VictorJuiz/thesis huggingface dataset:
Hierarchical clustering with global objectives : approximation algorithms and hardness results

Hierarchical Clustering is an important tool for data analysis, used in diverse areas ranging from Phylogenetics in Biology to YouTube video recommendations and everything in between. The term ``hierarchical'' is used to contrast with ``flat'' clustering approaches, like k-means or k-center, which only produce one specific partitioning of a data set. Hierarchical Clustering is a way of understanding the overall structure of large data sets by visualizing them as a collection of successively smaller and smaller clusters, capturing different levels of granularity simultaneously. The end result is a tree, whose internal nodes represent nested clusters and with leaves representing the data points. Despite the plethora of applications and heuristics, the theory behind Hierarchical Clustering was underdeveloped, since no ``global'' objective function was associated with the final output. A well-formulated objective allows us to compare different algorithms, measure their quality, explain their success or failure and enables continuous optimization methods with gradient descent. This lack of objectives is in stark contrast with the flat clustering literature, where objectives like k-means, k-median or k-center have been studied intensively starting from the 1950s, leading to a comprehensive theory on clustering. In this thesis, we study approximation algorithms and their limitations, making progress towards building such a theory for objective-based Hierarchical Clustering (HC). For the minimization cost function with pairwise similarities introduced recently by Dasgupta (2016) and its maximization variants, we show how standard tools like Sparsest Cut, Balanced Cut, Max Cut or Max Uncut Bisection can be used in a black box manner, to produce provably good HC and we also complement our algorithms with inapproximability results. Escaping from worst-case analyses, we also study scenarios where extra structure is imposed on the data points (e.g., feature vectors) or qualitative information is provided (e.g., the common triplet or quartet constraints in Phylogenetics). Our results are inspired by geometric ideas (semidefinite programming relaxations, spreading metrics, random projections) and novel connections with \textit{ordering} Constraint Satisfaction Problems. Overall, this thesis provides state-of-the-art approximation and hardness results for optimizing global HC objectives, reveals unexpected connections with common linkage or graph cut heuristics and advances our understanding of old Phylogenetics problems. As such, we hope it serves the community as the foundation for objective-based Hierarchical Clustering and for future improvements

Advisor: Charikar Moses 
Roughgarden Tim 
Tan Li-Yang 
Valiant Gregory
High dimensional analysis of the human dendritic cell network : discovery of a novel transitional dendritic cell population related to plasmacytoid dendritic cells

As the professional antigen-presenting cells of the immune system, dendritic cells (DCs) are unique in their capacity to modulate a broad range of immune responses. Their ability to enact such diverse outcomes is mediated by a division of labor among functionally specialized subsets, including plasmacytoid DCs (pDCs) and two subsets of classical DCs, cDC1 and cDC2. Plasmacytoid DCs have been attributed a role in both type-I interferon production and antigen presentation. However, it remains poorly understood how pDCs enact these two functions. Here, we applied cutting edge high-dimensional approaches to analyze the human DC network with the goal of understanding the functional diversity within pDCs. First, we used mass cytometry (CyTOF) and identified a novel population of DCs with a continuum of phenotypes between pDCs and cDCs, which we named transitional DCs (tDCs). Next, we identified the murine equivalent of human tDCs and found evidence of their developmental connection with pDCs. Finally, we used the assay for transposase accessible chromatin using sequencing (ATAC-seq) to profile the chromatin accessibility landscape of primary human DCs, including tDCs and stimulated pDCs. We conclude that tDCs are an evolutionarily conserved DC population that is closely related to pDCs. Independently of the tDC population, we found that pDC functional heterogeneity arises from their capacity for cellular plasticity. Altogether, these findings reveal new insights into DC biology that are critical for the rational design of therapeutics that harness these specialized cells

Advisor: Idoyaga Juliana
Engleman Edgar G.
Habtezion Aida
Schneider David (David Samuel)
High quality factor phase gradient metasurfaces for nonlinear and reconfigurable nanophotonics

