AlphaGo, COVID-19 Contact Tracing and New Data Set
Data Skeptic28 Mar 2020

AlphaGo, COVID-19 Contact Tracing and New Data Set

Announcing Journal Club

I am pleased to announce Data Skeptic is launching a new spin-off show called "Journal Club" with similar themes but a very different format to the Data Skeptic everyone is used to.

In Journal Club, we will have a regular panel and occasional guest panelists to discuss interesting news items and one featured journal article every week in a roundtable discussion. Each week, I'll be joined by Lan Guo and George Kemp for a discussion of interesting data science related news articles and a featured journal or pre-print article.

We hope that this podcast will give listeners an introduction to the works we cover and how people discuss these works. Our topics will often coincide with the original Data Skeptic podcast's current Interpretability theme, but we have few rules right now or what we pick. We enjoy discussing these items with each other and we hope you will do.

In the coming weeks, we will start opening up the guest chair more often to bring new voices to our discussion. After that we'll be looking for ways we can engage with our audience.

Keep reading and thanks for listening!

Kyle

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Episoder(601)

Student Spotlight: Aaron Payne, Data Analyst

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...

1 Mai 25min

The Future is Agentic in Recommender Systems

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 mat...

25 Apr 49min

Book Ratings and Recommendations

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 mat...

27 Mar 39min

Disentanglement and Interpretability in Recommender Systems

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 corre...

10 Mar 30min

Collective Altruism in Recommender Systems

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 r...

27 Feb 54min

Niche vs Mainstream

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 spec...

18 Feb 34min

Healthy Friction in Job Recommender Systems

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. Roa...

2 Feb 26min

Fairness in PCA-Based Recommenders

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 Enter...

26 Jan 49min

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