Computer science doctoral student Mike Schaekermann, Dr. Joslin Goh and Schaekermann’s cosupervisors, Professors Kate Larson and Edith Law, have received a best paper award at CSCW 18, the 21stACM Conference on Computer Supported Cooperative Work and Social Computing.
Research Professor Maura Grossman has been named as a Leading Individual in Canada for her contributions to eDiscovery by Who’s Who Legal 2018.
Ben Armstrong, Master’s candidate
David R. Cheriton School of Computer Science
Understanding the factors causing groups to engage in coordinating behaviour has been an active research area for decades. In this thesis, we study this problem using a novel dataset of crowd behaviour from an online experiment hosted by Reddit.
The Vector Institute drives excellence and leadership in Canada’s knowledge, creation and use of artificial intelligence to foster economic growth and improve the lives of Canadians. The institute is dedicated to the transformative field of artificial intelligence, excelling in machine and deep learning research.
Carolyn Lamb, PhD candidate
David R. Cheriton School of Computer Science
This thesis is driven by the question of how computers can generate poetry, and how that poetry can be evaluated. We survey existing work on computer-generated poetry and interdisciplinary work on how to evaluate this type of computer-generated creative product.
Nicole McNabb, Master’s candidate
David R. Cheriton School of Computer Science
Clustering is the task of partitioning data so that “similar” points are grouped together and “dissimilar” ones are separated. In general, this is an ill-defined task. One way to make clustering well-defined is to introduce a clustering objective to optimize. While many common objectives such as k-means are known to be NP-hard, heuristics output “nice” clustering solutions efficiently in practice. This work analyzes two avenues of theoretical research that attempt to explain this discrepancy.
Irish Medina, Master’s candidate
David R. Cheriton School of Computer Science
Smart water meters have been installed across Abbotsford, British Columbia, Canada, to measure the water consumption of households in the area. Using this water consumption data, we develop machine learning and deep learning models to predict daily water consumption for existing multi-family residences. We also present a new methodology for predicting the water consumption of new housing developments.
Daniel Recoskie, PhD candidate
David R. Cheriton School of Computer Science
The wavelet transform is a well-studied and understood analysis technique used in signal processing. In wavelet analysis, signals are represented by a sum of self-similar wavelet and scaling functions. Typically, the wavelet transform makes use of a fixed set of wavelet functions that are analytically derived. We propose a method for learning wavelet functions directly from data. We impose an orthogonality constraint on the functions so that the learned wavelets can be used to perform both analysis and synthesis. We accomplish this by using gradient descent and leveraging existing automatic differentiation frameworks. Our learned wavelets are able to capture the structure of the data by exploiting sparsity. We show that the learned wavelets have similar structure to traditional wavelets.
Abdullah Rashwan, PhD candidate
David R. Cheriton School of Computer Science
We present a discriminative learning algorithm for Sum-Product Networks (SPNs) based on the Extended Baum-Welch (EBW) algorithm.
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.