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This dissertation presents the applications of state-of-the-art computation techniques and data analysis algorithms in three physical and biological problems: assembling DNA pieces, optimizing self-assembly yield, and identifying correlations from large multivariate datasets. In the first topic, in-depth analysis of using Sequencing by Hybridization (SBH) to reconstruct target DNA sequences shows that a modified reconstruction algorithm can overcome the theoretical boundary without the need for different types of biochemical assays and is robust to error. In the second topic, consistent with theoretical predictions, simulations using Graphics Processing Unit (GPU) demonstrate how controlling the short-ranged interactions between particles and controlling the concentrations optimize the self-assembly yield of a desired structure, and nonequilibrium behavior when optimizing concentrations is also unveiled by leveraging the computation capacity of GPUs. In the last topic, a methodology to incorporate existing categorization information into the search process to efficiently reconstruct the optimal true correlation matrix for multivariate datasets is introduced. Simulations on both synthetic and real financial datasets show that the algorithm is able to detect signals below the Random Matrix Theory (RMT) threshold. These three problems are representatives of using massive computation techniques and data analysis algorithms to tackle optimization problems, and outperform theoretical boundary when incorporating prior information into the computation., Engineering and Applied Sciences

My dissertation examines the consequences of the massive infrastructure investment in China from three different aspects. In the first chapter, I examine the distributional impacts of high-speed rail upgrade in China. By exploiting the quasi-experimental variation in whether counties were affected by this project, I find that relative to non-affected counties, affected counties on the upgraded railway lines suffered a 4-6 percent reduction in GDP and GDP per capita following the upgrade, which can be explained largely by the concurrent drop in fixed asset investment. In addition, since high-speed rail upgrade affects transportation of passengers and not transportation of goods, the negative impact of the upgrade is significant in the service sector and not in the manufacturing sector. A possible mechanism consistent with the core-periphery model is also discussed in this chapter. In the second chapter, I use four waves of a primary panel household survey conducted in 17 remote natural villages in China to study how road access shapes farmers' production patterns, input use, and rural poverty. The results show that access to roads facilitates specialization in agricultural production. In natural villages with better road access, farmers plant fewer numbers of crops, purchase more fertilizer, and hire more labor. Consequently, road connections improve household agricultural income and reduce poverty. However, better access to rural roads does not appear to bring about significant changes in nonagricultural income. In the third chapter, I study whether the increasing insecurity of home ownership being reported in the media induces urban households to save more in Chinese cities, which is in favor of the precautionary savings motive. Using Difference GMM models, I find that worse insecurity of home ownership, as indicated by more frequent forced evictions, leads to higher household savings rate at the prefecture city level. In addition, I find that the impact could work through a reduction of home purchase due to forced evictions, as well as precautionary savings motive of existing home owners.