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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.

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.

Location:

United States

Description:

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.

Language:

English


Episodes

Fault Tolerant Distributed Gradient Descent

2/22/2021
Nirupam Gupta, a Computer Science Post Doctoral Researcher at EDFL University in Switzerland, joins us today to discuss his work “Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.” Works Mentioned: https://arxiv.org/abs/2101.12316 Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent by Nirupam Gupta and Nitin H. Vaidya Conference Details: https://georgetown.zoom.us/meeting/register/tJ0sc-2grDwjEtfnLI0zPnN-GwkDvJdaOxXF

Duration:00:36:05

Decentralized Information Gathering

2/15/2021
Mikko Lauri, Post Doctoral researcher at the University of Hamburg, Germany, comes on the show today to discuss the work Information Gathering in Decentralized POMDPs by Policy Graph Improvements. Follow Mikko: @mikko_lauri Github https://laurimi.github.io/

Duration:00:32:55

Leaderless Consensus

2/5/2021
Balaji Arun, a PhD Student in the Systems of Software Research Group at Virginia Tech, joins us today to discuss his research of distributed systems through the paper “Taming the Contention in Consensus-based Distributed Systems.” Works Mentioned “Taming the Contention in Consensus-based Distributed Systems” by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindran https://www.ssrg.ece.vt.edu/papers/tdsc20-author-version.pdf “Fast Paxos” by Leslie...

Duration:00:27:23

Automatic Summarization

1/29/2021
Maartje ter Hoeve, PhD Student at the University of Amsterdam, joins us today to discuss her research in automated summarization through the paper “What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.” Works Mentioned “What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.” by Maartje der Hoeve, Juilia Kiseleva, and Maarten de...

Duration:00:27:56

Gerrymandering

1/22/2021
Brian Brubach, Assistant Professor in the Computer Science Department at Wellesley College, joins us today to discuss his work “Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives". WORKS MENTIONED: Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives by Brian Brubach, Aravind Srinivasan, and Shawn Zhao

Duration:00:34:07

Even Cooperative Chess is Hard

1/15/2021
Aside from victory questions like “can black force a checkmate on white in 5 moves?” many novel questions can be asked about a game of chess. Some questions are trivial (e.g. “How many pieces does white have?") while more computationally challenging questions can contribute interesting results in computational complexity theory. In this episode, Josh Brunner, Master's student in Theoretical Computer Science at MIT, joins us to discuss his recent paper Complexity of Retrograde and Helpmate...

Duration:00:23:07

Consecutive Votes in Paxos

1/11/2021
Eil Goldweber, a graduate student at the University of Michigan, comes on today to share his work in applying formal verification to systems and a modification to the Paxos protocol discussed in the paper Significance on Consecutive Ballots in Paxos. Works Mentioned : Previous Episode on Paxos https://dataskeptic.com/blog/episodes/2020/distributed-consensus Paper: On the Significance on Consecutive Ballots in Paxos by: Eli Goldweber, Nuda Zhang, and Manos Kapritsos Thanks to our...

Duration:00:30:09

Visual Illusions Deceiving Neural Networks

1/1/2021
Today on the show we have Adrian Martin, a Postdoctorial researcher from the Univeristy of Pompeu Fabra in Barcelona, Spain. He comes on the show today to discuss his research from the paper “Convolutional Neural Networks can be Decieved by Visual Illusions.” Workes Mentioned in Paper: “Convolutional Neural Networks can be Decieved by Visual Illusions.” by Alexander Gomez-Villa, Adrian Martin, Javier Vazquez-Corral, and Marcelo Bertalmio Examples: Snake...

Duration:00:33:41

Earthquake Detection with Crowd-sourced Data

12/25/2020
Have you ever wanted to hear what an earthquake sounds like? Today on the show we have Omkar Ranadive, Computer Science Masters student at NorthWestern University, who collaborates with Suzan van der Lee, an Earth and Planetary Sciences professor at Northwestern University, on the crowd-sourcing project Earthquake Detective. Email Links: Suzan: suzan@earth.northwestern.edu Omkar: omkar.ranadive@u.northwestern.edu Works Mentioned: Paper: Applying Machine Learning to Crowd-sourced Data...

Duration:00:29:26

Byzantine Fault Tolerant Consensus

12/22/2020
Byzantine fault tolerance (BFT) is a desirable property in a distributed computing environment. BFT means the system can survive the loss of nodes and nodes becoming unreliable. There are many different protocols for achieving BFT, though not all options can scale to large network sizes. Ted Yin joins us to explain BFT, survey the wide variety of protocols, and share details about HotStuff.

Duration:00:35:31

Alpha Fold

12/11/2020
Kyle shared some initial reactions to the announcement about Alpha Fold 2's celebrated performance in the CASP14 prediction. By many accounts, this exciting result means protein folding is now a solved problem. Thanks to our sponsors! Brilliant brilliant.org/dataskepticBetterhelpbetterhelp.com/dataskeptic

Duration:00:23:13

Arrow's Impossibility Theorem

12/4/2020
Above all, everyone wants voting to be fair. What does fair mean and how can we measure it? Kenneth Arrow posited a simple set of conditions that one would certainly desire in a voting system. For example, unanimity - if everyone picks candidate A, then A should win! Yet surprisingly, under a few basic assumptions, this theorem demonstrates that no voting system exists which can satisfy all the criteria. This episode is a discussion about the structure of the proof and some of its...

Duration:00:26:18

Face Mask Sentiment Analysis

11/27/2020
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...

Duration:00:41:10

Counting Briberies in Elections

11/20/2020
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...

Duration:00:37:53

Sybil Attacks on Federated Learning

11/13/2020
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...

Duration:00:31:31

Differential Privacy at the US Census

11/6/2020
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...

Duration:00:29:41

Distributed Consensus

10/30/2020
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...

Duration:00:27:43

ACID Compliance

10/23/2020
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/dataskeptic Brilliant.org/dataskeptic

Duration:00:23:46

National Popular Vote Interstate Compact

10/16/2020
Patrick Rosenstiel joins us to discuss the The National Popular Vote.

Duration:00:30:35

Defending the p-value

10/12/2020
Yudi Pawitan joins us to discuss his paper Defending the P-value.

Duration:00:29:50