Seminar

Please note: This PhD seminar will be given online.

Please note: This PhD seminar will be given online.

Jay Henderson, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Edward Lank

Please note: This PhD seminar will be given online.

Jay Henderson, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Edward Lank

Please note: This seminar will be given online.

Aishwarya Ganesan, Postdoctoral Researcher
VMware Research

The tradeoff between performance and correctness is pervasive across computer systems such as shared-memory multiprocessors, databases, and local file systems. The same tradeoff exists in distributed storage systems as well; designers must often choose consistency or performance but not both. In this talk, I will show how we can build distributed storage systems that provide strong guarantees yet also perform well.

Jumyung “JC” Chang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Christopher Batty

Please note: This PhD seminar will be given online.

Nik Unger, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ian Goldberg

Please note: This PhD seminar will be given online.

Jiayi Chen, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Urs Hengartner

Please note: This seminar will be given online.

Kazem Cheshmi, Department of Computer Science
University of Toronto

Sparse matrix computations are an important class of algorithms frequently used in scientific simulations such as computer graphics and weather modeling as well as in data analytics codes and machine learning computations. The performance of these simulations relies heavily on the high-efficient implementations of sparse computations. 

Please note: This seminar will be given online.

Xiaokui Xiao, School of Computing
National University of Singapore

Given a graph G, network embedding maps each node in G into a compact, fixed-dimensional feature vector, which can be used in downstream machine learning tasks. Most of the existing methods for network embedding fail to scale to large graphs with millions of nodes, as they either incur significant computation cost or generate low-quality embeddings on such graphs.

Please note: This PhD seminar will be given online.

Andre Kassis, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Urs Hengartner