phd dissertation stanford university

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Phd dissertation stanford university

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Multiple authorship of a published paper should be addressed by clearly designating, in an introduction, the role that the dissertation author had in the research and production of the published paper. The student must have a major contribution to the research and writing of papers included in the dissertation. Written permission must be obtained for all copyrighted materials; letters of permission must be uploaded electronically in PDF form when submitting the dissertation.

The submitted material must be in a form that is legible and reproducible as required by these specifications. The Office of the University Registrar will approve a dissertation if there are no deviations from the normal specifications that would prevent proper dissemination and utilization of the dissertation.

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The American Psychological Association APA publication guidelines normally apply to GSE doctoral dissertations, but is not required if the advisor and relevant committees determine that an alternative, and academically acceptable, protocol is more appropriate. The inclusion of published papers in a dissertation is the prerogative of the major department. Where published papers or ready-for-publication papers are included, the following criteria must be met:.

There must be an introductory chapter that integrates the general theme of the research and the relationship between the chapters. The introduction may also include a review of the literature relevant to the dissertation topic that does not appear in the chapters. Multiple authorship of a published paper should be addressed by clearly designating, in an introduction, the role that the dissertation author had in the research and production of the published paper.

The student must have a major contribution to the research and writing of papers included in the dissertation. Written permission must be obtained for all copyrighted materials; letters of permission must be uploaded electronically in PDF form when submitting the dissertation. The submitted material must be in a form that is legible and reproducible as required by these specifications.

The Office of the University Registrar will approve a dissertation is there are no deviations form the normal specifications that would prevent proper dissemination and utilization of the dissertation. If the published material does not correspond to these standards, it will be necessary for the student to reformat that portion of the dissertation.

Multiple authorship has implications with respect to copyright and public release of the material. Be sure to discuss copyright clearance and embargo options with your co-authors and your advisor well in advance of preparing your thesis for submission. Enter your keywords for search.

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Adaptable stem cell differentiation in the adult Drosophila intestinal epithelium []. Sanders, Erin Nicole, author. Resident stem cells replace damaged and dying cells by dividing and differentiating, stimulated by signals emanating from the dying cells themselves. How stem cells precisely tune their differentiation to match division and death rates in real time remains a largely open question.

First, I present a novel method for live imaging the adult Drosophila midgut while still intact in the living organism. With this method I am able to monitor in real time the dynamics of stem cell divisions, differentiation, and loss in the midgut. I am able to precisely measure Notch activity over time and find that a threshold level of Notch defines stem cells from their intermediate progenitors, enteroblasts.

Second, I find that injury alters differentiation rates and blurs normally distinct features of stem cells, enteroblasts and terminally differentiated enterocytes. I measure rates of Notch activation and deactivation, indicators of where cells are in the differentiation process and find that both are accelerated in injured midguts. Injury raises Notch activity in mitotic stem cells beyond the enteroblast threshold and maintains Notch activity longer in maturing cells.

The injury response pathway JAK-STAT appears to play a role in the acceleration of Notch deactivation kinetics and blurring between enteroblast and enterocyte characteristics, suggesting that JAK-STAT plays more of a role in regulating enterocyte maturation than enteroblast specification. Finally, I find that food consumption affects population wide Notch activity levels and excessive activation of insulin signaling might also lead to blurring between stem and enteroblast identities.

Online 4. Adaptive and efficient batch reinforcement learning algorithms []. Liu, Yao, author. However, real-world applications of reinforcement learning algorithms often cannot have high-risk online exploration. To bridge this gap, this dissertation investigates how to perform reinforcement learning from an offline dataset, named batch reinforcement learning methods.

We provide theoretically justified new algorithms, as well as empirical validation in simulation and real datasets. More specifically, this dissertation studies the two main aspects of batch reinforcement learning. How to evaluate a policy given a fixed dataset collected by other policies?

Offline policy evaluation is a challenging counterfactual reasoning problem, in these partial-information and sequential settings. The solution to this problem relies on two types of estimators: direct model-based estimators and importance re-weighing estimators. We propose a model-based estimator with mean-square error bound and analyze a variance reduction heuristic for importance sampling.

