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Please note: This Waterloo CPI seminar will be giving online.

Nikolaos Makriyannis, Cryptography researcher
Fireblocks

Building on the Gennaro & Goldfeder and Lindell & Nof protocols (CCS '18), we present two threshold ECDSA protocols, for any number of signatories and any threshold, that improve as follows over the state of the art: 

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

Yilun Bai, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Daniel M. Berry

Please note: This PhD seminar will be given online.

Jumyung “JC” Chang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Christopher Batty

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. 

Tuesday, December 8, 2020 11:00 am - 11:00 am EST (GMT -05:00)

Mathematics Education Seminar • Remote Teaching Adventures Inspired by COVID-19

Please note: This seminar will be given online.

Petra Bonfert-Taylor
Dartmouth College

While teaching remotely is more common in other parts of the world, at Dartmouth College and its Thayer School of Engineering all classes used to be taught in person. Until March of 2020 that is when COVID-19 gave us 2 weeks’ notice to switch to 100% remote instruction.

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

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

Supervisor: Professor Jesse Hoey

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.