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 …
Background: Predictive analytic models, including machine learning (ML) models, are increasingly integrated into electronic health record (EHR)-based decision support tools for clinicians. These models have …
The goal of this study is to create a simulation-trained convolutional neural network (CNN) that can detect and characterize stiffer tissue embedded in a softer …
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. …
Volcanic hazards monitoring relies heavily on constraints offered by geophysical inversions to decipher the structure of subvolcanic magmatic systems. Over the past decades a multidisciplinary …
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 …
Anterior cruciate ligament (ACL) disruption is a common injury, particularly in the young and active patient. ACL reconstruction surgery is the current standard of care, …
Sleep apnea is a common, yet underdiagnosed, sleep-related breathing disorder associated with increased risk of cardiovascular issues. This work uses a multifaceted approach integrating artificial …
Biological brains are dynamic. Recent advances in electrophysiology and neuroimaging have helped uncover various mechanisms through which brains construct and utilize rich variations in neural …
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 …
Deep learning shifts the way to build signal processing systems from coding or model-centric to data-centric. This paper presents a system to support data-centric deep …
How synaptic plasticity in-vivo manages diverse properties of biological neural networks remains poorly understood. My collaborators and I address this by examining basic models of …
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 …
Objective: To develop a neural network for the binary classification of cardiac arrest patient electroencephalograms (EEGs) between two categories: general periodic discharges (GPDs) or non-GPDs. …
Molten Salts Reactors (MSR) with fuel-containing coolants have seen a rise in interest thanks to their increased safety features, high fuel efficiency and lower waste …
