How to Build AI that respects (and protects) your privacy

Please join us for a presentation and discussion led by Alessandro Gagliardi, Data Engineer at Pandora.

Data science and data security have historically been natural enemies. One wants to know everything and the other wants to prevent that from happening. Over the last decade—leveraging advances in data science—machine learning applications have evolved to bring us services that anticipate our every need...but at the cost of our privacy. Perhaps it doesn’t have to be that way. In this presentation, Alessandro will outline a proposed architecture for building ML apps that not only respect but actually protect the end user’s privacy.

Alessandro Gagliardi is a Data Engineer at Pandora where he works on distributed analytics platforms. He has previously held roles as a Senior Data Scientist at Glassdoor and an Analytics Engineer at Path. He taught Data Science and Data Engineering at the University of San Francisco, University of New Haven/Galvanize and General Assembly. Alessandro received his B.A. in Computer Science from UCSC and pursued a Ph.D. in Behavioral and Neural Science at Rutgers.

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