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.
📻 Siste episoder av Data Skeptic
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Healthy Friction in Job Recommender Systems (00:26:37)
In this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable multi-stakeholder recommender systems for job recruitment. Roa...
Fairness in PCA-Based Recommenders (00:49:59)
In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enter...
Video Recommendations in Industry (00:38:16)
In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the inter...
Eye Tracking in Recommender Systems (00:52:08)
In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University,...
Cracking the Cold Start Problem (00:39:57)
In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply ap...
Designing Recommender Systems for Digital Humanities (00:36:48)
In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Te...
DataRec Library for Reproducible in Recommend Systems (00:32:48)
In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems resea...
Shilling Attacks on Recommender Systems (00:34:48)
In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithm...
Music Playlist Recommendations (00:52:29)
In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation syste...
Bypassing the Popularity Bias (00:34:33)
Sustainable Recommender Systems for Tourism (00:38:02)
In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates...
Interpretable Real Estate Recommendations (00:32:57)
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-Interpr...
Why Am I Seeing This? (00:49:36)
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 ...
Eco-aware GNN Recommenders (00:44:42)
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...
Networks and Recommender Systems (00:17:45)
Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field.
Network of Past Guests Collaborations (00:34:10)
Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.
The Network Diversion Problem (00:46:14)
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...
Complex Dynamic in Networks (00:56:00)
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 inf...
Github Network Analysis (00:36:46)
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 ne...
Networks and Complexity (00:17:49)
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....
Graphs for Causal AI (00:41:00)
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 ex...
Power Networks (00:41:50)
Unveiling Graph Datasets (00:44:12)
Network Manipulation (00:40:58)
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 soci...
The Small World Hypothesis (00:17:25)
Kyle discusses the history and proof for the small world hypothesis.
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