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dc.contributor.advisorBrenner, Michael P.
dc.contributor.authorQin, Yu
dc.date.accessioned2014-06-07T01:12:39Z
dc.date.issued2014-06-06
dc.date.submitted2014
dc.identifier.citationQin, Yu. 2014. Computations and Algorithms in Physical and Biological Problems. Doctoral dissertation, Harvard University.en_US
dc.identifier.otherhttp://dissertations.umi.com/gsas.harvard:11478en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12274619
dc.description.abstractThis 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.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dash.licenseLAA
dc.subjectApplied mathematicsen_US
dc.subjectComputer scienceen_US
dc.subjectPhysicsen_US
dc.subjectComputationen_US
dc.subjectInformation miningen_US
dc.subjectRandom matrix theoryen_US
dc.subjectSelf assemblyen_US
dc.subjectSequencing by hybridizationen_US
dc.titleComputations and Algorithms in Physical and Biological Problemsen_US
dc.typeThesis or Dissertationen_US
dash.depositing.authorQin, Yu
dc.date.available2014-06-07T01:12:39Z
thesis.degree.date2014en_US
thesis.degree.disciplineEngineering and Applied Sciencesen_US
thesis.degree.grantorHarvard Universityen_US
thesis.degree.leveldoctoralen_US
thesis.degree.namePh.D.en_US
dc.contributor.committeeMemberBrenner, Michaelen_US
dc.contributor.committeeMemberMahadevan, Lakshminarayananen_US
dc.contributor.committeeMemberRubinstein, Shmuelen_US
dash.contributor.affiliatedQin, Yu


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