Seminar

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

Entanglement is a type of resource used in quantum information theory that gives correlations that cannot be simulated using classical probability theory. It is known that entanglement cannot be created locally. 

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. 

Nick Rollick, Graduate student
Department of Pure Mathematics

For this week's seminar, I invite you to join me for an informal chat about my experiences using "reflective responses" in my elementary number theory course. In these bi-weekly formal reflective assignments, my students were asked to set and monitor learning goals, identify gaps in understanding, and ponder the value and importance of their course material. Most importantly, I responded in detail to every student's reflection, creating a meaningful course-long conversation.

Jaemyung Kim, PhD candidate
David R. Cheriton School of Computer Science

Transaction durability guarantees the ability to recover committed transactions from failures. However, making every transaction durable impacts transaction processing performance. Some ad-hoc durability mechanisms (e.g., delayed durability) improve performance, but they risk transactions losing their effects due to failures. The current one-size-fits-all transaction durability model does not solve this problem.

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:

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