Laura is a data scientist at Twitter with a taste for functional programming. She mostly writes Scala, though she really admires O'Caml as well. Laura knows a bit about recommender systems, statistics and math. She did her BSc in Financial and Actuarial Mathematics in Vilnius University, where with several friends she wrote a thesis about The Role of Tail Index in Analysis of Currency Returns.
Laura achieved her MSc in Computer Science at the Free University of Bozen – Bolzano, where she wrote a thesis about Pairwise Preferences in Collaborative Filtering. Laura spent three incredible months at Hacker School in New York and totally enjoyed it.
Currently, Laura is working as a data scientist at Twitter, mostly building domain specific user behaviour models, and is enjoying writing Scala, using Scalding, Algebird, and Summingbird.
You can check out Laura's blog here.
Talks I've Given
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Count-Min Sketch in Real Data Applications
Featuring Laura Bledaite
In this talk Laura will briefly explain several probabilistic data structures used for approximate query answering, such as a Bloom Filter, HyperLogLog and Count-Min Sketch, as well as present canonical examples of use at Twitter for each of the structures mentioned. You will learn how these...
query-answering bloom-filter hyperloglog count-min-sketch twitter algebird summingbird scala mapreduce