Emerging optical technologies spanning LIDAR to virtual reality will revolutionize the way we interact with the world around us. Key to these technologies will be the ability to generate tunable, reconfigurable, and compact optics that can replace conventional components like glass lenses and mechanically actuated devices. Metasurfaces, two dimensional arrays of subwavelength optical nanoantennas, promise to dramatically scale down device footprints with nanoscale control of optical wavefront's phase, amplitude, and polarization. However, the geometric design methodologies, weak light-matter interactions of most materials, and nanoscale design mean that metasurfaces are challenging to reconfigure. Amplifying their light-matter interaction and nonlinear responses are thus paramount to designing efficiently reconfigurable (re)active nanophotonic platforms. This thesis demonstrates a novel route to reconfigurable and nonlinear metasurfaces utilizing high quality factor (high-Q) resonances. Using Silicon nanobars as our nanoantenna elements, we demonstrate efficient metasurfaces at telecom frequencies. First, we show that guided mode resonances within individual nanoantennas can be exploited to generate high quality factors in arbitrary phase gradients. Next, we fabricate these structures in a silicon on sapphire platform and reveal record Q factors of 2,500 in beamsteering metasurfaces. Next, we discuss routes to apply these high-Q resonances for electro-optic modulation and nonlinear optics. we show that interfacing our high-Q metasurfaces with Lithium Niobate (LNO) enables efficient modulation of diffraction with an applied DC electric field, enabling compact, solid state wavefront manipulation on a chip. In addition, using the resonant field enhancement afforded by these resonant antennas, we are able to reduce the power required for the nonlinear Kerr effect. Using this, we numerically demonstrate efficient nonlinear and nonreciprocal modulation of diffraction with low illumination power. Finally, we consider more exotic optical potentials in metamaterials. Using plasmonic waveguide-based metamaterials with added loss and gain, we construct a Parity-Time symmetric metamaterial whose bandstructure can be dynamically tuned with the amount of loss and gain. By considering nonlinearities that would naturally occur in these materials, we demonstrate broadband and wide-angle nonreciprocity, a potentially enabling technology in tunable nonlinear free-space isolators. Our results show that resonant and nonlinear nanophotonics can be exploited for efficient wavefront generation and modulation, amenable to ultrathin, reconfigurable optical devices

Keywords: Materials Science and Engineering.
Advisor: Dionne Jennifer Anne
Brongersma Mark L.
Fan Jonathan Albert
High-Resolution Soybean Yield Mapping Across the US Midwest Using Sub-field Harvester Data

Cloud computing and freely available, high-resolution satellite data has enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remains uncommon or infeasible in much of the world. Here, we use a large-scale ground-truth dataset across the United States Midwest to assess machine learning models’ capacity for soybean yield prediction. First, we compare random forest (RF) implementations across 400,000 fields, testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-meter scale yield prediction. We find that Sentinel-2-based models can explain up to 45% of out-of-sample yield variability across 2017-18, while Landsat models explain up to 43% across the longer 2008-18 period. Using discrete Fourier transforms, or harmonic regressions, proved helpful for capturing soybean phenology, improving a Landsat-based model considerably. Second, we compare RF models trained using our fine-scale harvester data to models trained on freely available county-level data. We find that county-level models rely more heavily on just a few predictors, namely August weather covariates (VPD, rainfall, temperature) and July and August NIR observations. As a result, county-scale models perform relatively poorly on field-scale validation, especially for high-yielding fields, but perform similarly to field-scale models when evaluated at the county scale. Finally, we test whether our findings on variable importance can inform improvements to a Scalable Crop Yield Mapper (SCYM) approach that uses crop simulations to train models for yield estimation. Based on findings from our RF models, we employ harmonic regressions to estimate peak VI and a VI observation 30 days later, with August rainfall as a sole weather covariate in our new SCYM model. These changes proved effective for improving SCYM’s explained variance and creating a simple, generalizable framework for regions or time periods beyond which ground data are available.

Keywords: Deines, Jillian M
Lobell, David B
School of Earth Energy &amp; Environmental Sciences
Crop Yield Mapping
Remote Sensing
Machine Learning
Thesis
High-throughput biophysical assays and models to link molecular sequence variation to structural and functional consequences

Nucleic acids and proteins, the fundamental components of biological processes, are built as polymers from relatively small sets of molecular building blocks. Once synthesized, these polymeric chains fold into the 3D conformations necessary to perform their functions. The scientific community has begun to quantify this relationship between the sequence of these macromolecules, their resulting structures, and their effects as manifested through biochemical and biophysical interactions. However, the number of possible sequences grows exponentially with increasing length, rendering the task of fully characterizing their structural and functional diversity impossible. Instead, models of protein function must be built from existing data to predict effects of mutations. Such models have been widely adopted in fields like protein engineering, precision medicine, and genetically-modified organism development. Unfortunately, these models require a lengthy development cycle of data collection, analysis, and testing. Here, I present three new technologies to reduce the cycle time of model development for biophysical processes. The first, described in Chapters 2 and 3, is a novel assay, named BET-seq, for the rapid in vitro characterization of transcription factor-DNA interactions. We use this assay to quantify the binding site context specificities of two yeast transcription factors, Pho4 and Cbf1, to a greater resolution than previously possible. We find that the BET-seq data are sufficient to determine not only raw sequence preferences, but also higher-order epistatic interactions within the transcription factor-DNA complex, allowing for better models of these interactions. Chapter 3 provides a detailed protocol for running the BET-seq assay along with guidelines for assessing data quality and modeling the underlying biophysical process. Chapter 4 introduces an algorithm, named DeCoDe, to design protein-coding DNA libraries to rapidly test pools of hypothetically active protein constructs. DeCoDe uses integer linear programming to select optimal degenerate codons in pooled protein-coding DNA libraries. We show that DeCoDe significantly outperforms existing library design tools and, when used appropriately, can generate cost-effective libraries capable of screening functional protein sequence space. Finally, Chapter 5 presents preliminary efforts to model protein folding as a link-prediction problem using graph neural networks. The goal of this work is to reduce the time necessary to model the structural implications of changes to protein sequences. Taken together, these chapters provide a powerful new set of tools to rapidly design, build, and test systems of biophysical interactions

