My research interest lies at the intersection of machine learning, signal processing, statistics, and information theory and I work on data analysis problems that arise from diverse applications. In particular, my research vision is to develop a rich suite of techniques that take a holistic approach to the integrated design of learning algorithms and data acquisition systems. I have been designing the next generation of machine learning algorithms that leverage latent structures like graphs, subspaces, manifolds, and clusters to use data effectively while taking into account the constraints imposed by the data acquisition process. This often involves the design of pooling strategies for experiments, compressed data acquisition and transmission strategies, and strategies for interactivity of the learning algorithm with both its environment and with human experts.