[MINI] Parallel Algorithms

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 "protest movements" can paradoxically he ...  Show more

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 niche users while raising questions abo ...  Show more

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that cla ...  Show more

If you’re a data scientist, you know how important it is to keep your data orderly, clean, moving smoothly between different systems, well-documented… there’s a ton of work that goes into building and maintaining databases and data pipelines. This job, that of owner and maintaine ...  Show more

At its simplest, an algorithm is a sequence of step-by-step instructions designed to give a result. They are the building blocks of every computer program and are there to ensure every digital device gives the right results on request. For example, when we type a search query int ...  Show more

Q McCallum (Senior Content Advisor at Formulatedby, the company behind Data Science Salon) met up with