Nashid Shahriar, PhD candidate
David R. Cheriton School of Computer Science
Mike Schaekermann, PhD candidate
David R. Cheriton School of Computer Science
Artificial intelligence (AI) assistants for clinical decision making show increasing promise in medicine. However, medical assessments can be contentious, leading to expert disagreement. This raises the question of how AI assistants should be designed to handle the classification of ambiguous cases.
William Sigouin, Master’s candidate
David R. Cheriton School of Computer Science
Hemant Surale, PhD candidate
David R. Cheriton School of Computer Science
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.
Ibrahim Kettaneh, Master’s candidate
David R. Cheriton School of Computer Science
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.
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.
Mina Farid, PhD candidate
David R. Cheriton School of Computer Science
Kimon Fountoulakis, Assistant Professor
David R. Cheriton School of Computer Science