VictorJuiz/thesis huggingface dataset:
Emergent Ferroic Order at Oxide Heterointerfaces

This work focuses on complex-oxide superlattices as a pathway to numerous forms of emergent phenomena due to strong interfacial interactions present in unit-cell precise structures. Superlattices based on ferroelectric and dielectric materials can elicit new forms of ferroelectricity, exotic dipolar textures, and distinctive domain structures. Here, I place two different ferroelectric phases in the PbZr1-xTixO3 system and use superlattice design as a proxy for local composition – asking what happens when the overall chemistry is that of the morphotropic phase boundary, but the individual layers are far away from that boundary? The intimate interfacing of these dissimilar materials results in a unique combination of effects: simultaneous large polarization magnitude and large permittivity. The material effectively acts like a combination of the robust parent ferroelectrics and an interfacial region that akin to the phase-boundary. Next, I analyze relaxor-like behavior, which is typically associated with chemical inhomogeneity and complexity in solid solutions, in atomically precise (BaTiO3)n/(SrTiO3)n symmetric superlattices. Dielectric studies reveal frequency dispersion of dielectric response which increases in magnitude as the periodicity decreases. Techniques such as Vogel-Fulcher analysis and bond valence molecular dynamics simulations reveal that relaxor-like behavior in short-period superlattices arises from temperature-driven size variations of antipolar stripe domains in contrast to the more thermally stable dipolar configurations in long-period superlattices. Moreover, the size and shape of antipolar domains are tuned by superlattice periodicity following Kittel’s Law thus providing an artificial route to relaxor-like behavior which may expand the ability to control desired properties in these complex systems. Ultimately, I show that unit-cell-precise deposition provides a pathway to design novel heterostructures and thus access interfacial-driven phenomena, posing the question: can control at a single unit-cell be achieved with designer chemical ordering and structure? Such questions will motivate the community’s further pursuit of these matters. The current work paves the way for future research in the realization of novel approaches to ferroelectricity stemming from interfacial interactions, which will continue to be more relevant as devices get smaller.

Advisor: Martin, Lane W
Tailoring Nanoscale van der Waals Materials via Atomic Doping, Conversion, and Confinement

Control over the atomic structure of nanoscale materials allows for tailoring of their electronic and physical properties to enhance or alter their applications. The elemental and structural makeup of the nanomaterials dictates which methods of atomic control can be utilized to tailor their atomic structure. In this dissertation, we focus on nanomaterials comprised of low-dimensional building blocks, such as graphene, hexagonal boron nitride (h-BN), and transition metal chalcogenides (TMC), all members of the class of van der Waals (vdW) structures. We will leverage the 2-dimensional (2-D) and 1-D vdW nature of these nanomaterial systems to tailor their atomic and electronic structure through doping, conversion, or confinement.Part I will open our discussion on targeted material growth, utilizing synthetic chemistry techniques to reconfigure a nanomaterial’s atomic and electronic structure through doping and conversion. The work presented in Chapters 1-5 focuses on the concept of dimensionality and how it relates to material structure, property, and applications. Chapter 3 analyzes synthetic chemistry techniques used to alter the structure of graphene sheets and assemble those sheets into graphene-based macrostructures, such as doped-aerogels, for more meaningful applications. Chapter 4 examines methods to utilize the robust lattice of graphene as scaffolding for the conversion of other porous, macroscopic systems built from other 2-D materials, such as h-BN nanofoams. Chapter 5 explores the conversion method to access different compositions of porous structures beyond that of a layered material, breaking into the porous ceramics field. Part II examines other vdW nanoscale materials from the TMC, such as the transition metal dichalcogenides (TMD) and trichalcogenides (TMT). However, techniques discussed in Part I are not easily applied to the TMD and TMT to tailor their atomic and electronic structure. Therefore, we pursue other means of atomic tailoring, specifically constrained material growth, where the atomic and electronic structure of materials can be reconfigured through drastic physical confinement of the crystals grown. Chapters 8-14 studies how constraining the dimensions of these 2-D and 1-D vdW materials down to “atomic thinness” results in size quantization with profound consequences. Confinement through nanotube encapsulation of the TMD and TMT nanomaterials has resulted in several emergent phenomena ranging from structural distortions (Chapters 8 and 9) to newly stabilized crystalline structures (Chapters 10, 11, and 13) to periodic superstructures (Chapters 12 and 14).