Keywords: Genetics.
Advisor: Fordyce Polly 
Altman Russ 
Kundaje Anshul
Sherlock Gavin
Hiring and Recruiting Continuation School Teachers: Initial Findings from an Examination of Three Urban California School Districts

Teachers in California’s continuation high schools work with student populations that are markedly different from those in comprehensive schools in the same district. Yet, little research exists on how principals and administrators recruit and hire teachers to work in these settings. This report presents initial findings from qualitative interviews with six continuation school principals and administrators on the topic of teacher recruitment and hiring in continuation schools. Principals and administrators use a continuation school’s reputation, contractual language, and targeted credentialing as strategies for recruiting teachers. Among other qualities, they prioritize teachers’ personality characteristics and their ability to implement engaging instruction when interviewing candidates. Finally, principals and administrators use interviews, openness about the nature of the teaching role, and school perception as mechanisms to hire teachers who will be able to be successful. While not necessarily generalizable, these findings offer insight into opportunities for further research.

Keywords: POLS
continuation high school
recruitment
hiring
teacher
alternative
Thesis
History and context-dependent modulation of rodent sensory processing

This thesis consists of three separate chapters, representing my journey from engineer to neuroscientist, and my PhD thesis. Two are completed papers, and each project is self-contained with relevant introductory material and context in the respective introductions, while the general introduction provides a brief orientation to my work in the general context of systems neuroscience. These projects span sensory systems neuroscience from spinal cord neurobiology to cortex and computation. In the first chapter I present a surgical technique to implant a cannula into the vertebral column of mice, allowing optogenetic access to spinal sensory neurons in the dorsal horn. In the second chapter, I analyze visual cortex calcium imaging data from the Allen Institute Brain observatory during free locomotion behavior. This analysis reveals that lower background noise during locomotion might be responsible for the increased encoding accuracy during locomotion (as opposed to increased firing rates). In the last chapter, I introduce a rodent decision-making task intended to allow parametric control both of the animals' expectation of the sensory stimuli as well as the noise in the stimuli

Keywords: Electrical Engineering.
Advisor: Schnitzer Mark Jacob
Shenoy Krishna V. (Krishna Vaughn) 
Solgaard Olav
Holographic X-ray detection

The ability to non-destructively image the interior of an object with X-rays has enabled huge advances in science and medicine. X-ray differential phase contrast (DPC) is the next generation in imaging, providing higher contrast for soft materials. However, X-ray DPC suffers from poor X-ray detector resolution. Typical commercial detectors have a fundamental physical tradeoff between image resolution and radiation dose. In this dissertation, I present a new method for X-ray detection that overcomes this fundamental limit. This new method uses photo refractive materials from the field of holography to create a high resolution detector which can, in conjunction with X-ray DPC techniques, measure the absorption and refraction of X-rays as they pass through objects. This new detector operates in three main stages: first, interfering X-rays record a hologram into a photorefractive crystal. Then, this crystal is illuminated with visible light to 'read-out' and digitally record the image. Finally, the crystal is erased with ultraviolet light after the image is collected. The result is an X-ray detector with an order of magnitude improvement in resolution (to 208 lp/mm), an order of magnitude improvement in quantum efficiency (to 80%), and a drastic reduction in production cost

Keywords: Electrical Engineering.
Advisor: Hesselink Lambertus 
Pease R. (R. Fabian W.) 
Pianetta Piero
Home Stimulation and Gender as Moderators of the Association Between Siblings and Executive Functions in Pakistani Preschoolers

The current study investigates the association between the number of older siblings and child executive function (EF) skills in a sample of 1302 four-year-olds living in rural Pakistan. We examined the quality of home stimulation and child gender as potential moderators of this association. Using ordinary least square regression, we assessed a three-way interaction amongst the number of older siblings, home stimulation, and child gender. We found a positive association between the number of older siblings and EF skills for boys in both high and low home stimulation, and for girls in low home stimulation. However, this association was negative for girls from families characterized by higher levels of home stimulation. We discuss the findings in the context of family resource distribution and offer policy implications for early childhood interventions in low-middle income country (LMIC) contexts.

