Data Skeptic

Kyle Polich
  • 4.4 (473)
  • 科技
  • 两周一更
Data Skeptic

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. The Network Diversion Problem

    5天前

    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 分钟
  2. Complex Dynamic in Networks

    6月28日

    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 分钟
  3. Github Network Analysis

    6月22日

    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 分钟

  4. 6月14日

    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 分钟

  5. 6月1日

    Actantial Networks

    In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them.  The one who will present these networks is our guest Armin Pournaki, a joint PhD candidate at the Max Planck Institute for Mathematics in the Sciences and the Laboratoire Lattice (ENS-PSL), who specializes in computational social science, where he develops methods to extract and analyze political narratives using natural language processing and network science.  Armin explains how these methods can expose conflicting narratives around the same events, as seen in debates on COVID-19, climate change, or the war in Ukraine. Listeners will also discover how this approach helps make large-scale discourse—from millions of tweets or political speeches—more transparent and interpretable, offering tools for studying polarization, issue alignment, and narrative-driven persuasion in digital societies. Follow our guest Armin Pournaki's Webpage Twitter/X Bluesky Papers in focus How influencers and multipliers drive polarization and issue alignment on Twitter/X, 2025 A graph-based approach to extracting narrative signals from public discourse, 2024

    38 分钟

  6. 5月24日

    Graphs for Causal AI

    How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise.  Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbolic AI to represent and reason about complex relationships. This involves creating causal ontologies, incorporating the "weight" or strength of causal relationships and hyperrelations. This field has many practical applications such as for AI explainability, healthcare and autonomous driving. Follow our guest Utkarshani Jaimini's Webpage Linkedin Papers in focus CausalLP: Learning causal relations with weighted knowledge graph link prediction, 2024 HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph, 2024

    41 分钟

  7. 5月16日

    Power Networks

    Data Skeptic

    42 分钟

  8. 5月8日

    Unveiling Graph Datasets

    Data Skeptic

    44 分钟
查看全部 580 集

主持人与嘉宾

4.4
共 5 分
473 个评分

关于

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.

信息

  • 创作者
    Kyle Polich
  • 活跃年份
    2014年 - 2025年
  • 单集
    580
  • 分级
    儿童适宜
  • 版权
    © Creative Commons Attribution License 3.0
  • 节目网站
    Data Skeptic

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