Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for …
The Epoch of Reionization (EoR) is a period during the history of the Universe that as of today has been largely unconstrained by cosmological measurements. …
Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents …
While information systems are often associated with microelectronic devices, every physical process can be interpreted as an exchange of information. With the slowing of Moore's …
Just as an interconnected-computerized world has produced large amounts of data resulting in exciting challenges for machine learning, connected households with robots and smart devices …
This thesis discusses the design of a sensor-embedded data glove called alto.glove, created by the author and used in interactive performances involving string instrument playing …
I introduce novel concentration-of-measure bounds for the supremum deviation, several variance concepts, and a family of game-theoretic welfare functions. I then apply these bounds to …
Coreference Resolution is a fundamental natural language processing (NLP) problem, as it attempts to resolve which underlying discourse objects refer to one another. Further, it …
The structures of molecules in solution are essential to their reactivities in solution. The solution structures can be characterized by estimating the molecular weight of …
Recent machine learning techniques have become a powerful tool in a variety of tasks, including neural decoding. Artificial neural network models, particularly recurrent models, can …
Fractional-order partial differential equations (FPDEs) describe macroscopic properties of systems driven by Levy processes and, more generally, Continuous-Time Random Walks (CTRWs). CTRWs form a versatile …
This dissertation primarily contributes to the visualization and visual analytics community. It offers findings and methods to expand the visualization design space and to deepen …
Motivated by the ubiquitous demand of leveraging both data and partial knowledge of physical laws for stochastic modeling and uncertainty quantification, in the dissertation, a …
Background: Prognostic prediction has empirically proven to be a highly effective paradigm that is radically reshaping public health, clinical medicine, and healthcare as a domain. …
The past decades have witnessed an increasing demand for characterizing mechanical properties of materials at small scales due to the miniaturization of devices. While tremendous …
In this work, I present an implemented model that can learn interactively from natural language, enabling non-expert human trainers to convey complex tasks to machines …
Electronic-structure calculations have provided us with in-depth understanding of chemical and physical phenomena in atomic and molecular scales. In particular, density functional theory has been …
In eukaryotes, DNA is first transcribed into precursor-messenger RNA (pre-mRNA) which is then followed by extensive RNA processing events. One such event is RNA splicing, …
In both supervised and reinforcement settings, there exist learning problems that are hard due to having high computational or sample complexity. Researchers have shown, using …
