Speaker: Spyros Blanas, The Ohio State University
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.
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.
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.
James She, Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology
Speaker: Oliver Kennedy, University at Buffalo
Besat Kassaie, PhD candidate
David R. Cheriton School of Computer Science
Speaker: Dan Suciu, University of Washington
Xi He joined the David R. Cheriton School of Computer Science as an assistant professor in March 2019. She received her BS in computer science and applied mathematics from the University of Singapore in 2012 and her PhD in computer science from Duke University in 2018. Her research is on privacy and security for big-data management and analysis.
Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science