Current students

Mina Farid, PhD candidate
David R. Cheriton School of Computer Science

One challenge that faces most extraction tools is the long tail of information. Entities that lie in the long tail do not have enough mentions in the text, limiting their relevant context. The absence of enough repetition restricts the extraction of property values with high confidence.

Please note: This master’s thesis presentation will be given online.

Chufeng Hu, Master’s candidate
David R. Cheriton School of Computer Science

Local graph clustering methods are used to find small- and medium-scale clusters without traversing the graph. It has been shown that the combination of the Approximate Personalized PageRank (APPR) algorithm and the sweep method can efficiently detect a small cluster around the starting vertex. 

Please note: This master’s thesis presentation will be given online.

Alexandre Parmentier, Master’s candidate
David R. Cheriton School of Computer Science

This thesis presents two works with the shared goal of improving the capacity of multiagent trust modeling to be applied to social networks. 

Please note: This master’s thesis presentation will be given online.

Ibrahim Kettaneh, Master’s candidate
David R. Cheriton School of Computer Science

Please note: This master’s thesis presentation will be given online.

William Sigouin, Master’s candidate
David R. Cheriton School of Computer Science

Please note: This master’s thesis presentation will be given online.

Colin Vandenhof, Master’s candidate
David R. Cheriton School of Computer Science

Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world applications, from task-oriented dialogue systems to autonomous driving. However, one of the major bottlenecks to the adoption of RL is efficiency, as it often takes many time steps to learn an acceptable policy. 

Please note: This master’s thesis presentation will be given online.

Johann Wentzel, Master’s candidate
David R. Cheriton School of Computer Science

Please note: This PhD seminar will be given online.

Sasha Vtyurina, PhD candidate
David R. Cheriton School of Computer Science

Voice-based assistants have become a popular tool for conducting web search, particularly for factoid question answering. However, for more complex web searches their functionality remains limited, as does our understanding of the ways in which searchers can best interact with audio-based search results.