Advisor: Zettl, Alex
Fischer, Felix R
Learning Predictive Models for Efficient Policy Learning

For an intelligent agent to interact with the environment efficiently, it must have the ability to predict, plan and generalize. This thesis studies how an intelligent agent can learn to predict future observations and leverage the predictive models for efficient policy learning and generalization. The four instances in this thesis are on high-fidelity video prediction, video prediction that handles multi-modal data distribution, predictive model-based reinforcement learning, and model-based zero-shot policy generalization. In the first case, we use a model that disentangles motion and appearance to predict high-fidelity images. We find this method can alleviate the blurry artifact and shape deformation inherited in previous methods. In the second case, we propose to use an example-guided model in the face of the multi-modal distribution of real-world data. The proposed method can predict diverse, multi-modal data that can also generalize well. In the third instance, we propose a model-based reinforcement learning method with theoretical guarantees. Specifically, we propose a novel value discrepancy loss for predictive model training. We experimentally also prove such framework and loss will significantly improve sample efficiency. Finally, we propose a method that learns both the dynamics model as well as the value of regions for zero-shot policy generalization. We show that this approach can generalize without finetuning to novel tasks. This thesis proposes several methods toward learning and using better predictive models to achieve policies efficiently.

Advisor: Darrell, Trevor
On the Formation, Propagation and Control of Inhomogeneous Traffic in Grid Networks

This dissertation furthers the understanding of traffic dynamics in urban street networks. It focuses on networks that exhibit traffic inhomogeneities, to understand if parts or all of these networks can be described and controlled through aggregate relations. Using an idealized grid network, the work shows that spatially-inhomogeneous traffic is more commonplace than previously thought. Even when Origin-Destination (OD) demand patterns are spatially homogeneous, higher flows are observed in the center of the network, and lower flows in outlying portions. This spatial pattern persists when the network becomes congested. As congestion propagates, only traffic in the network’s center area remains homogeneous. Street links in the outlying portions exhibit levels of congestion that vary by travel direction, and therefore cannot be described using aggregate relations. Despite the presence of traffic inhomogeneities, it is shown that cordon control improves network performance when used to regulate vehicular accumulation in the network’s center. However, the effectiveness of this control method is greatly determined by features of the O-D table. Multiple fixed cordon sizes are tested. Larger cordons, farther away from the center, are shown to produce greater improvements. The effectiveness of cordon metering is compared against alternative means of congestion control and in more realistic settings to confirm present findings.

Advisor: Cassidy, Michael
Node-Pore Sensing Techniques for Biomarker Discovery

Microfluidic technologies have in conjunction with the field of biomedical research for their utility in making highly precise measurements of biological and chemical specimens. These measurements help us search for specific analytes that can be associated with biological states such as disease in a process called biomarker discovery. The analytes in question are the biomarkers of the states they represent, and these associations become part of the greater body of knowledge that guide other researchers in the recognition and study of other biological phenomena. Eventually, this work helps guide clinicians in making diagnostic and prognostic decisions. In this dissertation, I will describe work done to engineer and implement two microfluidic technologies designed to perform biomarker discovery via different modalities, albeit with both analyzing single-cell phenotypes. The first of these devices, called mechano-node-pore sensing, focused on the mechanical properties of cells, and how the mechanics of the cell as a material can reveal information about drug resistance in acute promyelocytic leukemia. The second of these devices instead analyzed cells for surface proteins using a novel scheme for chemical immobilization of antibody probes. In both cases, I demonstrate how mechanical properties and surface proteins can be used as biomarkers to discriminate between cell phenotypes, and how such measurements can help us more deeply understand a variety of biological phenomena, and eventually make advancements in biomedicine.

Advisor: Sohn, Lydia L
On the combinatorics of cluster structures on positroid varieties