How to learn a new policy from a fixed dataset collected by other policies? Prior theoretical justification relies on strong assumptions about the data distribution and thus is less informative to guide practice. We propose the idea of pessimism to constrain the policy search space and avoid instability. Following this intuition, we proposed three new algorithms: the first convergent policy gradient, value function learning with finite sample error bounds, and avoiding the overfitting in direct policy gradient.

The primary contribution of this dissertation is advancing the foundations of batch RL and develop batch RL algorithms with provable guarantees under realistic assumptions. One of the driving goals is finite-sample error bounds for algorithms in function approximation settings.

To that end, this dissertation makes progress in both policy evaluation and policy learning problems by studying the theory of these problems and using the theoretical insights to derive new algorithms with strong empirical performance as well. Online 5. Accurate and efficient simulation of the fluid flow and transport in the subsurface remains a challenging task.

In implicit pressure explicit saturation IMPES formulation, the elliptic flow equation is discretized implicitly, whereas the hyperbolic transport equation is explicit. To ensure stability, the latter requires the choice of a sufficiently small timestep size, such that the local Courant-Friedrichs-Lewy CFL condition is not violated. This restriction leads to a large number of small global timestep sizes which drives the simulation very expensive.

In order to take advantage of the local nature of the CFL condition, many local time-stepping methods were proposed. We eliminate the most expensive step of the original algorithm and describe the details of local timestep size assignment to ensure stability for non-convex non-monotone flux functions. We investigate the performance of the scheme for a number of model problems with various physics including the tracer problem, immiscible two-phase flow in presence of buoyancy and the black oil model.

We show that with modified ACTI, the size of the global timestep for explicit update of the transport can be chosen several orders of magnitude larger than that of standard IMPES. As a result, a speed-up of an order of magnitude is achieved in the number of flux and cell updates while maintaining negligible discrepancies in the solution when compared to conventional IMPES.

Jenny, Time adaptive conservative finite volume method," Journal of Computational Physics, vol. Online 6. Adaptive experiments and a rigorous framework for type I error verification and computational experiment design []. Sklar, Michael Benjamin, author.

In recent decades, modern statistics has found something to trade: at the price of additional complexity and the loss of Gaussian behavior of our estimators, we can get faster, more robust, more flexible, and more efficient experiments through the use of adaptive designs. This thesis covers breakthroughs in several areas of adaptive experiment design: i Chapter 2 Novel clinical trial designs and statistical methods in the era of precision medicine.

Online 7. Adaptive games for education: performance and engagement in preschoolers []. Active learning—allowing learners to direct their own studies—is a key part of some educational theories and practices but is not well-understood in terms of its underlying mechanisms or effectiveness at different ages. Meanwhile, cognitive psychology studies have found that adults can benefit from being given self-direction in some types of tasks, and school-aged children can also benefit in simplified versions of these paradigms.

Here I extend these past studies in two ways: studying younger children, whose metacognitive skills which likely play a role in effective self-directed learning are still developing, and using real-world stimuli for which children may have varying degrees of prior knowledge. In this multi-session study, I explore whether an active learning advantage can be found in preschoolers ages using a tablet game that teaches letter and number recognition over a week.

I compare performance and engagement in two conditions: active learners are able to control time allocation between letter and number games that adapt to their level of knowledge; control learners follow a standard curriculum with no adaptive game dynamics. Despite the challenges presented by the adaptive curriculum, I find that active learners performed just as well as their peers who followed a standard curriculum.

Online 8. Advances in data-driven financial econometrics and item response theory : theory and applications []. Liu, Chenru, author. The first part of this thesis focuses on a theoretical stochastic volatility model for option pricing. We propose a continuous-time stochastic volatility model based on an arithmetic Brownian motion: a one-parameter extension of the normal stochastic alpha-beta-rho SABR model.

Using two generalized Bougerol's identities in the literature, the study shows that proposed model has a closed-form Monte-Carlo simulation scheme and that the transition probability for one special case follows Johnson's SU distributiona popular heavy-tailed distribution originally proposed without stochastic process. It is argued that the SU distribution serves as an analytically superior alternative to the normal SABR model because the two distributions are empirically similar. The second part of this thesis gives a bias corrected algorithm of the least square Monte Carlo LSM algorithm for pricing American options.

The traditional LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues.

In addition to option pricing, the third part of this thesis studies item response theory IRT and its application to foraging data analysis. A fundamental problem in biology is to understand how the differences among components, or variation in function, contribute to collective behavior. To tackle this problem, we first give a new approach to latent trait modeling in IRT, then design corresponding experiments and finally apply IRT to data analysis.

