Current undergraduate students

Matthew Angus, Master’s candidate
David R. Cheriton School of Computer Science

There exists wide research surrounding the detection of out of distribution sample for image classification. Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing an image to be out of distribution. 

Shrinu Kushagra, PhD candidate
David R. Cheriton School of Computer Science

Clustering aims to group together data instances which are similar while simultaneously separating the dissimilar instances. The task of clustering is challenging due to many factors. The most well-studied is the high computational cost. The clustering task can be viewed as an optimization problem where the goal is to minimize a certain cost function (like k-means cost or k-median cost). Not only are the minimization problems NP-Hard but often also NP-Hard to approximate (within a constant factor). There are two other major issues in clustering, namely under-specificity and noise-robustness. 

Yingluo Xun, Master’s candidate
David R. Cheriton School of Computer Science

In reinforcement learning, entropy-regularized value function (in policy space) has attracted a lot of attention recently due to its effect on smoothing the value function, and the effect on encouraging exploration. However, there is a discrepancy between the regularized objective function and the original objective function in existing methods, which would potentially result in a discrepancy between the trained policy and the optimal policy, as the policy directly depends on the value function in the reinforcement learning framework. 

Greg d’Eon, Master’s candidate
David R. Cheriton School of Computer Science

Collaborative crowdsourcing tasks allow workers to solve more difficult problems than they could alone, but motivating workers in these tasks is complex. In this thesis, we study how to use payments to motivate groups of crowd workers. We leverage concepts from equity theory and cooperative game theory to understand the connection between fair payments and motivation. 

Priyank Jaini, PhD candidate
David R. Cheriton School of Computer Science

Triangular map is a recent construct in probability theory that allows one to transform any source probability density function to any target density function. Based on triangular maps, we propose a general framework for high-dimensional density estimation, by specifying one-dimensional transformations (equivalently conditional densities) and appropriate conditioner networks.