- alle zwei WochenErscheinungsrhythmus
- 34 mintypische Länge
- vor 3 Tagenletzte Folge
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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TastePrint
What if we could 3D print food with customized flavors and textures? Yamato Miyatake joins us to explore how digital food fabrication, human-computer interaction, and AI could transform the future of cooking and dining.
Gestures Around the Ear
The next evolution of wearable technology may not require new screens or controllers, but new ways to interact with the devices we already wear. Shimon explores how gesture-sensing earbuds could transform virtual reality and wearable comput
Implicit Interactions
How do we design robots and autonomous vehicles that understand the unwritten rules of human behavior? Kyle speaks with Cornell Tech professor Wendy Ju about implicit interaction, "Wizard of Oz" prototyping, and what studying pedestrians, s
The Lived Informatics Model
The data we collect about ourselves can tell us a lot—but only if the technology collecting it actually fits into our lives. Daniel Epstein explores personal informatics, from fitness trackers and food journals to baby tracking and AI, and
Recommender Systems Today and Tomorrow
In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control. From fake reviews and shilling attacks to explainable recommendations and
Recommender Systems Optimization Goals
In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores enga
Recommender Systems Origin Story
Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matr
Social Choice for Fair Recommendations
Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely fo
News Recommendations
News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world. In this episode, Kyle Polich speaks with Andreea Iana about responsible AI, filter bubbles, multilingual news recomm
Give Users the Wheel
What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional re
AutoLike
How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize
Student Spotlight: Aaron Payne, Data Analyst
Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They unpack a real-world forecasting project
The Future is Agentic in Recommender Systems
Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters. They unpack key dimensions of resp
Book Ratings and Recommendations
Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often matter more than differences between books.
Disentanglement and Interpretability in Recommender Systems
Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommender systems, finding that while disentanglement strongly correlates with interpretability, it doesn't
Collective Altruism in Recommender Systems
Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic
Niche vs Mainstream
Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and niche algorithms. His research shows specialized recommenders improve utility for
Healthy Friction in Job Recommender Systems
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. Roan discusses his research on creating AI-
Fairness in PCA-Based Recommenders
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 Enterprise and Society. David shares insights
Video Recommendations in Industry
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 intersection of human curation and machine le
Eye Tracking in Recommender Systems
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, Santiago explains the mechanics of eye
Cracking the Cold Start Problem
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 apply XGBoost to tabular data, recommender
Designing Recommender Systems for Digital Humanities
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 Technology. Florian is working on Monaster
DataRec Library for Reproducible in Recommend Systems
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 research. Guest Alberto Carlo Maria Mancino,
Shilling Attacks on Recommender Systems
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 algorithms. The conversation centers on shilling
Music Playlist Recommendations
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 systems. She explores three key types of fair
Sustainable Recommender Systems for Tourism
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 how these systems can address exposure
Interpretable Real Estate Recommendations
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
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 "rec