We also give detailed explanation and further discussion for experiment results. Online 9. Advancing retinal prostheses from two to three dimensions []. Huang, Tiffany Wanshing, author. Currently, no therapy exists for such condition, and the loss of sight is permanent.

Subretinal prostheses aim to restore sight via electrical stimulation of the surviving secondary neurons in the retina. This is a rapidly expanding area of research, especially with the boom of neural interfaces in recent years, with many groups focusing on various aspects of such devices. Our group is pursuing a full-system approach, from fabrication of the devices to in-vivo testing and to clinical trials.

In our system, silicon photovoltaic pixels convert light into electrical current to stimulate the nearby neurons. Further improvement of visual acuity requires miniaturization of the pixels and faces many challenges. Simply scaling the pixel size down with flat bipolar arrays decreases the penetration depth of the electric field into the tissue, increasing the stimulation threshold beyond the capacity of even the best charge-injection material.

To enable smaller pixels, moving from flat electrodes into a three-dimensional configuration helps mitigate these issues. Additionally, with planar junction diodes isolated by deep reactive ion etched DRIE trenches, carrier recombination at the pixel side walls may limit the light-to-current efficiency, and growing side-wall oxide results in oxidation-related stress in the Si, especially as pixels scale down. We addressed these limitations by transitioning from planar to vertical junction diodes.

In this thesis, I will discuss the limitations on reducing the pixel size and overcoming these limitations by using 1 pillar electrodes, 2 honeycomb electrodes, and 3 vertical junction diodes validated ex-vivo and in-vivo. I present the design and fabrication processes of such devices and also demonstrate the resulting photodiode functionality, electrode performance, retinal integration with 3-D devices in-vivo and electrophysiological responses.

I conclude by discussing the remaining work toward full utilization of such devices and moving toward single-cell resolution. Online The age of the perplexed : translating nature and bodies between the Ottoman Empire and Europe, []. Yildirim, Duygu, author. It reveals the intertwined stories of religious conflict, natural history, medical authority, and translation which illustrate an important episode of intellectual genealogy fostering the idea of human difference in physical, spiritual, and moral domains.

Over the course of their increasing interactions stimulated by religious, diplomatic, commercial, and intellectual agendas, Ottoman and European scholars faced a set of questions that still continues to resonate. Do all human beings have an essential nature? Were human bodies interchangeable in the medical context, or were "Turkish" bodies essentially different both emotionally and physically?

Perplexed by the dilemma of understanding one other, both Ottomans and Europeans saw cultural encounters as an opportunity to raise questions about the universality of knowledge and human nature. This project demonstrates the boundaries of what constituted knowledge exchange in an age of early modern globalization and religious turmoil. I approach the question of knowledge exchange from the differing perspectives of a diverse community of Ottoman and European scholars, historians, physicians, natural philosophers, apothecaries, drug traders, preachers, and jurists between Istanbul and Europe.

Each of these episodes reveals how efforts to bridge and differentiate distinct human societies were conducted through the medium of translation. Rather than seeing translation as a unidirectional process, this dissertation delineates what made translations successful or contingent failures by explaining how they created new perceptions of nature, human bodies, faith, and uncertainty, both in the Ottoman Empire and Europe.

While a growing number of Ottoman and European scholars were certain about why having foreign knowledge would make a difference for them, they also realized that knowledge in circulation was too unstable and uncertain to be definitive. Consequently, the rhetoric of uncertainty became a new mode of inquiry and a highly productive strategy; factual and trustworthy knowledge was not necessarily the first intention in cross-cultural scholarly engagements. The knowledge that emerged at the nexus of the Islamic world and Europe, with all of its contradictions and ambiguities, raised critical questions about the limits of human knowledge.

Fundamentally, these seventeenth-century conversations offered new possibilities to accommodate anxieties about what it meant to essentialize other societies—the people, their bodies, and their faith. Algorithmically guided human decision making in criminal justice and beyond [].

Lin, Zhiyuan, author. However, due to the high-stakes nature of decisions in criminal justice, the efficacy and equity of algorithms in judicial procedures are sometimes challenged and how algorithms can be appropriately incorporated into judicial processes remains a complex question to answer. In this dissertation, we present both applications demonstrating how algorithms may help human decisions in criminal justice and algorithmic tools for human-centered decision making.

