Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (58 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2020)

With continued advances in Geographic Information Systems and related computational technologies, researchers in diverse fields like forestry, environmental health, climate sciences etc. have growing interests in analyzing large scale data sets measured at a substantial number of geographic locations. Geostatistical models used to capture the space varying relationships in such data are often accompanied by onerous computations which prohibit the analysis of large scale spatial data sets. Less burdensome alternatives proposed recently for analyzing massive spatial datasets often lead to inaccurate inference or require slow sampling process. Bayesian inference, while attractive for accommodating uncertainties through their hierarchical structures, can become computationally onerous for modeling massive spatial data sets because of their reliance on iterative estimation algorithms. My dissertation research aims at developing computationally scalable Bayesian geostatistical models that provide valid inference through highly accelerated sampling process. We also study the asymptotic properties of estimators in spatial analysis.

In Chapter 2 and 3, we develop conjugate Bayesian frameworks for analyzing univariate and multivariate spatial data. We propose a conjugate latent Nearest-Neighbor Gaussian Process (NNGP) model in Chapter 2, which uses analytically tractable posterior distributions to obtain posterior inferences, including the large dimensional latent process. In Chapter 3, we focus on building conjugate Bayesian frameworks for analyzing multivariate spatial data. We utilize Matrix-Normal Inverse-Wishart(MNIW) prior to propose conjugate Bayesian frameworks and algorithms that can incorporate a family of scalable spatial modeling methodologies.

In Chapter 4, we pursue general Bayesian modeling methodologies beyond a conjugate Bayesian hierarchical modeling. We build scalable versions of a hierarchical linear model of coregionalization (LMC) and spatial factor models, and propose a highly accelerated block update MCMC algorithm. Using the proposed Bayesian LMC model, we extend scalable modeling strategies for a single process into multivariate process cases.

All proposed frameworks are tested on simulated data and fit to real data sets with observed locations numbering in the millions. Our contribution is to offer practicing scientists and spatial analysts practical and flexible scalable hierarchical models for analyzing massive spatial data sets.

In Chapter 5, we investigate the asymptotic properties of the estimators in spatial analysis. We formally establish results on the identifiability and consistency of the nugget in spatial models based upon the Gaussian process within the framework of in-fill asymptotics, i.e. the sample size increases within a sampling domain that is bounded. We establish the identifiability of parameters in the Matern covariance function and the consistency of their maximum likelihood estimators in the presence of discontinuities due to the nugget.

    Cover page: On Simplified Bayesian Modeling for Massive Geostatistical Datasets: Conjugacy and Beyond
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2019)

    Persistent memory (PMEM) technologies preserve data across power cycles and provide performance comparable to DRAM. In emerging computer systems, PMEM will operate on the main memory bus, becoming byte-addressable and cache-coherent. One key feature enabled by persistent memory is to allow software directly accessing durable data using the CPU’s load/store instructions, even from the user-space.

    However, building reliable software for persistent memory faces new challenges from two aspects: crash consistency and fault tolerance. Maintaining crash consistency requires the ability to recover data integrity in the event of system crashes. Using load/store instructions to access durable data introduces a new programming paradigm, that is prone to new types of programming errors. Fault tolerance involves detecting and recovering from persistent memory errors, including memory media errors and scribbles from software bugs. With direct access, file systems and user-space applications have to explicitly manage these errors, instead of relying on convenient functions from lower I/O stacks.

    We identify unique challenges in improving reliability for PMEM-based software and propose solutions. The thesis first introduces NOVA-Fortis, a fault-tolerant PMEM file system incorporating replication, checksums, and parity for protecting the file system’s metadata and the user’s file data. NOVA-Fortis is both fast and resilient in the face of corruption due to media errors and software bugs.

    NOVA-Fortis only protects file data via the read() and write() system calls. When an application memory-maps a PMEM file, NOVA-Fortis has to disable file data protection because mmap() leaves the file system unaware of updates made to the file. For protecting memory-mapped PMEM data, we present Pangolin, a fault-tolerant persistent object library to protect an application’s objects from persistent memory errors.

    Writing programs to ensure crash consistency in PMEM remains challenging. Recovery bugs arise as a new type of programming error, preventing a post-crash PMEM file from recovering to a consistent state. Thus, we design two debugging tools for persistent memory programming: PmemConjurer and PmemSanitizer. PmemConjurer is a static analyzer using symbolic execution to find recovery bugs without running a compiled program. PmemSanitizer contains compiler instrumentation and run-time recovery bug analysis, compensating PmemConjurer with multi-threading support and store reordering tests.

