In this thesis we consider the problem of clustering the data lying in a union of subspaces using spectral methods. Though the data generated may have high dimensionality, in many of the applications, such as motion ...

Gene prioritization involves ranking genes by possible relevance to a disease of interest. This is important in order to narrow down the set of genes to be investigated biologically, and over the years, several computational ...

We study two fundamental problems related to geometric packing and covering, and design algorithms with improved worst-case performance guarantees for them. These problems have numerous applications in resource allocation, ...

Real-world systems consisting of interacting entities can be effectively represented as time-evolving networks or graphs, where the entities are depicted as nodes, and the interactions between them are represented as ...

This dissertation work is motivated by the critical need to address a perennial global problem, namely, how to mitigate the distress of the small and marginal agricultural farmers in emerging economies. Key reasons behind ...

In the recent years, the world has been devastated by multiple pandemics arising out of different variants of the Corona virus. When an epidemic or pandemic strikes, it is important to move quickly to contain and suppress ...

Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents ...

Online decision-making under uncertainty is a fundamental aspect of numerous real-world problems across various domains, including online resource allocation, crowd-sourcing, and online advertising. Multi-Armed Bandits ...

Understanding complex phenomena in diverse scientific and engineering disciplines often relies on decoding the interplay among real-valued or scalar fields that are measured or computed over a spatial domain. This thesis ...