Events

Li Liu, PhD candidate
David R. Cheriton School of Computer Science

Following my previous seminar talk on embezzlement of entanglement, this talk introduces a more general version of the problem — self-embezzlement. Instead of embezzling a pair of entangled state from a catalyst, self-embezzlement aims to create two copies of the catalyst state using only local operators. 

Panos K. Chrysanthis
Department of Computer Science, University of Pittsburgh

Zeynep Korkmaz, PhD seminar
David R. Cheriton School of Computer Science

Analysis on graphs have powerful impact on solving many social and scientific problems, and applications often perform expensive traversals on large scale graphs. Caching approaches on top of persistent storage are among the classical solutions to handle high request throughput. However, graph processing applications have poor access locality, and caching algorithms do not improve disk I/O sufficiently.

Thomas Lidbetter, Master candidate
David R. Cheriton School of Computer Science

In this talk we consider two mostly disjoint topics in formal language theory that both involve the study and use of regular languages. The first topic lies in the intersection of automata theory and additive number theory. 

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

Murray Dunne, Master’s candidate
David R. Cheriton School of Computer Science

Distributed, life-critical systems that bridge the gap between software and hardware are becoming an integral part of our everyday lives. From autonomous cars to smart electrical grids, such cyber-physical systems will soon be omnipresent. With this comes a corresponding increase in our vulnerability to cyber-attacks. Monitoring such systems to detect malicious actions is of critical importance. 

Andreas Stöckel, PhD candidate
David R. Cheriton School of Computer Science

The artificial neurons typically employed in machine learning and computational neuroscience bear little resemblance to biological neurons. They are often derived from the “leaky integrate and fire” (LIF) model, neglect spatial extent, and assume a linear combination of input variables. It is well known that these simplifications have a profound impact on the family of functions that can be computed in a single-layer neural network. 

Dhinakaran Vinayagamurthy, PhD candidate
David R. Cheriton School of Computer Science

Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science

Jade Marcoux-Ouellet, Master’s candidate
David R. Cheriton School of Computer Science