Finding Nemo: The Power of Probabilistic Method

Barna Saha, AT&T Research
Host: Rao Kosaraju

How, as computer scientists, can we help Marlin find Nemo in a very short time? The answer may lie in the Probabilistic Method. Pioneered by Paul Erdös more than seven decades back, the Probabilistic Method has provided a number of widely used tools in Combinatorics. However, many of these tools are non-constructive: while they show the existence of certain combinatorial structures, they do not tell us how to find them. One such powerful technique is László Lovász’s famous Local Lemma (LLL). LLL has diverse range of applications that include breakthroughs in packet-routing, a variety of theorems in graph-coloring, and a host of other surprising applications where probability appears to have no role. While the original LLL was nonconstructive, there has been a series of works trying to devise algorithmic versions of LLL. In the first half of my talk, I will describe our work in this regime which covers many compelling applications that were out of reach by previous methods. Using our technique, we resolve a fascinating open question in the area of resource allocation and scheduling while giving the first Monte-Carlo approximation algorithms for a large variety of combinatorial problems.

Probability comes as a surprising element in many of the above problems, however, there are applications where probability is inherent in the data. Applications such as information retrieval, data integration and cleaning, text analytics, social network analysis etc. deal with voluminous amount of uncertain data. Probability theory can play a critical role in designing scalable algorithms under such uncertainty. In the second part of my presentation, I will talk about a basic problem of ranking and how using probabilistic generating function idea, we can give a unified approach to finding top-k results over probabilistic databases.

Speaker Biography

Barna Saha is a Senior Member of Research at the AT&T Shannon Laboratories. She obtained her Ph.D. in Computer Science from University of Maryland College Park in 2011. Her Research interest spans the areas of theoretical computer science and database management system, such as design and analysis of algorithms, probabilistic methods, graph theory and big data analysis. She is the co-winner of the best paper award in VLDB 2009, one of the premier conferences in databases, for her work on probabilistic ranking algorithms. She is also the recipient of Deans Fellowship Award, University of Maryland, 2010, for her dissertation research.