      Cover page: Building Reliable Software for Persistent Memory
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 34 (2012)
        Cover page: Visual Context Effects on Thematic Role Assignment in Children versus Adults: Evidence from Eye Tracking in German
        • Article
        Research Reports (2021)
        Multivariate spatially-oriented data sets are prevalent in the environmental and physical sciences.Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture anyunderlying spatial association for each variable and associations among the different dependent variables.Multivariate latent spatial process models have proved effective in driving statistical inference andrendering better predictive inference at arbitrary locations for the spatial process. High-dimensionalmultivariate spatial data, which is the theme of this article, refers to data sets where the number of spatiallocations and the number of spatially dependent variables is very large. The field has witnessed substantialdevelopments in scalable models for univariate spatial processes, but such methods for multivariate spatialprocesses, especially when the number of outcomes is moderately large, are limited in comparison. Here,we extend scalable modeling strategies for a single process to multivariate processes. We pursue Bayesianinference which is attractive for full uncertainty quantification of the latent spatial process. Our approachexploits distribution theory for the Matrix-Normal distribution, which we use to construct scalableversions of a hierarchical linear model of coregionalization (LMC) and spatial factor models that deliverinference over a high-dimensional parameter space including the latent spatial process. We illustrate thecomputational and inferential benefits of our algorithms over competing methods using simulation studiesand an analysis of a massive vegetation index dataset.
          Cover page: Spatial Factor Modeling: A BayesianMatrix-Normal Approach for Misaligned Data
          • Article
          • Peer Reviewed
          Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 34 (2012)
            Cover page: The role of recent versus future events in children’s comprehension of referentially ambiguous sentences: Evidence from eye tracking
            • Article
            • Peer Reviewed
            Open Access Policy Deposits (2021)
              Cover page: On identifiability and consistency of the nugget in Gaussian spatial process models
              • Article
              Research Reports (2021)
              Spatial process models popular in geostatistics often represent the observed data as the sum of a smoothunderlying process and white noise. The variation in the white noise is attributed to measurement error,or micro-scale variability, and is called the “nugget”. We formally establish results on the identifiabilityand consistency of the nugget in spatial models based upon the Gaussian process within the framework ofin-fill asymptotics, i.e. the sample size increases within a sampling domain that is bounded. Our workextends results in fixed domain asymptotics for spatial models without the nugget. More specifically, weestablish the identifiability of parameters in the Matérn covariogram and the consistency of their maximumlikelihood estimators in the presence of discontinuities due to the nugget. We also present simulationstudies to demonstrate the role of the identifiable quantities in spatial interpolation.
                Cover page: On identifiability and consistency of the nugget in Gaussian spatial process models
                • Article
                • Peer Reviewed
                Research Reports (2018)
                With continued advances in Geographic Information Systems and related computationaltechnologies, statisticians are often required to analyze very large spatialdatasets. This has generated substantial interest over the last decade, already toovast to be summarized here, in scalable methodologies for analyzing large spatialdatasets. Scalable spatial process models have been found especially attractive dueto their richness and flexibility and, particularly so in the Bayesian paradigm, due totheir presence in hierarchical model settings. However, the vast majority of researcharticles present in this domain have been geared toward innovative theory or morecomplex model development.Very limited attention has been accorded to approachesfor easily implementable scalable hierarchical models for the practicing scientist orspatial analyst. This article devises massively scalable Bayesian approaches that canrapidly deliver inference on spatial process that are practically indistinguishable frominference obtained using more expensive alternatives. A key emphasis is on implementationwithin very standard (modest) computing environments (e.g., a standarddesktop or laptop) using easily available statistical software packages without requiringmessage-parsing interfaces or parallel programming paradigms. Key insights areoffered regarding assumptions and approximations concerning practical efficiency.
                  Cover page: Practical Bayesian Modeling and Inference for Massive SpatialDatasets On Modest Computing Environments
                  • Article
                  • Peer Reviewed
                  Technical Reports (2015)
                  Power side-channel attacks are a very effective cryptanalysis technique that can infer secret keys of security ICs by monitoring a chip’s power consumption. Since the emergence of practical attacks in the late 90s, they have been a major threat to many cryptographic-equipped devices including smart cards, encrypted FPGA designs, and mobile phones. Designers and manufacturers of cryptographic devices have in response developed various countermeasures for protection. Attacking methods have also evolved to counteract resistant implementations. This paper reviews foundational power analysis attack techniques and examines a variety of hardware design mitigations. The aim is to highlight exposed vulnerabilities in hardware-based countermeasures for future more secure implementations.

                  Pre-2018 CSE ID: CS2015-1012

                    Cover page: Power Side Channels in Security ICs: Hardware Countermeasures
                    • Article
                    Research Reports (2021)
                    Joint modeling of spatially-oriented dependent variables are commonplace in the environmentalsciences, where scientists seek to estimate the relationships among a set of environmental outcomesaccounting for dependence among these outcomes and the spatial dependence for each outcome. Suchmodeling is now sought for very large data sets where the variables have been measured at a very largenumber of locations. Bayesian inference, while attractive for accommodating uncertainties through theirhierarchical structures, can become computationally onerous for modeling massive spatial data sets becauseof their reliance on iterative estimation algorithms. This manuscript develops a conjugate Bayesianframework for analyzing multivariate spatial data using analytically tractable posterior distributions thatdo not require iterative algorithms. We discuss differences between modeling the multivariate responseitself as a spatial process and that of modeling a latent process. We illustrate the computational andinferential benefits of these models using simulation studies and real data analyses for a Vege Indicesdataset with observed locations numbering in the millions.
                      Cover page: High-dimensional MultivariateGeostatistics: A Conjugate BayesianMatrix-Normal Approach
                      Top