Aaron Moss, PhD candidate
David R. Cheriton School of Computer Science
Vijay Ganesh, ECE
University of Waterloo
Mustafa Korkmaz, PhD candidate
David R. Cheriton School of Computer Science
Data centers consume significant amounts of energy and consumption is growing each year. Alongside efforts in the hardware domain, there are some mechanisms in the software domain to reduce energy consumption. One of these mechanisms is dynamic voltage and frequency scaling (DVFS) and modern servers which are equipped with multi-core CPUs.
Andrew Beltaos / Amenda Chow
University of Waterloo / York University
Teaching via analogies builds upon students' existing knowledge. New concepts that are taught only within the context of mathematics may seem foreign to students at first glance, but if students have already learned analogous concepts elsewhere in life, as educators, we can make use of their existing framework to strengthen their learning.
Bushra Aloraini, PhD candidate
David R. Cheriton School of Computer Science
Amine Mhedhbi, PhD candidate
David R. Cheriton School of Computer Science
We study the problem of optimizing subgraph queries (SQs) using the new worst-case optimal (WCO) join plans in Selinger-style cost-based optimizers. WCO plans evaluate SQs by matching one query vertex at a time using multiway intersections. The core problem in optimizing WCO plans is to pick an ordering of the query vertices to match.
We make two contributions:
Jeff Avery, PhD candidate
David R. Cheriton School of Computer Science
Professor Brian Forrest
Department of Pure Mathematics, University of Waterloo
There are many challenges to teaching mathematics in a fully online environment. In this talk I will show the important role that assigned work plays in mitigating many of these challenges. I will also speak about how my experience in teaching online has impacted the way in which I approach my on campus courses.
Hemant Saxena, PhD candidate
David R. Cheriton School of Computer Science
We address the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of possible dependencies. However, distributed algorithms must also optimize data communication costs, especially in current shared-nothing settings.
Abel Molina, PhD candidate
David R. Cheriton School of Computer Science
Yao (1993) proved that quantum Turing machines and uniformly generated quantum circuits are polynomially equivalent computational models: t >= n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t, and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time.