Events - 2019

Monday, October 28, 2019 — 10:30 AM EDT

Speaker: Saif M. Mohammad, National Research Council Canada

Friday, October 25, 2019 — 2:00 PM EDT

Speaker: Marco Serafini, University of Massachusetts Amherst

Friday, October 18, 2019 — 10:30 AM EDT

Richard Zanibbi, Director, Document and Pattern Recognition Lab
Rochester Institute of Technology

Tuesday, September 10, 2019 — 10:30 AM EDT

Speaker: Guoliang Li, Tsinghua University

Abstract:   

Wednesday, August 7, 2019 — 2:30 PM EDT

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). 

Wednesday, August 7, 2019 — 10:30 AM EDT

Speaker: Spyros Blanas, The Ohio State University

Wednesday, July 31, 2019 — 12:15 PM EDT

PLEASE NOTE: THIS TALK IS CANCELLED

Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science

Wednesday, June 12, 2019 — 12:15 PM EDT

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, May 29, 2019 — 12:15 PM EDT

Besat Kassaie, PhD candidate
David R. Cheriton School of Computer Science

Friday, May 24, 2019 — 10:30 AM EDT

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.

Tuesday, May 21, 2019 — 9:30 AM EDT

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.

Monday, May 13, 2019 — 10:30 AM EDT

Speaker: Oliver Kennedy, University at Buffalo

Friday, April 26, 2019 — 3:30 PM EDT

James She, Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology

Monday, April 15, 2019 — 3:00 PM EDT

Speaker: Dan Suciu, University of Washington

Wednesday, April 10, 2019 — 9:00 AM EDT

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

Tuesday, April 9, 2019 — 10:30 AM EDT

Ian Soboroff, Leader, Retrieval Group
National Institute of Standards and Technology

Tuesday, March 12, 2019 — 11:00 AM EDT

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

Wednesday, February 27, 2019 — 12:15 PM EST

Alexey Karyakin, PhD candidate
David R. Cheriton School of Computer Science

Energy consumed by the main memory in existing database systems does not effectively scale down with lower system utilization, both in terms of actual memory usage and load conditions. At the same time, main memory represents a sizable portion of the total server energy footprint, which makes it an outlier as the rest of the system moves towards energy proportionality. 

We introduce DimmStore, a prototype main-memory database system that addresses the problem of memory energy consumption.

Wednesday, February 13, 2019 — 12:15 PM EST

Chang Ge, PhD candidate
David R. Cheriton School of Computer Science

Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for differentially private query answering place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quantity they care about, namely accuracy of query answers.

Monday, January 14, 2019 — 10:30 AM EST

Speaker: Verena Kantere, University of Ottawa

Abstract: Big Data analytics in science and industry are performed on a range of heterogeneous data stores, both traditional and modern, and on a diversity of query engines. Workflows are difficult to design and implement since they span a variety of systems. To reduce development time and processing costs, some automation is needed. In this talk we will present a new platform to manage analytics workflows.

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