Nanoparticles (NPs) are emerging as attractive drug carriers in therapeutic and diagnostic applications. The physiochemical properties of NPs, such as particle size, shape, and surface chemistry, play important roles in the functions of engineered nanoconstructs−NP cores with surface ligands. Recent work has screened these properties by monitoring cellular uptake and/or...
This project traces the efforts by Scottish Protestants to achieve complete religious uniformity in the decades following the Reformation. Discontented with outward conformity, the via media that long characterized our understanding of religious reform in England, Scotland’s ministers sought to work genuine conversions among those who resisted the new order....
The viscoelastic properties of microbial biofilms have attracted great interests in recent years due to the ubiquity of biofilms and their wide range of industrial and municipal applications causing tremendous societal impacts. Biofilms are predominantly architected by extracellular polymeric substances (EPS) matrices composed of bacterial cells and biopolymers secreted by...
One of the grand challenges in science is development of soft materials that mimic living organisms to optimize the way we use energy, translate or morph reversibly or sense their envi- ronment and respond in a useful fashion. Using the insights from studying biological structures, we hope to design soft...
This dissertation presents novel advancements in the field of continuous nonlinear optimization, focusing on the development of efficient second-order methods for second-order conic programs (SOCPs) and continuous nonlinear two-stage optimization problems. The primary focus is on the theory and computations of Sequential Quadratic Programming (SQP) methods, which are widely used...
Recently, a myriad of applications take advantage of deep learning methods to solve regression/classification problems. Although deep neural networks have shown powerful learning capability, many deep learning applications suffer from the extremely time-consuming training of the neural networks. In order to reduce the training time, researchers usually consider parallel training...
My dissertation identifies the causes of inequality traps - i.e., high and persistent levels of economic inequality - in Latin America and explains how and why some countries manage to escape such traps and embark on paths of diminishing inequality. I argue that the Redistributive State Power shapes the main...
In this dissertation I study the effects of mortgage leverage policies. These policies have become widely used in recent years, both as a macroprudential tool and to protect consumers, yet their effects are still not well understood. In Chapter 1, I show that mortgage leverage rules implemented under the Dodd-Frank...
New capabilities in x-ray microscopy have been enabled by rapid advances in synchrotronlight sources and in high performance computing. We present here conceptual and computational
advances in two areas of x-ray microscopy. One involves ptychography, where computation is
used to obtain images from diffraction patterns as a finite coherent illumination...
Deep neural networks have shown impressive performance for many applications. In this dissertation, leveraging the capabilities of neural networks for modeling the non-linearity exists in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The suggested models can transfer knowledge from...