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PODCAST

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.

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All Episodes

32:57

Interpretable Real Estate Recommendations

Data Skeptic ·

2025/09/22

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations" The discussion explores how the post-COVID real estate landscape has created a need for better recommendation systems that can introduce home buyers to emerging neighborhoods they might not know about. Dr. Mukherjee, explains how his team

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49:36

Why Am I Seeing This?

Data Skeptic ·

2025/09/08

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.

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44:42

Eco-aware GNN Recommenders

Data Skeptic ·

2025/08/30

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.

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17:45

Networks and Recommender Systems

Data Skeptic ·

2025/08/17

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

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34:10

Network of Past Guests Collaborations

Data Skeptic ·

2025/07/21

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

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46:14

The Network Diversion Problem

Data Skeptic ·

2025/07/06

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

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56:00

Complex Dynamic in Networks

Data Skeptic ·

2025/06/28

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

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36:46

Github Network Analysis

Data Skeptic ·

2025/06/22

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

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17:49

Networks and Complexity

Data Skeptic ·

2025/06/14

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.

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37:34

Actantial Networks

Data Skeptic ·

2025/06/01

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

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41:00

Graphs for Causal AI

Data Skeptic ·

2025/05/24

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

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41:50

Power Networks

Data Skeptic ·

2025/05/16

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44:12

Unveiling Graph Datasets

Data Skeptic ·

2025/05/08

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40:58

Network Manipulation

Data Skeptic ·

2025/04/30

In 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

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17:25

The Small World Hypothesis

Data Skeptic ·

2025/04/21

Kyle discusses the history and proof for the small world hypothesis.

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33:55

Thinking in Networks

Data Skeptic ·

2025/04/12

Kyle asks Asaf questions about the new network science course he is now teaching. The conversation delves into topics such as contact tracing, tools for analyzing networks, example use cases, and the importance of thinking in networks.

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42:55

Fraud Networks

Data Skeptic ·

2025/04/01

In this episode we talk with Bavo DC Campo, a data scientist and statistician, who shares his expertise on the intersection of actuarial science, fraud detection, and social network analytics. Together we will learn how to use graphs to fight against insurance fraud by uncovering hidden connections between fraudulent claims and bad actors. Key insights include how social network analytics can detect fraud rings by mapping relationships between policyholders, claims, and service providers, and how

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43:35

Criminal Networks

Data Skeptic ·

2025/03/17

In this episode we talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London, who explores how network science helps uncover criminal networks. Justin is also a member of the organizing committee of the satellite conference dealing with criminal networks at the network science conference in The Netherlands in June 2025. Listeners will learn how graph-based models assist law enforcement in analyzing missing data, identifying key figures in

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29:01

Graph Bugs

Data Skeptic ·

2025/03/10

In this episode today’s guest is Celine Wüst, a master’s student at ETH Zurich specializing in secure and reliable systems, shares her work on automated software testing for graph databases. Celine shows how fuzzing—the process of automatically generating complex queries—helps uncover hidden bugs in graph database management systems like Neo4j, FalconDB, and Apache AGE. Key insights include how state-aware query generation can detect critical issues like buffer overflows and crashes, the challenges

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44:15

Organizational Network Analysis

Data Skeptic ·

2025/03/03

In this episode, Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organizational structures using OrgXO, a tool that maps companies as networks rather than rigid hierarchies. Listeners will learn how analyzing workplace collaboration networks can reveal hidden influencers, organizational bottlenecks, and engagement levels, offering a data-driven approach to improving effectiveness and resilience. Key insights include how companies can

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584 results

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