CS Department Colloquium Series

The growing need for systems that operate on large datasets is well known, but most programming models for such systems are either too low level to appeal to non-expert programmers, or are specialized to tightly restricted problem domains.

In genomic sciences, the amount of data has grown faster than statistical methodologies necessary to analyze those data. Furthermore, the complex underlying structure of these data means that simple, unstructured statistical models do not perform well.

Historically, most large distributed storage systems (e.g., Hotmail) have been using replication to provide reliability against machine failures. Today however as the amount of stored data reaches multiple Exabytes keeping few copies of data around is becoming prohibitively expensive.

The U.S. Institute of Medicine commissioned my 2011 report on the role of trustworthy software in the context of the "510(k)" U.S. medical device regulations. More recently, the U.S. Food and Drug Administration has released draft guidance on cybersecurity for medical device manufacturing.

In this talk, I will tell the story of our work with some truly remarkable undergraduate students at Rutgers-Camden, who despite many odds have achieved success that is unprecedented for the Camden campus.

The goal of secure program obfuscation is to make a program “unintelligible” while preserving its functionality. For decades, program obfuscation for general programs has remained an art, with all public general-purpose obfuscation methods known to be broken.

This talk will present a simple principle relating knowledge and action in distributed systems: If some condition must be true when a given action is taken, then this condition must be known when the action is taken.

The Web has transformed the nature of creative work. For the first time, millions of people have a direct outlet for sharing their creations with the world.

Machine Learning is often viewed through the lens of statistics, where one tries to model or fit a set of data under stochastic conditions; for example, it is typical to assume one's observations were sampled IID.

The rapid growth in data volumes requires new computer systems that scale out across hundreds of machines. While early frameworks, such as MapReduce, handled large-scale batch processing, the demands on these systems have also grown.