Current students

Anastasia Kuzminykh, PhD candidate
David R. Cheriton School of Computer Science

Video-mediated communication has long struggled with asymmetrical constraints on situational awareness, especially in hybrid work meetings between collocated and remote participants. Advances in computer vision offer exciting opportunities to augment mediated situational awareness, but we must first understand what is meaningful to capture and present.

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. 

Jeff Avery, 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:

Dhinakaran Vinayagamurthy, 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.

Xinan Yan, PhD candidate
David R. Cheriton School of Computer Science

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.