Browsing by Subject "Machine learning"
Now showing items 1-19 of 19
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A Semi-Supervised Predictive Model to Link Regulatory Regions to Their Target Genes
(2015)Next generation sequencing technologies have provided us with a wealth of data profiling a diverse range of biological processes. In an effort to better understand the process of gene regulation, two predictive machine learning ... -
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
(2018)Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the ... -
Appearance-based Gaze Estimation and Applications in Healthcare
(2020)Gaze estimation, the ability to predict where a person is looking, has become an indispensable technology in healthcare research. Current tools for gaze estimation rely on specialized hardware and are typically ... -
Applications of Topological Data Analysis and Sliding Window Embeddings for Learning on Novel Features of Time-Varying Dynamical Systems
(2017)This work introduces geometric and topological data analysis (TDA) tools that can be used in conjunction with sliding window transformations, also known as delay-embeddings, for discovering structure in time series and dynamical ... -
Classification and Characterization of Heart Sounds to Identify Heart Abnormalities
(2019)The main function of the human heart is to act as a pump, facilitating the delivery of oxygenated blood to the many cells within the body. Heart failure (HF) is the medical condition in which a heart cannot adequately pump ... -
Compressive Sensing in Transmission Electron Microscopy
(2018)Electron microscopy is one of the most powerful tools available in observational science. Magnifications of 10,000,000x have been achieved with picometer precision. At this high level of magnification, individual atoms are ... -
Computational Analysis of Clinical Brain Sub-cortical Structures from Ultrahigh-Field MRI
(2015)Volumetric segmentation of brain sub-cortical structures within the basal ganglia and thalamus from Magnetic Resonance Image (MRI) is necessary for non-invasive diagnosis and neurosurgery planning. This is a challenging ... -
Data-driven investigations of disgust
(2019)Disgust features prominently in many facets of human life, from dining etiquette to spider phobia to genocide. For some applications, such as public health campaigns, it might be desirable to know how to increase disgust, ... -
Development of X-ray Fan Beam Coded Aperture Diffraction Imaging for Improving Breast Cancer Diagnostics
(2021)X-ray imaging technology has been used for a multitude of medical applications over the years. The typically measured X-ray transmission data, which records shape and density information by measuring the differences in X-ray ... -
Dynamic Time Varying Models for Predicting Patient Deterioration
(2017)Streaming data are becoming more common in a variety of fields. One common data stream in clinical medicine is electronic health records (EHRs) which have been used to develop risk prediction models. Our motivating application ... -
Examining the Role of Ballast Water in the Global Translocation of Microorganisms
(2019)Ballast water is a known vector for the global translocation of microorganisms. Research into the ballast microbiome recently accelerated following a ballast-associated outbreak of Vibrio cholerae in Peru during the 1990s ... -
Interpretability by Design: New Interpretable Machine Learning Models and Methods
(2020)As machine learning models are playing increasingly important roles in many real-life scenarios, interpretability has become a key issue for whether we can trust the predictions made by these models, especially when we are ... -
Minimax Fairness in Machine Learning
(2022)The notion of fairness in machine learning has gained significant popularity in the last decades, in part due to the large number of decision-making models that are being deployed on real-world applications, which have presented ... -
Nonparametric Bayesian Context Learning for Buried Threat Detection
(2012)This dissertation addresses the problem of detecting buried explosive threats (i.e., landmines and improvised explosive devices) with ground-penetrating radar (GPR) and hyperspectral imaging (HSI) across widely-varying ... -
Optimization of Fault-Insertion Test and Diagnosis of Functional Failures
(2011)Advances in semiconductor technology and design automation methods have introduced a new era for electronic products. With design sizes in millions of logic gates and operating frequencies in GHz, defects-per-million rates ... -
Physically Motivated Feature Development for Machine Learning Applications
(2017)Feature development forms a cornerstone of many machine learning applications. In this work, we develop features, motivated by physical or physiological knowledge, for several applications: energy disaggregation, brain cancer ... -
Predicting Colonization of Microbes in the Human Gut
(2018)The human gut is home to trillions of microbes that interact intimately with the host and its diet. An important emergent phenotype by these microbes is colonization resistance, the process by which a microbial community ... -
Soul and machine (learning)
(Marketing Letters, 2020-12-01)Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are ... -
Supervised MELD for Multi-domain Mixed Membership Analyses
(2017)When variables used in a mixed membership analysis can be classied into conceptu-ally distinct domains, interpretation of results is facilitated by using domain-specicmodels with a small number of imposed pure-type proles ...
