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Friday, April 17, 2020 1:00 pm - 1:00 pm EDT (GMT -04:00)

PhD Seminar • Human-Computer Interaction — Ambiguity-aware AI Assistants for Medical Data Analysis

Please note: This PhD seminar will be given online.

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. 

Tuesday, April 21, 2020 10:30 am - 10:30 am EDT (GMT -04:00)

PhD Seminar • Data Systems — Estimating Property Values for Long Tail Entities

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.

Friday, April 24, 2020 1:00 pm - 1:00 pm EDT (GMT -04:00)

PhD Seminar • Human-Computer Interaction — A Mixed-Method Analysis of Text and Audio Search Interfaces

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.

Friday, April 24, 2020 4:00 pm - 4:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Machine Learning — Local Graph Clustering Using l1-regularized PageRank Algorithms

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.