We start by showing risk assessment tools, one of the most common algorithm applications in criminal justice, can outperform humans on predicting recidivism under ecologically valid settings in a randomized controlled experiment. We then demonstrate how both predictive and non-predictive algorithms can help with better human decision making in criminal justice by reducing incarceration time and mitigating racial bias in two real-world applications.

Finally, inspired by decision making problems encountered in aforementioned applications, we propose two algorithmic methods that aid human decision making through modeling the structure of evolving probabilistic predictions and estimating the preference of the human decision maker while searching for optimal policy configurations during sequential experimentation.

Together, this dissertation contributes to the understanding of interplay between humans and algorithms in criminal justice and other domains by presenting an interconnected collection of randomized controlled experiments, real-world applications, and innovative computational methods. Algorithms for black-box safety validation []. Corso, Anthony Louis, author. Since human lives are at risk in these applications, we require rigorous safety validation before deployment.

Traditional safety validation approaches such as real-world testing and scenario-based testing in simulation are not scalable to complex systems and environments and may miss unforeseen failures. Formal verification techniques also lack the scalability required for large scale autonomy. The thesis address the safety validation problem with black-box sampling techniques, which assume no knowledge of the design of the autonomous system.

The system takes actions in a stochastic environment and failures are discovered by sampling environmental disturbances. The black-box assumption allows for better scalability to complex autonomous systems and sampling can be combined with machine learning to discover unforeseen failures. Previous black-box safety validation approaches have been based on optimization, path-planning, reinforcement learning and importance sampling.

Although successful for many safety validation applications, existing algorithms may have poor interpretabiliy, scalability, and efficiency. Black-box sampling approaches can provide example failure trajectories but do not provide a high-level description of failures, as scenario-based approaches do. We present a new technique for generating failure descriptions in the form of signal temporal logic specifications on the environment disturbances.

The specifications are optimized with genetic programming to produce failure examples and can used to gain insight into why a failure occurred. A key contribution of this thesis is the proposal and analysis of a state-dependent sampling distribution to approximate the distribution over failures. The use of the state of the environment produces a more efficient sampling distribution than baseline importance sampling approaches, but may be limited by the size of the state space.

To improve scalability, we propose a decomposition technique for multi-agent validation tasks. Each subproblem is solved independently and the results are combined for better performance than learning from scratch. During the design of an autonomous system, safety validation is performed repeatedly, requiring a large computational expense.

We propose a transfer learning technique that can reduce the number of required samples and lead to better performance. Knowledge from previous validation tasks is transferred to new tasks in the form of value functions that are combined using a learned set of attention weights. Results show improved knowledge transfer between tasks compared to baseline techniques. The safety validation algorithms presented in this work are tested on two gridworld scenarios and two driving scenarios.

A simple gridworld scenario is used to illustrate important safety validation concepts while a gridworld with multiple adversaries is used as a test case for multi-agent validation. A rules-based autonomous driving policy is tested in a crosswalk scenario with a pedestrian and a T-intersection scenario with multiple vehicles.

It is shown that the presented algorithms can improve the interpretability, scalability, and efficiency of safety validation. Algorithms for multi-agent robotic assembly planning []. Brown, Kyle Jordan, author. Our work touches on many pieces of the "factory autonomy stack.

We propose an efficient method to solve such problems. The two keys to our algorithm's efficiency are its decoupling of task assignment and route planning, and its ability to take advantage of situations where some robots can be delayed in their own schedule without causing any negative impact on the factory's overall performance.

The next major contribution of the thesis is a family of replanning algorithms geared toward an online version of our discrete factory problem. In the online setting, the factory command center periodically receives new manufacturing workloads that must be quickly incorporated into the overall schedule.

Theses and dissertations Result includes all theses and dissertations — from all sources — held in the Stanford Libraries and Digital Repository. Toggle facets Refine your results. Stanford student work. Stanford school or department. At the Library 4, Online 2, Resource type. Book 4, English 4, Pauly, John John M. Call number. Electron beams 57 Algorithms 50 Semiconductors 48 Traveling-wave tubes 46 Integrated circuits 42 Radio 40 Electric networks 32 Plasma Ionized gases 32 Electronic digital computers 31 Signal processing 31 Ionospheric radio wave propagation 29 Digital filters Mathematics 28 Transistors 28 Amplifiers, Vacuum-tube 24 Klystrons 24 Silicon 24 Radio waves 23 Electric circuits 22 Ionosphere 22 Microwaves 21 more topics.

