Seminar

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.

Di Wang, Postdoctoral fellow
Georgia Institute of Technology

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

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. 

John P. Conley, Department of Economics
Vanderbilt University

Blockchains are distributed, immutable, append only, ledgers designed to make trustless interactions between anonymous agents feasible and safe. The ledgers are maintained by networks of independent nodes who process transactions and come to a consensus view of which are valid and how this affects the ledger state. The integrity of blockchain ledgers therefore depends on the incentives contained in the consensus protocols that are designed to make the validating nodes behave honestly.

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

N. Asokan, Department of Computer Science
Aalto University, Finland

All kinds of previously local services are being moved to cloud settings. While this is justified by the scalability and efficiency benefits of cloud-based services, it also raises new security and privacy challenges. Solving them by naive application of standard security/privacy techniques can conflict with other functional requirements. In this talk, I will outline some cloud-assisted services and the conflicts that arise while trying to secure these services.

Verena Kantere
School of Electrical Engineering and Computer Science, University of Ottawa

Big Data analytics in science and industry are performed on a range of heterogeneous data stores, both traditional and modern, and on a diversity of query engines. Workflows are difficult to design and implement since they span a variety of systems. To reduce development time and processing costs, some automation is needed.

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