Podcast
Data Skeptic
Hosted by Kyle Polich · EN
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.
33episodes
Episodes
Newest firstAll episodes
Complex Dynamic in Networks
Jun 28, 202500:56:00Tap to summarizeIn 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
Transcribe →
Github Network Analysis
Jun 22, 202500:36:46Tap to summarizeIn 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.
Transcribe →Networks and Complexity
Jun 14, 202500:17:49Tap to summarizeIn 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.
Transcribe →Actantial Networks
Jun 1, 202500:37:34Tap to summarizeIn 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
Transcribe →Graphs for Causal AI
May 24, 202500:41:00Tap to summarizeHow 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
Transcribe →Power Networks
May 16, 202500:41:50Tap to summarizeNo description available
Transcribe →Unveiling Graph Datasets
May 8, 202500:44:12Tap to summarizeNo description available
Transcribe →Network Manipulation
Apr 30, 202500:40:58Tap to summarizeIn this episode we talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, with a focus on detecting coordinated manipulation campaigns on social media. Key insights include how coordinated reply attacks target influential figures like journalists and politicians, how machine learning models can detect these inauthentic campaigns using structural and behavioral features, and how deletion patterns reveal efforts to evade moderation or manipulate engagement metrics. Follow our guest X/Twitter Google Scholar Papers in focus Coordinated Reply Attacks in Influence Operations: Characterization and Detection ,2025 Manipulating Twitter through Deletions,2022
Transcribe →
Memory in Chess
Feb 12, 2024Tap to summarizeOn today’s show, we are joined by our co-host, Becky Hansis-O’Neil. Becky is a Ph.D. student at the University of Missouri, St Louis, where she studies bumblebees and tarantulas to understand their learning and cognitive work. She joins us to discuss the paper: Perception in Chess. The paper aimed to understand how chess players perceive the positions of chess pieces on a chess board. She discussed the findings paper. She spoke about situations where grandmasters had better recall of chess positions than beginners and situations where they did not. Becky and Kyle discussed the use of chess engines for cheating. They also discussed how chess players use chunking. Becky discussed some approaches to studying chess cognition, including eye tracking, EEG, and MRI. ## Paper in Focus Perception in chess ## Resources Detecting Cheating in Chess with Ken Regan
Transcribe →
k-means Image Segmentation
Feb 22, 2022Tap to summarizeLinh Da joins us to explore how image segmentation can be done using k-means clustering. Image segmentation involves dividing an image into a distinct set of segments. One such approach is to do this purely on color, in which case, k-means clustering is a good option. Check out our website for extended show notes and images! Thanks to our Sponsors: Visit Weights and Biases mention Data Skeptic when you request a demo! & Nomad Data In the image below, you can see the k-means clustering segmentation results for the same image with the values of 2, 4, 6, and 8 for k.
Transcribe →