss ways in which key exchange is evolving to meet new security demands and deliver new functionality. 

Friday, January 12, 2018 — 9:30 AM EST

Title: Towards Large-Scale Nonconvex/Stochastic Discrete Optimization

Speaker: Cong Han Lim
Affiliation: University of Wisconsin-Madison
Room: MC 5501

Abstract:

Modern data analytics is powered by scalable mathematical optimization methods. For decision-making, we want to be able to solve large-scale mathematical problems that include discrete choices or structures. These can already be very challenging to solve exactly even when the objective and feasible region are convex.

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