Organization as author. Stanford University. Department of Electrical Engineering 4, Stanford University. Computer Science Department 2. View results as: View Normal Gallery Brief. Sort by relevance relevance new to the Libraries year new to old year old to new author title. Online 1. Advancing retinal prostheses from two to three dimensions []. Huang, Tiffany Wanshing, author. Currently, no therapy exists for such condition, and the loss of sight is permanent.

Subretinal prostheses aim to restore sight via electrical stimulation of the surviving secondary neurons in the retina. This is a rapidly expanding area of research, especially with the boom of neural interfaces in recent years, with many groups focusing on various aspects of such devices. Our group is pursuing a full-system approach, from fabrication of the devices to in-vivo testing and to clinical trials.

In our system, silicon photovoltaic pixels convert light into electrical current to stimulate the nearby neurons. Further improvement of visual acuity requires miniaturization of the pixels and faces many challenges. Simply scaling the pixel size down with flat bipolar arrays decreases the penetration depth of the electric field into the tissue, increasing the stimulation threshold beyond the capacity of even the best charge-injection material.

To enable smaller pixels, moving from flat electrodes into a three-dimensional configuration helps mitigate these issues. Additionally, with planar junction diodes isolated by deep reactive ion etched DRIE trenches, carrier recombination at the pixel side walls may limit the light-to-current efficiency, and growing side-wall oxide results in oxidation-related stress in the Si, especially as pixels scale down.

We addressed these limitations by transitioning from planar to vertical junction diodes. In this thesis, I will discuss the limitations on reducing the pixel size and overcoming these limitations by using 1 pillar electrodes, 2 honeycomb electrodes, and 3 vertical junction diodes validated ex-vivo and in-vivo. I present the design and fabrication processes of such devices and also demonstrate the resulting photodiode functionality, electrode performance, retinal integration with 3-D devices in-vivo and electrophysiological responses.

I conclude by discussing the remaining work toward full utilization of such devices and moving toward single-cell resolution. Online 2. Better optimization algorithms, moving beyond the classics []. Jiang, Qijia, author. More specifically, 1 we give an iteration complexity-optimal algorithm for minimizing highly smooth convex function with Lipschitz continuous higher-order derivatives; 2 we investigate the complexity of optimizing convex, non-smooth functions with a highly parallel oracle, giving an algorithm with improved depth compared to the state-of-the-art and obtaining tighter lower bound characterizing when the parallel information helps compared to its fully sequential counterpart; 3 we propose an efficient sketching-based distributed algorithm with lightweight communication that can return high-accuracy solution for problem having composite structure.

Through a better understanding of the fundamental barrier to problem efficiency on one hand, and the design of practical algorithms addressing requirement of modern computation model on the other, the thesis offers glimpse of the vast opportunities that the role of optimization remains to play for data science in either direction.

Online 3. Circuit techniques for compact and lightweight high-voltage DC power supplies []. Park, Sanghyeon, author. The size and weight of the circuit often limit the portability of the system and the range of applications. Smaller, faster, and more efficient power supplies will enable new scientific and commercial advances. My thesis presents circuit techniques to improve the performance of high voltage dc generators. A traditional way of generating a high dc voltage involves a step-up transformer built with a bulky magnetic core and a Cockcroft-Walton voltage multiplier, assembled on an FR4 printed circuit board and sometimes potted in insulating epoxy.

Recently, many advances are being made in wide-bandgap semiconductor devices, sub-millimeter size passive components, and high-frequency circuit design methodologies. Those technological developments enable new techniques to design a high voltage power supply. This dissertation presents several new ways to build a high voltage dc generator: a planar PCB transformer that simplifies high-voltage isolation transformer design, a new voltage multiplier topology that exhibits less output voltage drop than the traditional Cockcroft-Walton multiplier, and foldable converters assembled on a flexible PCB to miniaturize kilovolts-level power supplies down to the sub-centimeter scale.

My dissertation includes the motivation for investigating each of those circuit techniques and relevant power supply designs as well as application examples. I experimentally validate that by using those techniques appropriately, one can build a smaller and lighter power supply that exceeds traditional boundaries of high voltage generator performance.

