With off-chip memory access taking 100's of processor cycles, getting data to the processor in a timely fashion remains one of the key performance bottlenecks in current systems. With increasing core counts, this problem ...

This thesis touches three different topics in graph theory, namely, rainbow colouring, product dimension and boxicity. Rainbow colouring An edge colouring of a graph is called a rainbow colouring, if every pair of vertices ...

Outlier detection is an important data mining task due to its applicability in many contemporary applications such as fraud detection and anomaly detection in networks, etc. It assumes significance due to the general ...

An identity-based cryptosystem (IBC) is a public-key system where the public key can be represented by any arbitrary string such as an e-mail address. The notion was introduced by Shamir with the primary goal of simplifying ...

Given a program with assertions, the assertion checking problem is to tell whether there is an execution of the program that violates one of the assertions. One approach to this problem is to explore different paths towards ...

Model checking is a powerful technique for analyzing reach ability and temporal properties of finite state systems. Model-checking finite state systems has been well-studied and there are well known efficient algorithms ...

Unreliable communication channels are a practical reality. They add to the complexity of protocol design and verification. In this work, we consider noisy channels which can corrupt messages. We present an approach to model ...

The focus of this thesis is models for non-parametric clustering of multivariate count data. While there has been significant work in Bayesian non-parametric modelling in the last decade, in the context of mixture models ...

In this thesis, we address several generic problems concerned with procurement of tasks from a crowd that consists of strategic workers with uncertainty in their qualities. These problems assume importance as the quality ...

Hierarchical Bayesian Models and Matrix factorization methods provide an unsupervised way to learn latent components of data from the grouped or sequence data. For example, in document data, latent component corn-responds ...