Radio and PodcastLive Radio & PodcastsOpening Radio and Podcast...
Radio and PodcastLive Radio & PodcastsFetching podcast shows and categories...
Radio and PodcastLive Radio & PodcastsFetching podcast episodes...
Data Skeptic is a data science podcast exploring machine learning, statistics, artificial intelligence, and other data topics through short tutorials and interviews with domain experts.
Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to t...
Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how...
Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professio...
Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommende...
Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively co...
Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose betw...
In this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable mu...
In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Profess...
In this episode, Kyle Polich sits down with Cory Zechmann , a content curator working in streaming television with 16 years of experience ru...
In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As...
In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional mach...
In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian At...
In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring repr...
In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Wal...
In this episode, Rebecca Salganik , a PhD student at the University of Rochester with a background in vocal performance and composition, dis...
Data Skeptic is a data science podcast exploring machine learning, statistics, artificial intelligence, and other data topics through short...
In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on...
In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research assoc...
In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible....
In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Anton...
Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommend...
Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.
In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful fr...
In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of int...
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...
In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we are...
How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlatio...
Data Skeptic is a data science podcast exploring machine learning, statistics, artificial intelligence, and other data topics through short...
Data Skeptic is a data science podcast exploring machine learning, statistics, artificial intelligence, and other data topics through short...
In this episode we talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, with a f...
Kyle discusses the history and proof for the small world hypothesis.
Kyle asks Asaf questions about the new network science course he is now teaching. The conversation delves into topics such as contact tracin...
In this episode we talk with Bavo DC Campo, a data scientist and statistician, who shares his expertise on the intersection of actuarial sci...
In this episode we talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London,...
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...
In this episode, Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organizat...
Is it better to have your work team fully connected or sparsely connected? In this episode we'll try to answer this question and more with o...
A man goes into a bar… This is the beginning of a riddle that our guest, Yoed Kennet, an assistant professor at the Technion's Faculty of Da...
In this episode, Garima Agrawal, a senior researcher and AI consultant, brings her years of experience in data science and artificial intell...
In this episode, Bnaya Gross, a Fulbright postdoctoral fellow at the Center for Complex Network Research at Northwestern University, explore...
Our guests, Erwan Le Merrer and Gilles Tredan, are long-time collaborators in graph theory and distributed systems. They share their experti...
In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and gr...
Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply...
Our guest in this episode is David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, who specializes in scalable...
In this episode, Dave Bechberger, principal Graph Architect at AWS and author of "Graph Databases in Action", brings deep insights into the...
In this episode, Adam Machowczyk, a PhD student at the University of Leicester, specializes in graph rewriting and its intersection with mac...
In this episode, the data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok. We tal...
Alex Bisberg, a PhD candidate at the University of Southern California, specializes in network science and game analytics, with a focus on u...
In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a...
We are joined by Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and planning...