Online 4. Compression of raw genomic data []. Chandak, Shubham, author. Several large scale sequencing projects for humans and other species are expected to further increase the volume of this data. While the initial progress was led by second generation high-throughput sequencers such as Illumina, more recently there has been increasing interest in third generation sequencers like Oxford Nanopore that enable real-time and portable sequencing of long reads.

In this context, compression techniques play a crucial role in enabling efficient storage and transfer of this data. Unfortunately, the traditional general-purpose compressors like Gzip are unable to fully exploit the inherent redundancy in this data. Furthermore, in many cases the data is noisy, and it is possible to deploy lossy compression algorithms that can reduce the storage space without adverse impacts on the data quality for downstream analysis.

This thesis presents two specialized compressors for genomic data, focusing on raw genomic data which consists of sequencing reads FASTQ format as well as raw signal data produced by nanopore sequencing FAST5 format. We first describe SPRING, which is an efficient compressor for unaligned single and paired-end genomic reads, supporting various lossless and lossy compression modes.

Next, we discuss lossy compression of nanopore raw signal data using LFZip, which is a general-purpose lossy compressor for time series and sensor data. We also discuss the evaluation of the impact of lossy compression on the performance of downstream applications like basecalling, consensus and methylation calling. Online 5. Computational fluorescence microscopy for three dimensional reconstruction [].

Ikoma, Hayato, author. Computational algorithms tailored for specific experimental settings are demanded to solve given tasks such as denoising, spectral unmixing, 3D localization and reconstruction, and ptychography. In this thesis, we present the reconstruction of dense and sparse three dimensional fluorescent volumes.

In the first half, we present a volumetric reconstruction method designed for 3D fluorescence imaging of biological samples in the low-light regime. Our method deconvolves a captured focal stack through optimization. As deconvolution is an ill-posed problem, the uniqueness of the solution is imposed through regularization.

We formulate the objective function as a sum of a data fidelity term and a regularization term, and minimize it using the alternating direction method of multipliers algorithm. The data fidelity is accurately evaluated with a negative log-likelihood function based on a mixed Poisson-Gaussian model of photon shot noise and camera read noise, which are both present in low-light imaging.

Among several possible regularization strategies, we show that a Hessian-based regularizer is most effective for describing locally smooth features present in biological specimens. We demonstrate its performance for fixed and live cell imaging, showing its applicability as a tool for biological research. In the second half, we introduce a hybrid optical-electronic computing approach to three dimensional localization microscopy.

Driven by artificial intelligence, this approach learns a set of depth-dependent point spread functions PSFs and a localization network jointly in an end-to-end fashion, co-designing an optical imaging system and a neural network. We also present a custom grayscale lithography process to fabricate freeform diffractive optical elements that optically implement the designed PSFs and outline several biological experiments with fixed and live cells that demonstrate the efficacy of the proposed computational microscopy approach.

Online 6. Convex optimization methods for adaptive radiation therapy []. Fu, Anqi, author. In this dissertation, we present an optimization-based framework for adaptive radiation treatment planning. We focus on two specific planning challenges: 1 satisfying dose-volume constraints and 2 handling nonlinear patient health dynamics.

For each situation, we show how to formulate the treatment planning problem as a nonconvex optimization problem and obtain a good estimate of the solution by solving a series of convex approximations. We demonstrate the effectiveness of our method on several clinical examples. Finally, we release an open-source Python library that implements our method using generic convex solvers. The last part of this dissertation concerns applications of optimization beyond radiation therapy.

We develop a domain-specific language DSL for formulating and solving a broad class of convex optimization problems. Then, we describe an implementation of our DSL in R, a popular programming language for statistical modeling. Our resulting software package, CVXR, allows users to construct optimization problems in a natural mathematical syntax. CVXR automatically verifies the problem's convexity and converts it into the standard form required by a specific solver.

We illustrate CVXR's modeling framework with a variety of examples drawn from statistics, engineering, and radiation treatment planning. Online 7. Creating hardware component knowledge bases from PDF datasheets []. Hsiao, Luke Wen-syong, author. These databases store information about hardware components that allow designers to find the components they need. However, creating detailed hardware databases requires hundreds of thousands of hours of manual data entry. As a result, existing databases are often proprietary, incomplete, and may have sporadic human data entry errors.

