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Wednesday, November 18, 2020 12:00 pm - 12:00 pm EST (GMT -05:00)

PhD Seminar • Data Systems — Studying Query Abandonment Behavior in Web Search

Please note: This PhD seminar will be given online.

Mustafa Abualsaud, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Mark D. Smucker

When search results fail to satisfy users’ information needs, users often reformulate their search query in the hopes of receiving better results. In many cases, users abandon their queries without clicking on any search results. 

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

Hsiu-Wei Yang, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity matching is to align entities in a source language with their counterparts in target languages. 

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

Omar Attia, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ihab Ilyas

Machine learning data repair systems (e.g., HoloClean) have achieved state-of-the-art performance for the data repair problem on many datasets. However, these systems still face significant challenges when applied to sparse datasets.

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

Ashutosh Devendrakumar Adhikari, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

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

Alex Pawelczyk, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Daniel M. Berry

Please note: This seminar will be given online.

Sepideh Mahabadi
Toyota Technological Institute at Chicago

Searching and summarization are two of the most fundamental tasks in massive data analysis. In this talk, I will focus on these two tasks from the perspective of diversity and fairness.