Data Skeptic

As the COVID-19 pandemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask-wearing via Twitter. What does this data tell us about public opinion? How does it vary by demographic? What, if anything, can make people change their minds?

Today we speak to, Neil Yeung and Jonathan Lai, Undergraduate students in the Department of Computer Science at the University of Rochester, and Professor of Computer Science, Jiebo-Luoto to discuss their recent paper. Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.

Works Mentioned
https://arxiv.org/abs/2011.00336

Emails:
Neil Yeung
nyeung@u.rochester.edu

Jonathan Lia
jlai11@u.rochester.edu

Jiebo Luo
jluo@cs.rochester.edu

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Direct download: face-mask-sentiment-analysis.mp3
Category:general -- posted at: 10:56am PST

Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper “On the Robustness of Winners: Counting Briberies in Elections.”

Links Mentioned:
https://www.akt.tu-berlin.de/menue/team/boehmer_niclas/

Works Mentioned:
“On the Robustness of Winners: Counting Briberies in Elections.” by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier

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Direct download: counting-briberies-in-elections.mp3
Category:general -- posted at: 8:26am PST

Clement Fung, a Societal Computing PhD student at Carnegie Mellon University, discusses his research in security of machine learning systems and a defense against targeted sybil-based poisoning called FoolsGold.

Works Mentioned:
The Limitations of Federated Learning in Sybil Settings

Twitter:

@clemfung

Website:
https://clementfung.github.io/

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Direct download: sybil-attacks-on-federated-learning.mp3
Category:general -- posted at: 10:25am PST

Simson Garfinkel, Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau, discusses his work modernizing the Census Bureau disclosure avoidance system from private to public disclosure avoidance techniques using differential privacy. Some of the discussion revolves around the topics in the paper Randomness Concerns When Deploying Differential Privacy.
 
WORKS MENTIONED:
 
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Direct download: differential-privacy-at-the-us-census.mp3
Category:general -- posted at: 8:13am PST

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