Knowledge base construction KBC systems help automate the process of creating and populating structured databases and have been applied effectively to many domains. Knowledge base construction techniques reduce dependency on human input, making it faster, easier, and cheaper to build these databases. This dissertation presents a machine-learning-based approach for creating hardware component databases directly from manufacturers' published component datasheets.

Extracting data directly from datasheets is challenging for three reasons. First, the data is relational in nature; accurate interpretation relies on non-local context. Second, datasheets are filled with technical jargon.

Third, datasheets are PDFs, a format that decouples visual locality from locality within the document. These challenges illuminate why human input is required, but human input is error-prone, time-consuming, and expensive.

Instead of relying solely on human input, the approach of using a rich data model, weak supervision, data augmentation, and multi-task learning in this dissertation presents a more automated alternative. When utilized effectively, these machine-learning techniques create large knowledge bases cheaply and in just days. This dissertation consists of three parts.

First, it presents Fonduer, a novel knowledge base construction system for richly formatted data based on a multimodal data model and weak supervision. It motivates Fonduer by studying the challenging properties of richly formatted data like the PDF datasheets electronics manufacturers use to publish component specifications. These insights lead to developing the building blocks necessary to enable automated information extraction from hardware datasheets.

Fonduer is validated across various domains beyond only hardware datasheets by creating large knowledge bases in days. Second, this dissertation shows how Fonduer can be used to build hardware component knowledge bases in practice. The multimodal information that Fonduer captures provides signals utilized in training data generation and the augmentation of deep learning models for multi-task learning. An evaluation of this approach on datasheets of three types of components achieves an average quality of 0.

Third, this dissertation demonstrates the utility of Fonduer with end-to-end applications and empirical results applied to real-world use cases. Two end-to-end applications, the enhancement of product catalogs with thumbnail images and the analysis of electrical characteristics, demonstrate that hardware component knowledge bases created in days make hardware component selection easier. Together, these results show three things. First, it is possible to automate the generation of hardware component knowledge bases.

Second, these generated knowledge bases can be of higher quality than existing human-curated knowledge bases. Finally, these higher-quality knowledge bases open the door to innovative applications and tools for designing electronics. Online 8. Data-driven methods in laboratory-scale study of enhanced oil recovery [].

Anderson, Timothy Isaac, author. In the petroleum sciences, a significant body of work has arisen applying such techniques to modeling and analysis of reservoir-scale data, but laboratory-scale data and applications have received relatively less attention. In this work, we develop data-driven methods for assimilating experimental data from two domains: image-based characterization of shale source rocks and in-situ combustion kinetics modeling.

In the first half of this work, we develop methods for modality translation and synthesis of shale images, specifically for reconstructing or synthesizing 3D volumetric data when only 2D training data is available. We propose an experimental and computational workflow applying image translation and super-resolution models to predict destructive shale microscopy images from non-destructive input data.

We then propose an approach to regularizing image-to-image models to improve volume reconstruction using only 2D training data. The results show that our models improve the volume prediction in terms of morphological image features and create image volumes suitable for flow simulations. We also propose a fundamentally new approach to synthesizing porous media images.

Our approach is based on generative flow models and is the first approach that can generate grayscale and multimodal image data from only 2D training images. We apply this method to synthesizing baseline sandstone and limestone samples, as well as scanning electron microscopy and dual-mode focused ion beam milled scanning electron microscopy and nano-computed tomography images.

The synthetic images are similar to the ground truth data in both appearance and morphological descriptors as gauged by Minkowski functionals distributions. In the second half, we develop methods for modeling and upscaling in-situ combustion chemical kinetics.

We develop a data-driven model for predicting oxygen consumption during heavy oil combustion directly from laboratory data, and apply this method to simulating combustion kinetics and analyzing heating schedules for ramped temperature oxidation experiments. Our results show that a data-driven model accurately predicts heavy oil oxidation from a relatively small sample of experimental data.

We then present a fully-automated parameter estimation and uncertainty quantification approach for in-situ combustion chemical reaction models. Our approach is generalized to any in-situ combustion reaction model and requires no manual history matching or parameter initialization.

We apply this parameter calibration workflow to multiple reaction models for two different heavy oil samples. Our results show that reaction schemes can differ significantly in both stoichiometry and kinetics parameters when calibrated to different heavy oil samples. Very different reaction models were shown to provide fits of similar accuracy to the same heavy oil sample.

