Current students

Youngbin Kim, Master’s candidate
David R. Cheriton School of Computer Science

AbstractServerless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. 

Taylor Hornby, Master’s candidate
David R. Cheriton School of Computer Science

This thesis contributes to two areas. The first is the study of parallel repetition theorems and concentration bounds for nonlocal games and quantum interactive proofs. 

We make the following contributions:

Pak Hay Chan, Master’s candidate
David R. Cheriton School of Computer Science

We consider a new problem of designing a network with small $s$-$t$ effective resistance. In this problem, we are given an undirected graph $G=(V,E)$ where each edge $e$ has a cost $c_e$ and a resistance $r_e$, two designated vertices $s,t \in V$, and a cost budget $k$.

Daniel Recoskie, PhD candidate
David R. Cheriton School of Computer Science

Irfan Ahmad, Founder and CEO
CachePhysics

​Caches in modern distributed and storage systems must be manually tuned and sized in response to changing application’s workload. A balance must be achieved between cost, performance and revenue loss from cache sizing mis-matches. However, caches are inherently nonlinear systems making this exercise equivalent to solving a maze in the dark.

Vineet John, Master’s candidate
David R. Cheriton School of Computer Science

This thesis tackles the problem of disentangling the latent style and content variables in a language modelling context. This involves splitting the latent representations of documents by learning which features of a document are discriminative of its style and content, and encoding these features separately using neural network models.

Royal Sequiera, Master’s candidate
David R. Cheriton School of Computer Science

Erinn Atwater, PhD candidate
David R. Cheriton School of Computer Science

Nimesh Ghelani, Master’s candidate
David R. Cheriton School of Computer Science

High recall information retrieval is crucial to tasks such as electronic discovery and systematic review. Continuous Active Learning (CAL) is a technique where a human assessor works in loop with a machine learning model; the model presents a set of documents likely to be relevant and the assessor provides relevance feedback. 

Kshitij Jain, Master’s candidate
David R. Cheriton School of Computer Science

We introduce a problem called the Minimum Shared-Power Edge Cut (MSPEC). The input to the problem is an undirected edge-weighted graph with distinguished vertices s and t, and the goal is to find an s-t cut by assigning "powers'' at the vertices and removing an edge if the sum of the powers at its endpoints is at least its weight. The objective is to minimize the sum of the assigned powers.