The massive growth of data now being made available from a variety of sources leads to an
increased demand for fast data processing to extract value from the data. In data streams …

Software systems often exhibit a surprising flexibility in the range of execution paths they can
take to produce an acceptable result. This flexibility enables new techniques that augment …

In approximate processing on stream data, most works focus on how to approximate online
arrival data. However, the efficiency of approximation needs to consider multiple aspects …

We present Topaz, a new task-based language for computations that execute on
approximate computing platforms that may occasionally produce arbitrarily inaccurate …

Many emerging distributed applications operate on inherently noisy data or produce
approximate results. Emerging application domains, including IoT, self-driving cars, and …

The growing computing demands of emerging application domains such as
Recognition/Mining/Synthesis (RMS), visual computing, wearable devices and the Internet …

Many modern applications (such as multimedia processing, machine learning, and big-data
analytics) exhibit an inherent tradeoff between performance and the accuracy of the …

Energy efficiency is a key concern in the design of modern computer systems. One
promising approach to energyefficient computation, approximate computing, trades off …

The focus of this dissertation is the analysis of and verification of discrete time stochastic
systems using martingales. Martingale theory yields a powerful set of tools that have recently …

Continuous computations, which involve continuous data and operations on them, are
ubiquitous in diverse areas such as machine learning and scientific computing. In theoretical …