Keywords: executive functions
siblings
home stimulation
gender
low-middle income country
Pakistan
Stanford Graduate School of Education International Education Policy Analysis
Thesis
Homological mirror symmetry for elliptic Hopf surfaces

We define the notion of a non-algebraic Landau-Ginzburg model and its associated Fukaya category. We show that non-Kahler surfaces obtained by performing two logarithmic transformations to the product of the projective line and an elliptic curve have non-algebraic Landau-Ginzburg models as their mirror spaces in the sense of homological mirror symmetry. This class of surface includes the classical Hopf surface and other elliptic primary and secondary Hopf surfaces. We also define localization maps from the Fukaya categories associated to the Landau-Ginzburg models to partially wrapped and fully wrapped categories, and we show mirror symmetry results that relate the partially wrapped and fully wrapped categories to spaces of coherent analytic sheaves on open submanifolds of the compact complex surfaces in question. We use these results to sketch a proof of a full homological mirror symmetry result for these compact surfaces

Keywords: Mathematics.
Advisor: Auroux Denis 
Vakil Ravi
Eliashberg Y.
Host metabolism controls malaria outcome

Malaria, caused by infection with members of the genus Plasmodium, causes the death of over 400,000 people, mostly children under age 5, every year. With the increasing prevalence of resistance to antimalarial drugs, new therapies that treat malaria are desperately needed. In order to survive infection, an infected organism must both restrict pathogen load and limit tissue damage. Recent studies have shown that an organism's ability to control tissue damage requires adaptation to the metabolic needs of infection. While the ways that hosts restrict pathogen load during malaria are relatively well-studied, the causes of tissue damage during infection are less clear. This work examines how metabolism affects tissue damage and malaria outcome. Chapter 1 of this dissertation discusses the types and causes of tissue damage during malaria. Chapter 2 uses a multi-omic approach to describe host metabolism, as well as immune responses and pathology, during a mouse model of malaria. As a proof of principle, I demonstrate the importance of a metabolite signaling pathway in limiting tissue damage. In Chapter 3, I describe the interaction between metabolism, fever, and infection outcome and detail ongoing work to develop mouse models that will allow experimental dissection of fever during malaria. Lastly, Chapter 4 reflects on current malaria therapeutics and discusses objectives for the next generation of drugs. Taken together, the work described here highlights the importance of metabolic adaptation to malaria in limiting tissue damage and advocates for development of models and experimental approaches that elucidate the causes of tissue damage during infection

Keywords: Microbiology and Immunology.
Advisor: Schneider David (David Samuel)
Dodd Dylan
Monack Denise M. 
Mudgett Mary Beth
Housing and uncertainty in macroeconomics

In my dissertation, I study housing, uncertainty and their interaction in macroeconomics. In Chapter 2, my coauthor and I show that the complementarity between housing and marriage can have important implications on homeownership and marriage in the life cycle in the context of China. In Chapter 3, my coauthors and I show that policy uncertainty can induce substantial negatively impact on business investment, employment and productivity in the context of Brexit. In Chapter 4, my coauthors and I show that the introduction of a futures market can help developers and households hedge against future house price risks, expand construction, reduce house prices and the pass-through from risks into house prices, and improve households' welfare in the equilibrium

Keywords: Economics.
Advisor: Schneider Martin (Professor of economics)
Bloom Nick
Piazzesi Monika
How Crime Narratives Shape Subjective Assessments of Guilt

Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated newspaper crime stories. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments.
In addition, we build BERT-based predictive models based on SuspectGuilt and show that these models benefit from in-domain pretraining with unlabeled data. Besides, we experiment with joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the effects of crime-reporting on a large scale.

Keywords: Kreiss, Elisa
Symbolic Systems Program
Stanford University
Natural Language Processing
SuspectGuilt
Thesis
Advisor: Potts Christopher
Jurafsky Dan
How does context matter? : segregation, inequality, and disparities in K-12 education

Though optimistically touted as "the great equalizer, " the U.S. education system has historically been plagued by inequality along the axes of race, ethnicity, and socioeconomic status. Guided by theoretical traditions from sociology and the applied perspective of education policy, this dissertation builds on prior work by asking: How does racial, ethnic, and socioeconomic context matter for educational inequality? With a particular focus on racial/ethnic segregation and socioeconomic inequality, this work explores inter-group disparities in educational inputs and experiences that evidence suggest matter for student wellbeing, including school discipline and finance. Together, these studies provide descriptive evidence of the social processes and contexts that structure disparities in educational experiences. This kind of descriptive work, informed by theory as well as the applied concerns of education researchers, represents a key addition to scholarship focused on how to define, understand, and remedy inequality in K-12 education

Keywords: Education.
Advisor: Reardon Sean F. 
Hwang Jackelyn 
Pearman Francis A.