Our results also suggest that it is the number of stages in pseudocomponent cracking, not the number of pseudocomponents or reactions, that most impacts the ability of a reaction model to fit oxidation data for a heavy oil sample. Overall, this work demonstrates the capabilities of using data-driven modeling to expand our characterization capabilities and improve our understanding of enhanced oil recovery processes at the laboratory-scale.

It is our hope that the work presented here will enable new directions in nanoscale characterization of shales and upscaling of in-situ combustion chemical reaction models. Online 9. Deep learning for automated analysis of dendritic spines in volumetric microscopic images []. Xiao, Xuerong, author.

They are considered a major readout of the cortical synaptic function and neural connectivity. A mature dendritic spine consists of a spine head connected to the dendritic branch by a thin neck. With novel experience, thin new dendritic spines appear, characterized by small spine heads. At the same time, pre-existing spines can undergo further plastic changes such as enlargement of spine heads.

These positive changes in dendritic spine density and structure are thought to constitute a memory trace of novel learning. Dendritic spines that undergo these changes cluster discretely on a neuronal dendritic tree where stimulated inputs are making synaptic contacts. On the other hand, neighboring un-stimulated dendritic spines can exhibit shrinkage over time, and eventually disappear, a process that is exacerbated in neurodegenerative diseases such as Alzheimer's or Parkinson's disease.

In-depth description of how dendritic spine numbers and structure change with normal experience is critical for understanding the scope of excitatory synapse plasticity in healthy brains, and how it is impacted in pathological situations. With some exceptions, most of the analysis of dendritic spines is performed by manual counting, sometimes supplemented with sorting spines into predetermined categories based on their overall shape.

This limited approach is further exacerbated by the fact that the overall numbers of dendritic spines on any given pyramidal neuron can run in the tens of thousands. The more comprehensive analysis of dendritic spines under any condition is not attainable with current tools.

In addition to the number of spines, there are other features of spines that humans cannot easily extract, including the spine shapes, spine head volumes, spine neck lengths, and spine clusters, all of which are informative about neural activities and hence motivated the need for automation. Here, we show an automated deep learning based approach that detects, segments, and characterize dendritic spines in microscopic volumes in an unbiased manner that is needed to fully capture how spines change with normal experience, and during dysfunctional disease states.

Our analysis covered both two-dimensional and three-dimensional data. The two-dimensional analysis was an initial study on the application of deep learning in maximum intensity projected microscopic images. It paved the path for the three-dimensional analysis that is capable of capturing the full shapes of dendritic spines in the volumetric data.

The detection models trained on confocal microscopic images can be generalized to two-photon microscopic images.

CHEAP DISSERTATION CONCLUSION WRITERS WEBSITES USA

Multiple authorship of a published paper should be addressed by clearly designating, in an introduction, the role that the dissertation author had in the research and production of the published paper. The student must have a major contribution to the research and writing of papers included in the dissertation. Written permission must be obtained for all copyrighted materials; letters of permission must be uploaded electronically in PDF form when submitting the dissertation.

The submitted material must be in a form that is legible and reproducible as required by these specifications. The Office of the University Registrar will approve a dissertation is there are no deviations form the normal specifications that would prevent proper dissemination and utilization of the dissertation. If the published material does not correspond to these standards, it will be necessary for the student to reformat that portion of the dissertation.

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Enter your keywords for search. News Directory Events Give. Toggle navigation. Current Students Toggle navigation. You are here Home. Dissertation Content. Acceptable problems are those that: pose a puzzle to the field at a theoretical, methodological, or policy level; make analytical demands for solution, rather than mere cataloging or descriptive demands; and can yield to a reasonable research methodology.

See the Registrar's COVID and Academic Continuity page for information for students, faculty, and staff relevant to classes and academic activities and administration. This new procedure should be used by PhD, JSD, DMA, and Engineer students who need to virtually gather signatures from each reading committee member, and will enable them to fully satisfy both the title page and reading committee signature page requirements.

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This will allow sufficient time for staff to process and enter forms. Note: These preparation guidelines are minimum standards for professional presentation of your doctoral work. The Office of the University Registrar, which is responsible for facilitating dissertation and thesis submission, encourages students to ask questions about format before final preparation of the manuscript.

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