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Algorithm Design Meets Big Data

Bahman Bahmani (Stanford University)
Hardcore Data Science Gramercy Suite
Average rating: ****.
(4.10, 10 ratings)
Slides:   1-PPTX 

Many big data applications require distributed computations over a cluster of commodity machines, e.g., under the MapReduce framework. This distributed computational model leads to algorithmic tradeoffs (e.g., between computation, memory usage, and network communication) that are different from those traditionally considered by algorithm designers. We will present these tradeoffs, explain the properties that a scalable big data algorithm must possess, and then provide pragmatic techniques, such as filtering, modulation, and distributed sketching, to effectively design such algorithms for different applications. We will demonstrate these techniques through concrete examples from machine learning (e.g., large scale clustering) to social network analysis (e.g., community detection) and text analytics (e.g., similarity search). We will show how utilizing these algorithmic techniques can enable big data applications that would otherwise be simply infeasible even using the most modern big data architectures.

Photo of Bahman Bahmani

Bahman Bahmani

Stanford University

Bahman did his PhD at Stanford University, supported by William R. Hewlett Stanford Graduate Fellowship, and focused on the topic of algorithms for big data applications, in which he is a well-published author in some of the best conferences and journals, including PVLDB, SIGMOD, WWW, and KDD. He was the last PhD student of the legendary late Rajeev Motwani, and was also advised by Ashish Goel and Prabhakar Raghavan (formerly Yahoo Chief Strategy Officer, currently Google VP of Engineering). His industry experience during his PhD studies spans several internships and collaborations with some of the best researchers and practitioners from Twitter, Microsoft Research, Yahoo Research, AOL, and Google. A recipient of the Yahoo Key Scientific Challenges Award, he was dubbed by Yahoo Labs as one of the “outstanding graduate student researchers who have the greatest potential to make significant contributions and become thought leaders in their research fields”.

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