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Data Skeptic

Author: Kyle Polich

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The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
341 Episodes
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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 Thanks to our sponsors! Springboard School of Data offers a comprehensive career program encompassing data science, analytics, engineering, and Machine Learning. All courses are online and tailored to fit the lifestyle of working professionals. Up to 20 Data Skeptic listeners will receive $500 scholarships. Apply today at springboard.com/datasketpic Check out Brilliant's group theory course to learn about object-oriented design! Brilliant is great for learning something new or to get an easy-to-look-at review of something you already know. Check them out a Brilliant.org/dataskeptic to get 20% off of a year of Brilliant Premium!
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 Thanks to our sponsors: Springboard School of Data: Springboard is a comprehensive end-to-end online data career program. Create a portfolio of projects to spring your career into action. Learn more about how you can be one of twenty $500 scholarship recipients at springboard.com/dataskeptic. This opportunity is exclusive to Data Skeptic listeners. (Enroll with code: DATASK) Nord VPN: Protect your home internet connection with unlimited bandwidth. Data Skeptic Listeners-- take advantage of their Black Friday offer: purchase a 2-year plan, get 4 additional months free. nordvpn.com/dataskeptic (Use coupon code DATASKEPTIC)
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/ Thanks to our sponsors: Brilliant - Online learning platform. Check out Geometry Fundamentals! Visit Brilliant.org/dataskeptic for 20% off Brilliant Premium! BetterHelp - Convenient, professional, and affordable online counseling. Take 10% off your first month at betterhelp.com/dataskeptic
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: “Calibrating Noise to Sensitivity in Private Data Analysis” by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith https://link.springer.com/chapter/10.1007/11681878_14 "Issues Encountered Deploying Differential Privacy" by Simson L Garfinkel, John M Abowd, and Sarah Powazek https://dl.acm.org/doi/10.1145/3267323.3268949 "Randomness Concerns When Deploying Differential Privacy" by Simson L. Garfinkel and Philip Leclerc https://arxiv.org/abs/2009.03777 Check out: https://simson.net/page/Differential_privacy   Thank you to our sponsor, BetterHelp. Professional and confidential in-app counseling for everyone. Save 10% on your first month of services with www.betterhelp.com/dataskeptic
Distributed Consensus

Distributed Consensus

2020-10-3027:44

Computer Science research fellow of Cambridge University, Heidi Howard discusses Paxos, Raft, and distributed consensus in distributed systems alongside with her work “Paxos vs. Raft: Have we reached consensus on distributed consensus?” She goes into detail about the leaders in Paxos and Raft and how The Raft Consensus Algorithm actually inspired her to pursue her PhD. Paxos vs Raft paper: https://arxiv.org/abs/2004.05074 Leslie Lamport paper “part-time Parliament” https://lamport.azurewebsites.net/pubs/lamport-paxos.pdf Leslie Lamport paper "Paxos Made Simple" https://lamport.azurewebsites.net/pubs/paxos-simple.pdf Twitter : @heidiann360 Thank you to our sponsor Monday dot com! Their apps challenge is still accepting submissions! find more information at monday.com/dataskeptic
ACID Compliance

ACID Compliance

2020-10-2323:471

Linhda joins Kyle today to talk through A.C.I.D. Compliance (atomicity, consistency, isolation, and durability). The presence of these four components can ensure that a database’s transaction is completed in a timely manner. Kyle uses examples such as google sheets, bank transactions, and even the game rummy cube.   Thanks to this week's sponsors: Monday.com - Their Apps Challenge is underway and available at monday.com/dataskeptic Brilliant - Check out their Quantum Computing Course, I highly recommend it! Other interesting topics I’ve seen are Neural Networks and Logic. Check them out at Brilliant.org/dataskeptic
Patrick Rosenstiel joins us to discuss the The National Popular Vote.
Defending the p-value

Defending the p-value

2020-10-1230:001

Yudi Pawitan joins us to discuss his paper Defending the P-value.
Retraction Watch

Retraction Watch

2020-10-0532:04

Ivan Oransky joins us to discuss his work documenting the scientific peer-review process at retractionwatch.com.  
Crowdsourced Expertise

Crowdsourced Expertise

2020-09-2127:50

Derek Lim joins us to discuss the paper Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.  
Neil Johnson joins us to discuss the paper The online competition between pro- and anti-vaccination views.
Consensus Voting

