Colloquium

Research in cryptography and security has an incredible potential for fruitful interaction between theory and practice. The case of public-key cryptography may be considered an example of a successful transition from theory to practice. Elsewhere, there has been less interaction between the fields.

Chip multi-processors (CMP), where a processor core is replicated multiple times on a single chip, are widely recognized as a means to exploit the exponentially growing transistor budgets predicted by Moore's Law, while respecting design complexity and physical constraints.

Statistical estimation problems arise naturally in many areas of machine intelligence. In this talk, we consider problems of this kind from two areas of machine learning: supervised learning, where the goal is to learn to make decisions in an i.i.d.

In this talk I will discuss the main principles of inference from data developed in statistical learning theory which consider in non-parametric framework the problem of inference from a given (fixed) sample size.

Along with a survey of new ideas of statistical learning theory and their comparis

Building a secure system is a complex and error-prone process in computing. System designers and developers face many challenges: What does it mean for a system to be secure? How do I know whether my system is secure? Will the security of my system break if I add a new component?

Understanding the nature of reflection and illumination is important in many areas of computer graphics and computer vision.

Programming for Pervasive Computing Environments Pervasive computing provides an attractive vision for the future of computing. Computing power will be available everywhere. Mobile and stationary devices will dynamically connect and coordinate to seamlessly help people accomplish their tasks.

Aerial acoustic communications are remarkably successful in biological systems, most notably in humans, but they have been ignored or dismissed in computer networks. There are good reasons for this: the data rates are relatively low when compared to other media and the sounds tend to be annoying.

A powerful theme from computational learning theory is uniform convergence: the fact that a large or infinite class C of random variables can be simultaneously estimated from a sample whose size is just logarithmic in the size of C, or dependent only on combinatorial parameters such as its VC dimens