• A GROUP SEQUENTIAL MULTIPLE TESTING METHOD AND ITS APPLICATION TO GENOMIC DATA 

  Kim, Yewon (2022-01-01)

  In this dissertation, we consider the simultaneous testing of groups and hypotheses within the groups which occurs in many scientific problems. A group is commonly judged to be significant if at least one hypothesis within ...

• Modeling and PDE Theory for The Large Deflections 

  Deliyianni, Maria (2022-01-01)

  Flutter is defined as a self-excitation of a thin structure where a surrounding flow destabilizes its natural elastic modes. Cantilevers are particularly prone to flutter, and it has been shown that this instability can ...

• Statistical Inference on High Dimensional Normal Mean Under Linear Inequality Constraints and Efficient Integration of Data in Meta-Analysis 

  Agarwala, Neha (2022-01-01)

  In this dissertation, we provide a framework for incorporating linear inequality parameter constraints in estimation and hypothesis testing involving high dimensional normal means. Modern statistical problems often involve ...

• Neural Networks for the Sanitization of Compton Camera Based Prompt Gamma Imaging Data for Proton Radiotherapy 

  Barajas, Carlos Alexander (2022-01-01)

  Proton beam radiotherapy is a method of cancer treatment that uses proton beamsto irradiate cancerous tissue, while simultaneously sparing doses to healthy tissue. In order to optimize radiation doses to the tumor and ...

• Statistical Modeling using Conditionally Specified Joint Distributions with Applications 

  Wijekoon, Nadeesri (2021-01-01)

  Often in practice, conditional distributions are easier to model and interpret while the joint distribution itself is either intractable or not available in closed form. When the observed response consists of both continuous ...

• METHODS IN LARGE SCALE MULTIPLE TESTING: MIXTURE NULL, SMALL SAMPLE REPLICATES, AND POWER BOOSTING 

  Ramos, Mark Louie Frac (2021-01-01)

  In this dissertation, we study some methods in multiple testing. In the first topic, we consider the setting of gene expression experiments that use logfold change statistics where the null distribution is assumed to be a ...

• Topics in Data Assimilation and Stochastic Partial Differential Equations 

  price, randy (2021-01-01)

  In the first part of the thesis we consider data assimilation. We consider the nudging data assimilation algorithm applied to the 3D Navier-Stokes equations and we derive conditions, based entirely on the observations, ...

• OPTIMIZATION ALGORITHMS FOR TRAINING DEEP NEURAL NETWORKS 

  Praniewicz, Jared (2021-01-01)

  A formal representation of a deep neural network is constructed, andit is demonstrated that networks satisfying the representation can be trained via feed forward back propagation efficiently. Analysis of the formal ...

• Hidden Markov Models for High Dimensional Data with Geostatistical Applications 

  Majumder, Reetam (2021-01-01)

  Stochastic precipitation generators (SPGs) are a class of statistical models which generate synthetic data that can simulate dry and wet rainfall stretches for long durations. Generated precipitation time series data are ...

• Fully Distributed Algorithms for Densely Coupled Optimization Problems in Sparse Optimization and Transportation Applications 

  Hathibelagal Kammara, Eswar Kumar (2021-01-01)

  Distributed algorithms are gaining increasing attention with broad applications indifferent areas such as multi-agent network systems, big data, machine learning, and distributed control systems, among others. Most of the ...

• A Generalized Model of Flocking with Steering 

  Djokam, Guy Aime (2021-01-01)

  Flocking is a phenomenon in which self-propelled agents use only simple rules based on information about the locations and velocities of other agents to move from an unordered motion to an ordered motion in the long run. ...

• Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network 

  Barajas, Carlos A.; Gobbert, Matthias; Wang, Jianwu (Springer, 2021-10-30)

  Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic ...

• Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM (Papers Track) 

  Ali, Sahara; Huang, Yiyi; Huang, Xin; Wang, Jianwu

  Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been ...

• Performance Benchmarking of Data Augmentation and GPU Count Variability for Deep Learning Tornado Predictions 

  Basalyga, Jonathan N.; Barajas, Carlos A.; Gobbert, Matthias; Wang, Jianwu (Elsevier, 2021-07-15)

  Predicting violent storms and dangerous weather conditions with current mod- els can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify ...

• Positive and Negative Photoconductivity in Monolayer MoS₂ as a Function of Physisorbed Oxygen 

  Gustafson, Jon; Wines, Daniel; Gulian, Ellen; Ataca, Can; Hayden, Michael (ACS, 2021-04-16)

  We investigate the effect of molecular oxygen on the photoconductivity of monolayer MoS₂ via broad-band time-resolved terahertz spectroscopy. We observe that the photoconductivity of monolayer MoS₂ transitions from ...

• FLIP: A Utility Preserving Privacy Mechanism for Time Series 

  McElroy, Tucker; Roy, Anindya; Hore, Gaurab (2022-07-15)

  Guaranteeing privacy in released data is an important goal for data-producing agencies. There has been extensive research on developing suitable privacy mechanisms in recent years. Particularly notable is the idea of noise ...

• Criteria for the (in)stability of planar interfaces in singularly perturbed 2-component reaction-diffusion equations 

  Carter, Paul; Doelman, Arjen; Lilly, Kaitlynn; Obermayer, Erin; Rao, Shreyas (2022-07-11)

  We consider a class of singularly perturbed 2-component reaction-diffusion equations which admit bistable traveling front solutions, manifesting as sharp, slow-fast-slow, interfaces between stable homogeneous rest states. ...

• Team-Based Online Learning in Multidisciplinary Research and Instruction 

  Gobbert, Matthias; Wang, Jianwu

  There is a critical nationwide shortage of IT professionals as well as of scientists and engineers with high-performance computing (HPC) and big data related advanced computing skills. Simultaneously, the technology is ...

• A Note on Tournaments and Negative Dependence 

  Malinovsky, Yaakov; Rinott, Yosef (2022-06-16)

  Negative dependence of sequences of random variables is often an interesting characteristic of their distribution, as well as a useful tool for studying various asymptotic results, including central limit theorems, Poisson ...

• Inexact and primal multilevel FETI-DP methods: a multilevel extension and interplay with BDDC 

  Sousedik, Bedrich (Wiley, 2022-06-07)

  We study a framework that allows to solve the coarse problem in the FETI-DP method approximately. It is based on the saddle-point formulation of the FETI-DP system with a block-triangular preconditioner. One of the blocks ...

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