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Description:: Prediction and causality are of significant importance in public health and epidemiology. Questions of interest related to predictive modeling include the screening for high risk …
Year:: 2020
Contributor:: Xu, Yizhen (creator); Hogan, Joseph (Advisor); Liu, Tao (Reader); Kantor, Rami (Reader); Marshall, Brandon (Reader); Brown University. Department of Biostatistics (sponsor)
Genre:: theses
Subject:: causal inference; Medical policy; Bayesian Machine Learning; Ensemble Learning
Collection:: Public Health; Biostatistics; Theses and Dissertations

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Description:: An essential goal in systems neuroscience is to understand how cell-level properties give rise to emergent network-level activity. An accurate mechanistic understanding of emergent dynamics …
Year:: 2025
Contributor:: Tolley, Nicholas (creator); Jones, Stephanie (Advisor); Vargas-Irwin, Carlos (Reader); Donoghue, John (Reader); Moore, Christopher (Reader); Einevoll, Gaute (Reader); Brown University. Department of Neuroscience (sponsor)
Genre:: theses
Subject:: Biophysics; Neocortex; Computational neuroscience; Deep Learning; Bayesian Machine Learning; EEG biomarker; Neural Dynamics; Digital twins (Computer simulation)
Collection:: Neuroscience; Theses and Dissertations

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Description:: Development is a complex process governed by coordinated gene regulation at a precise time and place. Uncovering how transcription factors (TFs) and their targets communicate …
Year:: 2022
Contributor:: Conard, Ashley Mae (creator); Larschan, Erica (Advisor); Crawford, Lorin (Advisor); Lawrence, Charles (Advisor); Singh, Ritambhara (Reader); Severson, Kristen (Reader); Brown University. Center for Computational Molecular Biology (sponsor)
Genre:: theses
Subject:: Machine Learning; Transcription factors; Computational biology; Bioinformatics; Bayesian Machine Learning; Time-series analysis; Gene regulatory networks; Multi-omics data
Collection:: Center for Computational Molecular Biology; Theses and Dissertations

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Description:: For the recent decades, statistical (machine) learning and Bayesian modelling have emerged as popular frameworks for various fields of area, regardless of academy or industry. …
Year:: 2020
Contributor:: Lee, Taehee (creator); Lawrence, Charles (Advisor); Lisiecki, Lorraine (Reader); Harrison, Matthew (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: Applied mathematics; Paleoclimatology; Gaussian processes; State-space models; Machine learning--Statistical methods; Bayesian Machine Learning; Paleoceanography
Collection:: Applied Mathematics; Theses and Dissertations

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Description:: Consistent medical care is essential for the health of people living with HIV (PLWH). Regular engagement in care improves access to antiretroviral therapy (ART), reduces …
Year:: 2026
Contributor:: Lewis, Nickolas (creator); Hogan, Joseph (Advisor); Oganisian, Arman (Reader); Steingrimsson, Jon (Reader); Kantor, Rami (Reader); Brown University. Department of Biostatistics (sponsor)
Genre:: theses
Subject:: HIV/AIDS; missing data; Bayesian Machine Learning; Variable Importance; Electronic Health Records
Collection:: Public Health; Biostatistics; Theses and Dissertations

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