Cluster algebras are a class of commutative rings with a remarkable combinatorial structure, introduced by Fomin and Zelevinsky. A cluster algebra has a distinguished set of generators, called cluster variables, which are grouped together into overlapping subsets called seeds. This dissertation is concerned with the cluster algebra structure of coordinate rings of open positroid varieties in the Grassmannian. Open positroid varieties are projections of open Richardson varieties from the full flag variety to the Grassmannian. They were studied first by Lusztig and Rietsch in the context of total positivity, and then by Knutson--Lam--Speyer, who connected them to the combinatorics of the totally nonnegative Grassmannian developed by Postnikov. Open positroid varieties are smooth, irreducible, and stratify the Grassmannian; open Schubert varieties are a special case./nSeminal work of Scott established that the homogeneous coordinate ring of the Grassmannian is a cluster algebra, and moreover that Postnikov's plabic graphs for the Grassmannian give seeds for this cluster algebra. Postnikov defined plabic graphs not just for the Grassmannian but for all positroid varieties. Accordingly, experts long believed that the coordinate ring of any open positroid variety is also a cluster algebra, with seeds given by plabic graphs. In Chapter 3, which is joint work with Khrystyna Serhiyenko and Lauren Williams, we prove this in the case of open Schubert varieties in the Grassmannian. Work of Leclerc on Richardson varieties in the full flag variety implies that the coordinate rings of these varieties are cluster algebras, but does not give any explicit descriptions of seeds. We show that Postnikov's plabic graphs give seeds in this cluster algebra. For skew Schubert varieties, we show that Leclerc's cluster algebra is given by relabeled plabic graphs, whose boundary vertices are permuted./nShortly following my work with Serhiyenko and Williams, Galashin--Lam showed that Postnikov's graphs give a cluster algebra structure on coordinate rings of arbitrary positroid varieties using similar methods. In Chapter 4, which is joint work with Chris Fraser, we expand on this result to show that positroid varieties admit a number of different cluster structures, with seeds given by relabeled plabic graphs. Along the way, we show that many positroid varieties are isomorphic, using a permuted version of the Muller--Speyer twist map. We conjecture that all of these distinct cluster structures differ only by rescaling, and prove this conjecture for open Schubert varieties. This enlarges the class of combinatorially well-understood seeds for positroid varieties, which provides additional tools to further study the cluster structure on positroid varieties.

Advisor: Williams, Lauren K.
Nonlinear Investigation and Shape Optimization of Wave Energy Converter "Wave Carpet"

Wave energy converter "wave carpet" is an artificial flexible structure originally introduced and actively developed by the Theoretical and Applied Fluid Dynamics Laboratory (TAFLab) for nearly a decade. Wave carpet is submerged on shallow-water seabed and absorbs the energy of incoming surface waves. Several studies conducted by TAFLab members numerically and experimentally proved its concept and supported its technical feasibility./nInheriting from the previous works, this thesis further extends the understanding of wave carpet by numerically investigating one of its possible configurations and heuristically optimizing its planar shape. The study concentrates on arbitrary-shaped wave carpet locally embedded on the seafloor. This type of wave carpet is simplified into a visco-elastic part of the surrounding rigid seabed. In order to prepare the simulation environment, we first solve the problem of wave-viscoelastic seabed interaction using the High-Order Spectral (HOS) method. As a result, the solution presents the propagation of periodic waves over the entire visco-elastic ocean bottom. Wave carpet is subsequently limited to a partial seafloor, and the simulation domain is repetitively wave-fed and marched over time by Runge-Kutta method. Eventually, the procedure results in a robust numerical model of a wave tank with a wave carpet of an arbitrary shape. The model is validated against several criteria to ensure its correctness. It is referred to as the HORSK model that resembles a wave tank./nThe HOSRK wave tank is numerically stable, three-dimensional and possibly highly nonlinear. It serves as a computational environment simulating the real-world scenarios in which incoming waves propagate over a wave carpet and damp out. Through many experiments with the numerical wave tank, we find the optimal water deepness and the optimal restoring force and damping ratio, two main characteristics of the wave carpet, for the maximum energy absorption. We further investigate a wide range of linear and nonlinear elliptical wave carpets, and we find that toward the optimal point the difference between linear and nonlinear carpets in terms of energy absorption is subtle./nTo optimize the wave carpet shape, we propose the Neural Network-based optimization method for linear optimization and the Genetic Algorithm for nonlinear optimization. The results show that in most wave cases the optimal-shaped carpets can absorb approximately twice more energy than the baseline circular shape, and the nonlinear optimal shapes are very similar to the linear optimal ones.

Advisor: Alam, Reza M
Recognizing Cell Identity: Classifying cell types in scRNAseq data

