Probabilistic models have been successfully applied for a wide variety of problems, such as but not limited to information retrieval, computer vision, bio-informatics and speech processing. Probabilistic models allow us to encode our assumptions about the data in an elegant fashion and enable us to perform machine learning tasks such...
Montane meadows comprise a small area of the predominantly forested landscape
of the Oregon Cascade Range. Tree encroachment in the last century in these areas has
threatened a loss of biodiversity and habitat. Climate change in the coming century may
accelerate tree encroachment into meadows, and exacerbate biodiversity loss. Multiple...
Recently, delta-sigma modulation has become a widely applied technique for high-performance analog-to-digital conversion of narrow-band signals. Most of the early designs used discrete-time structure for good accuracy and good linearity. The transfer functions are independent of the clock frequency. However, high unity-gain bandwidths of the opamps are required to satisfy...
Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute...
Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction.
In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
The focus of this thesis is to design, characterize, and apply novel computational methods and molecular systems to interrogate heterogeneous human gut microbiome-related phenomena. In Chapter 2, I design, implement, and characterize a method for embedding co-occurrence patterns derived from massive 16s amplicon datasets. I use this method to 1....
Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...