Consensus Voting

2020-09-0722:57

Mashbat Suzuki joins us to discuss the paper How Many Freemasons Are There? The Consensus Voting Mechanism in Metric Spaces. Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)
Voting Mechanisms

Voting Mechanisms

2020-08-3127:28

Steven Heilman joins us to discuss his paper Designing Stable Elections. For a general interest article, see: https://theconversation.com/the-electoral-college-is-surprisingly-vulnerable-to-popular-vote-changes-141104 Steven Heilman receives funding from the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
False Consensus

False Consensus

2020-08-2433:06

Sami Yousif joins us to discuss the paper The Illusion of Consensus: A Failure to Distinguish Between True and False Consensus. This work empirically explores how individuals evaluate consensus under different experimental conditions reviewing online news articles. More from Sami at samiyousif.org Link to survey mentioned by Daniel Kerrigan: https://forms.gle/TCdGem3WTUYEP31B8
In this solo episode, Kyle overviews the field of fraud detection with eCommerce as a use case.  He discusses some of the techniques and system architectures used by companies to fight fraud with a focus on why these things need to be approached from a real-time perspective.
Listener Survey Review

Listener Survey Review

2020-08-1123:12

In this episode, Kyle and Linhda review the results of our recent survey. Hear all about the demographic details and how we interpret these results.
Moses Namara from the HATLab joins us to discuss his research into the interaction between privacy and human-computer interaction.
Mark Glickman joins us to discuss the paper Data in the Life: Authorship Attribution in Lennon-McCartney Songs.
Erik Härkönen joins us to discuss the paper GANSpace: Discovering Interpretable GAN Controls. During the interview, Kyle makes reference to this amazing interpretable GAN controls video and it’s accompanying codebase found here. Erik mentions the GANspace collab notebook which is a rapid way to try these ideas out for yourself.
David Ifeoluwa Adelani joins us to discuss Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection.
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Comments (17)

ncooty

@6:00: The threshold for statistical significance does not "depend on the outcome." It raises a red flag even to hear someone say that, especially the host of a "data science" podcast. (Of course, if he knew what he was talking about, he'd be a "statistician" instead.) He might more accurately have said that any such estimate of the minimum sample size depends on the number of planned comparisons and the assumed effect size for each measured effect. Confusion about this should disqualify someone from hosting such a podcast.

Aug 23rd
Reply

ncooty

@2:19: Too much interpretation as if respondents were randomly sampled. Respondents self-selected.

Aug 23rd
Reply

Antonio Andrade

thanks so much for sharing the results

Aug 12th
Reply

ncooty

@1:03: It doesn't "beg the question"; it "raises the question." To "beg the question" is to commit a logical fallacy in which one assumes the conclusion.

Jun 15th
Reply

Benjamin Weckerle

Is the spin-off / journal club podcast on castbox?

Jun 2nd
Reply

Platte Gruber

KILLER intro, awesome work!

Jan 8th
Reply (1)

Marco Gorelli

"I find it stunning that people don't do that. The only thing I can think of is that there's just the lack of focused time. There's so many things we could spend our time on now we spend a little on all of them and we don't have depth that we need. A lot of people will come to a conference or something like that just to be away from work and only focus on one thing. Unfortunately they also bring their phone and completely break that paradigm. " ouch

Dec 8th
Reply

Bhavul Gauri

Brilliantly put!

Aug 27th
Reply

Akshay Shirsath

Thoughtful episode.

Jun 8th
Reply (1)

Achint Verma

A very very high level introduction to Kalman Filters. You could have talked about the matrices.

Mar 29th
Reply

Troy Kirin

Golden, thanks for this!

Mar 19th
Reply

Vannucci Santos

Why the guy talking about ethics was so evasive?

Dec 25th
Reply

Anna Malahova

I love everything about this podcast channel! It is easy to listen to, easy to understand without data science background, interesting topics and examples of situations where to apply. Very enjoyable and entertaining delivery. Informative show notes that help you to recall what the episode was about even after a while. Really can't think about any downsides. I am listening to all episodes starting from early days like an audiobook, love how music into evolved over time.

Nov 24th
Reply

Abdul Wahab Abrar

What about using Deep Learning techniques directly and integrate it with Neuroscience

Feb 12th
Reply

Giancarlo Vercellino

rilevante dal 23esimo minuto

Dec 12th
Reply
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