The classification of cell type is one of the first steps in scRNAseq analysis for translating observed transcriptional variation to biological insights. The same cell types can be sampled from different environment and using different technologies and their transcriptional profile can differ. Thus, defining cell types in scRNAseq data is much more than a matter of identifying clusters of cells that are similar to each other. In chapter 1, we developed a simulation method SymSim in order to understand the different facets of variability in scRNAseq. In Chapter 2, we applied a Bayesian Variational Inference method scVI for the harmonization scRNAseq datasets and propose a new method scANVI in the same frame work for the annotation of these datasets. We tested the performance of scVI and scANVI using both SymSim and experimental data. In Chapter 3 we applied our data harmonization method scVI to a Multiple Sclerosis (MS) case-control study using scRNAseq data to profile immune cells. We identified cellular changes associated with MS in tissue-specific cell type abundance and transcriptional changes after being able to identify shared cell types in both blood and CSF in multiple donors. In Chapter 4 we apply a number of scRNAseq harmonization and annotation including scVI and scANVI to a large consortium cell atlas project Tabula Sapiens. Tabula Sapiens aims to provide a comprehensive reference scRNAseq dataset for the scientific community. We developed an automatic annotation pipeline named PopularVote to facilitate the in-house data annotation process, and to be published for using as a public tool for other scientists to annotate their own data. This dissertation presents a set of tools that we developed or used in cell type annotation in a diverse set of scRNAseq applications (identifying rare cell types, comparing cell types across conditions, generating automatic data annotations). The potential of scRNAseq is best realized by generating a well-curated dataset that everyone in the research community can use and contribute to, and the ability to classify cells in an automatic manner will enable such efforts in the future.

Advisor: Yosef, Nir
Development of High Purity Germanium Detectors for High Rate Gamma-Ray Spectroscopy and Imaging

While High Purity Germanium (HPGe) detectors are the gold standard for high-resolution gamma-ray spectroscopy, conventional, coaxial HPGe detectors show significant performance degradation at high incident count rates (tens of thousands of counts per second (cps)). This dissertation describes a new prototype high-purity germanium detector that has good energy resolution and high throughput performance at count rates in excess of 2 Mcps, as well as providing 3D position sensitivity. This device allows for high-resolution spectroscopy and imaging in high count rate environments, which is of interest for many applications in basic and applied nuclear science./nThe detector design, which features a double-sided strip detector (DSSD) geometry with a strip pitch of 0.5 mm, was selected by performing detailed analytical and numerical calculations of the expected efficiency, throughput, timing, energy, and position resolution for various geometries and electrode configurations. The design study and the trade-offs considered in selecting the detector geometry will be presented. Details of the design, predicted performance, and ongoing fabrication will be discussed. Whilst the final fabrication of the device has not yet been completed, simulations indicate that this detector will, at an event rate of 2 Mcps, have an energy resolution of less than 2 keV at 662 keV and throughput greater than 90%.

Advisor: Vetter, Kai
Financial Institutions and the Real Economy

This dissertation examines the role of financial institutions as they relate to foreign currency payments and financial stability. The first chapter of this dissertation examines how the foreign currency component of international payments can be costly for importers and exporters by studying the introduction of a payments system between Brazil and Argentina established in 2008. The second chapter of this dissertation examines the reasons behind short-term funding vulnerabilities of financial institutions by studying Building and Loan Associations in California during the Great Depression. Finally, the last chapter of this dissertation studies the COVID-19 pandemic, which is one of the most important public health and economic events of recent history. This chapter studies the effect of stay-at-home orders, enacted to combat the spread of COVID-19, on local labor markets.

Advisor: Gourinchas, Pierre-Olivier
Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives

In this thesis, we pursue the design of Bayesian models as well as large-scale approximate inference procedures for the analysis of single-cell transcriptomics data./nFirst, we rely on Bayesian statistics to formalize several central algorithmic steps of single-cell transcriptomics data analysis. In particular, we introduce single-cell Variational Inference (scVI), a deep generative model that separates technical noise from biological signal, and is readily applicable de novo cell type identification, batch effect correction, and differential expression. As follow-up work, we introduce single-cell ANnotation using Variational Inference (scANVI), an extension of scVI for sharing annotation of cells across experimental conditions. scANVI is a fully-probabilistic method for harmonizing collections of single-cell RNA sequencing datasets, and therefore provides a principled way of quantifying the uncertainty of cell state annotation. The inference procedure for both methods is based on auto-encoding variational Bayes and scales to millions of cells./nIn a second line of work, we expose two pathological behaviors of variational inference we encountered while developing scVI. First, the presence of an encoder network leads to entanglement of the latent variables, an undesirable behavior for factoring out unwanted variations from embeddings. To address this problem, we introduce the Hilbert-Schmidt Information Criterion (HSIC) constrained Variational Autoencoder (VAE), a generic variant of the VAE that incorporates a non-parametric measure of dependence as an explicit constraint for the variational distribution. Second, we expose the unsuitability of the variational distribution for Bayesian decision-making and introduce a simple procedure for making more accurate decisions with VAEs. In particular, we explore alternative training procedures, based on using separate variational bounds for learning the generative model and variational posterior.

Advisor: Jordan, Michael I
Yosef, Nir