Faculty

Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science

We address the problem of incremental domain adaptation (IDA). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We propose to augment a recurrent neural network (RNN) with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity.

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.

Professors Olga Veksler and Yuri Boykov joined the David R. Cheriton School of Computer Science earlier this year. Previously, both were full professors in the Department of Computer Science at Western University, where they were faculty members for 14 years.

Their research interests are in the area of computer vision. In particular, Olga’s interests are in visual correspondence and image segmentation, and Yuri’s also include 3D reconstruction and biomedical image analysis.

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 21^stACM 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.

Faculty

PhD Seminar: Progressive Memory Banks for Incremental Domain Adaptation

PhD Seminar: Beyond LIF: The Computational Power of Passive Dendritic Trees

Meet Professors Olga Veksler and Yuri Boykov, two computer vision researchers who recently joined the Cheriton School of Computer Science

Mike Schaekermann, Joslin Goh, Kate Larson and Edith Law win best paper award at CSCW 18

Maura Grossman named as a Leading Individual in Canada by Who’s Who Legal 2018 for contributions to eDiscovery

Master’s Thesis Presentation: Coordination in a Peer Production Platform: A Study of Reddit’s /r/Place Experiment

Eight CS professors become Vector Institute faculty affiliates

PhD Defence: TwitSong: A Current Events Computer Poet and the Thorny Problem of Assessment

Master’s Thesis Presentation: On the Computational Complexity of Center-based Clustering

Master’s Thesis Presentation: Predicting Short-Term Water Consumption for Multi-Family Residences

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