Data Skeptic

Kyle Polich

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.

  1. Why Am I Seeing This?

    8. 9.

    Why Am I Seeing This?

    In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri Ognibene introduce their innovative "recommender neutral user model" for inferring the influence of opaque algorithms.

    50 min
  2. Eco-aware GNN Recommenders

    30. 8.

    Eco-aware GNN Recommenders

    In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Recommendations" and explores how we can measure and reduce the environmental impact of recommender systems without sacrificing performance.

    45 min
  3. Networks and Recommender Systems

    17. 8.

    Networks and Recommender Systems

    Kyle reveals the next season's topic will be "Recommender Systems".  Asaf shares insights on how network science contributes to the recommender system field.

    18 min
  4. Network of Past Guests Collaborations

    21. 7.

    Network of Past Guests Collaborations

    Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.

    34 min
  5. The Network Diversion Problem

    6. 7.

    The Network Diversion Problem

    In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing on specific structural aspects of the input. This framework allows researchers to solve NP-complete problems more efficiently when certain parameters, like the structure of the graph, are "well-behaved". At the center of the discussion is the network diversion problem, where the goal isn’t to block all routes between two points in a network, but to force flow - such as traffic, electricity, or data - through a specific path. While this problem appears deceptively similar to the classic "Min.Cut/Max.Flow" algorithm, it turns out to be much harder and, in general, its complexity is still unknown. Parameterized complexity plays a key role here by offering ways to make the problem tractable under constraints like low treewidth or planarity, which often exist in real-world networks like road systems or utility grids. Listeners will learn how vulnerability measures help identify weak points in networks, such as geopolitical infrastructure (e.g., gas pipelines like Nord Stream). Follow out guest: Pål Grønås Drange

    46 min
  6. Complex Dynamic in Networks

    28. 6.

    Complex Dynamic in Networks

    In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads.  Our guest, Professor Baruch Barzel of Bar-Ilan University, is a leading researcher in network dynamics and complex systems ranging from biology to infrastructure and beyond.  BarzelLab BarzelLab on Youtube Paper in focus: Universality in network dynamics, 2013

    56 min
  7. Github Network Analysis

    22. 6.

    Github Network Analysis

    In this episode we'll discuss how to use Github data as a network to extract insights about teamwork. Our guest, Gabriel Ramirez, manager of the notifications team at GitHub, will show how to apply network analysis to better understand and improve collaboration within his engineering team by analyzing GitHub metadata - such as pull requests, issues, and discussions - as a bipartite graph of people and projects. Some insights we'll discuss are how network centrality measures (like eigenvector and betweenness centrality) reveal organizational dynamics, how vacation patterns influence team connectivity, and how decentralizing communication hubs can foster healthier collaboration.  Gabriel’s open-source project, GH Graph Explorer, enables other managers and engineers to extract, visualize, and analyze their own GitHub activity using tools like Python, Neo4j, Gephi and LLMs for insight generation, but always remember – don't take the results on face value. Instead, use the results to guide your qualitative investigation.

    37 min

  8. 14. 6.

    Networks and Complexity

    In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory.  In complexity theory, we are about the runtime of an algorithm based on its input size.  For many graph problems, the interesting questions we want to ask take longer and longer to answer!  This episode provides the fundamental vocabulary and signposts along the path of exploring the intersection of graph theory and computational complexity theory.

    18 min
Zobrazit vše (583)

Moderují a účinkují

Informace

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.

Informace

  • Autor
    Kyle Polich
  • Aktivní
    2014 - 2025
  • Díly
    583
  • Hodnocení
    Nezávadné
  • Copyright
    © Creative Commons Attribution License 3.0
  • Web pořadu
    Data Skeptic

Také by se vám mohlo líbit