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Artwork for Data Skeptic

Data Skeptic

Kyle Polich
Machine Learning
Recommender Systems
Large Language Models
Network Science
Artificial Intelligence
Data Science
Animal Behavior
Computer Vision
Network Analysis
Graph Theory
Social Network Analysis
Generative AI
Community Detection
Social Media
Graph Neural Networks
Criminal Networks
Graph Databases
Lemurs
Natural Language Processing
Fraud Detection

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.

PublishesTwice monthlyEpisodes600Founded12 years ago
Number of ListenersCategories
MathematicsTechnologyScience

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Artwork for Data Skeptic

Latest Episodes

Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters. They unpack key dimensions of responsible AI... more

Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often matter more than differences between books. The episo... more

Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommender systems, finding that while disentanglement strongly correlates with interpretability, it doesn't consistent... more

Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest m... more

Key Facts

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Recent Guests

Hannes Rosenbusch
Professor in the Department of Psychological Methods, University of Amsterdam
University of Amsterdam
Episode: Book Ratings and Recommendations
Ervin Dervishay
PhD student focusing on representation learning in recommender systems; guest on the episode
University of Copenhagen
Episode: Disentanglement and Interpretability in Recommender Systems
Ekaterina Filadova
PhD student at MIT studying strategic learning.
MIT
Episode: Collective Altruism in Recommender Systems
Roan Schellingerhout
Fourth year PhD student at Maastricht University studying explainable multi-stakeholder recommender systems.
Maastricht University
Episode: Healthy Friction in Job Recommender Systems
Cory Zechmann
Content curator and music blogger, who has worked with streaming services and has his own music blog.
Slink TV, Silence Nogood
Episode: Video Recommendations in Industry
Santiago de Leon Martínez
Spanish-American researcher at the Kempelin Institute of Intelligent Technologies and Brno University of Technology, focusing on eye tracking and AI.
Kempelin Institute of Intelligent Technologies
Episode: Eye Tracking in Recommender Systems
Alberto Carlo Mario Mancino
Postdoc researcher devoted to recommender systems
SysInflab Laboratory
Episode: DataRec Library for Reproducible in Recommend Systems
Aditya Chichani
Senior Machine Learning Engineer at Walmart with a focus on recommender systems.
Walmart
Episode: Shilling Attacks on Recommender Systems
Ashmi Banerjee
Doctoral candidate at Technical University of Munich specializing in recommender systems and sustainable tourism.
Technical University of Munich
Episode: Sustainable Recommender Systems for Tourism

Hosts

Ekaterina Filadova
Host providing insights on data-driven discussions and expert interviews focusing on data science and its applications.
Kyle
A knowledgeable host guiding discussions in the fields of machine learning and AI, known for his critical approach to evaluating data methods and technologies.

Reviews

4.6 out of 5 stars from 1k ratings
  • LOVE THE SHOW

    I just absolutely love the show and I’m just wondering if maybe you can cover as a topic sub polynomial compute for graph networks?

    Apple Podcasts
    5
    Cdascientist
    United States3 months ago
  • A gem of a podcast

    I am not a data scientist, but I very much enjoy the podcast! It is fascinating, the interviewer’s really know their field and it has provided me with topics that could be interesting to follow-up. If you are interested in technology, or a student, then this is a podcast that is well worth following and listening to. Top marks!

    Apple Podcasts
    5
    AH24Z
    United Kingdoma year ago
  • great

    great

    Apple Podcasts
    4
    JVo12
    Canada3 years ago
  • Nice podcast

    Listen it to learn industry English and new technology . Really good for me

    Apple Podcasts
    5
    jiazhi chao
    China3 years ago
  • Great resource

    A colleague introduced me to Data Skeptic last year and I’ve been enjoying the episodes. Kyle’s good at covering topics from many levels of data science understanding—his mini series with a non-data scientist are a great way to learn the basics!

    Apple Podcasts
    5
    calzone.onsets
    United States3 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Listeners appreciate the engaging and informative discussions on complex data-related topics.
Many praise the host's ability to simplify dense subjects, making them accessible to a broader audience.
Some feedback highlights the professionalism and depth of the interviews with expert guests.
Concerns have been raised about audio quality during certain episodes, detracting from the listening experience.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#207
United States/Technology
Apple Podcasts
#243
United Kingdom/Technology
Apple Podcasts
#14
Saudi Arabia/Technology
Apple Podcasts
#22
Colombia/Technology
Apple Podcasts
#62
Russia/Technology
Apple Podcasts
#116
Philippines/Technology

Talking Points

Recent interactions between the hosts and their guests.

Disentanglement and Interpretability in Recommender Systems
Q: How do you see LLMs affecting your work in recommender systems?
LLMs are increasingly integrated to denoise noisy implicit data, help identify relevant items in user histories, and potentially improve downstream tasks; however, they also introduce challenges around explainability and reproducibility, and currently their impact varies by task and data.
Disentanglement and Interpretability in Recommender Systems
Q: What is representation learning?
Representation learning is the process by which a model learns to build its own internal representations from raw data, rather than relying on manually engineered features; these learned representations are then used for downstream prediction tasks.
Niche vs Mainstream
Q: How are you planning to move forward with your research?
The next study involves allowing users to control what is on their algorithm, emphasizing user agency in recommendation systems.
Niche vs Mainstream
Q: How do you look at it practically?
The simulation environment allows for testing how niche and mainstream recommenders affect user experience and whether users prefer one over the other.
Niche vs Mainstream
Q: What would it mean for a recommender system to be unfair?
Unfairness in recommender systems can arise from issues such as representative fairness and allocative fairness, impacting how different groups experience recommendations.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

Listeners per Episode
Gender Skew
Location
Interests
Professions
Age Range
Household Income
Social Media Reach

Frequently Asked Questions About Data Skeptic

What is Data Skeptic about and what kind of topics does it cover?

This podcast features insightful discussions and interviews on topics surrounding data science, machine learning, artificial intelligence, and statistics. Focusing on the application of critical thinking and the scientific method, each episode evaluates the claims and methods used in data collection and analysis, ensuring accuracy and relevancy. Listeners can expect a blend of expert opinions, case studies, and research findings that highlight the importance of rigorous investigation in the field of data-driven technologies. The podcast is especially notable for its emphasis on fairness, ethics, and the interaction between technology and user experiences, making it a rich resource for tech enthusiasts and professionals alike.

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How many listeners does Data Skeptic get?

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How many subscribers and views does Data Skeptic have?

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Which podcasts are similar to Data Skeptic?

These podcasts share a similar audience with Data Skeptic:

1. Super Data Science: ML & AI Podcast with Jon Krohn
2. Practical AI
3. DataFramed
4. Latent Space: The AI Engineer Podcast
5. Software Engineering Daily

How many episodes of Data Skeptic are there?

Data Skeptic launched 12 years ago and published 600 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

How do I contact Data Skeptic?

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What guests have appeared on Data Skeptic?

Recent guests on Data Skeptic include:

1. Hannes Rosenbusch
2. Ervin Dervishay
3. Ekaterina Filadova
4. Roan Schellingerhout
5. Cory Zechmann
6. Santiago de Leon Martínez
7. Alberto Carlo Mario Mancino
8. Aditya Chichani

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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