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Tuesday, May 21, 2019 9:30 am - 9:30 am EDT (GMT -04:00)

MMath Thesis Presentation • Entity Matching and Disambiguation Across Multiple Knowledge Graphs

Michael Farag, MMath candidate
David R. Cheriton School of Computer Science

Knowledge graphs are considered an important representation that lies between free text on one hand and fully-structured relational data on the other. Knowledge graphs are a backbone of many applications on the Web. With the rise of many large-scale open-domain knowledge graphs like Freebase, DBpedia, and Yago, various applications including document retrieval, question answering, and data integration have been relying on them.

Friday, May 24, 2019 10:30 am - 10:30 am EDT (GMT -04:00)

DSG Seminar Series • Ultra-scalable transactional management, linear scale out of transactional workloads

Speaker: Ricardo Jimenez-Peris

Abstract: The talk will present the ultra-scalable distributed algorithm to process transactional management and how it has been implemented as part of the LeanXcale database. The talk will go into the details on how ACID properties have been scaled out independently in a composable manner.

Wednesday, June 12, 2019 12:15 pm - 12:15 pm EDT (GMT -04:00)

PhD Seminar • HoloDetect: Few-Shot Learning for Error Detection

Alireza Heidari, PhD candidate
David R. Cheriton School of Computer Science

We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement.

Wednesday, August 7, 2019 2:30 pm - 2:30 pm EDT (GMT -04:00)

MMath Research Paper Presentation • Math Information Retrieval with Wildcards in the Context of Pen-based Interfaces

Camilo Munoz, MMath candidate
David R. Cheriton School of Computer Science

Thanks to the advance in mobile and touch screen devices, handwritten input has gained more popularity among users. When considering mathematical input, however, handwritten math interfaces have to deal with new problems and issues not found in natural language. A popular area of interest that deals with math formulae recognition is math information retrieval (MIR).